Evaluation of Membrane Characterization · PDF fileEvaluation of Membrane Characterization...

Subject Area: Water Quality

Web Report #4102

Evaluation of Membrane Characterization Methods

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Jointly sponsored by:Water Research Foundation6666 West Quincy Avenue, Denver, CO 80235and

U.S. Environmental Protection AgencyWashington DC 20460

Published by:

Prepared by:Amy E. ChildressDepartment of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557

Jonathan A. BrantDepartment of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071

Pawel RempalaDepartment of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557

Donald W. Phipps Jr.Orange County Water District/Research and Development Department, 10500 Ellis Avenue, Fountain Valley, CA 92708-8300and

Pierre KwanHDR Engineering, Inc., 500 108th Avenue NE, Suite 1200, Bellevue, WA 98004-5549



DISCLAIMER

This study was funded by the Water Research Foundation (Foundation) and the U.S. Environmental Protection Agency (USEPA) under Cooperative Agreement No. X-83294801-1. The Foundation and USEPA assume no responsibility for the content of the research study reported in this

publication or for the opinions or statements of fact expressed in the report. The mention of trade names for commercial products does not represent or imply the approval or endorsement

of the Foundation or USEPA. This report is presented solely for informational purposes.

Copyright © 2012by Water Research Foundation

ALL RIGHTS RESERVED.No part of this publication may be copied, reproduced

or otherwise utilized without permission.


v

CONTENTS

TABLES ....................................................................................................................................... vii LIST OF FIGURES ....................................................................................................................... ix FOREWORD ................................................................................................................................ xv ACKNOWLEDGMENTS .......................................................................................................... xvii EXECUTIVE SUMMARY ......................................................................................................... xix CHAPTER 1: INTRODUCTION AND BACKGROUND ............................................................ 1

Contact Angle Measurements ............................................................................................. 1 Zeta Potential Measurements .............................................................................................. 6 Surface Roughness – Atomic Force Microscopy (AFM) ................................................... 8 Transmission Electron Microscopy (TEM) ........................................................................ 9 Scanning Electron Microscopy (SEM) ............................................................................. 10 Chemical Force Microscopy (CFM) ................................................................................. 12 X-Ray Photoelectron Spectroscopy (XPS) ....................................................................... 14 Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) ..... 15 Objectives ......................................................................................................................... 16

CHAPTER 2: MATERIALS AND METHODS .......................................................................... 18 Membrane Samples ........................................................................................................... 18 Statistical Analysis ............................................................................................................ 19 Membrane Community Survey On Membrane Characterization Techniques .................. 19 Development Of Standard Techniques For Characterizing Membrane Surfaces ............. 19

AFM Surface Roughness Measurements .................................................................... 20 Contact Angle Measurements ..................................................................................... 21 Streaming Potential Measurements ............................................................................. 22

Assessment of Membrane Performance and Fouling ....................................................... 22 Temperature Correction Factors for Normalizing Membrane Permeate Flux ............ 25 Membrane Performance Testing ................................................................................. 28 Overview of Membrane Performance Test Method ................................................... 29 Normalizing Membrane Performance Response ........................................................ 29

CHAPTER 3: RESULTS AND DISCUSSION ............................................................................ 31 Method Development For AFM Surface Roughness ........................................................ 31

Imaging and Roughness Analysis of Flat Membrane Surfaces .................................. 31 Imaging and Roughness Analysis of Curved (Hollow Fiber) Membrane Surfaces .... 36 Standard Method for AFM Surface Roughness Measurements on Flat-Sheet and Hollow Fiber Membranes ........................................................................................... 38

Method Development For Contact Angle Measurements ................................................ 38 Contact Angle Analysis Of The Flat-Sheet Teflon Standard Surface ........................ 38 Contact Angle Analysis Of Flat Sheet Membrane Surfaces ....................................... 41 Statistical Analysis Of Contact Angle Results For Flat Surfaces ............................... 44 Contact Angle Analysis Of Curved Surfaces .............................................................. 47 Standard Method For Contact Angle Measurements On Flat-Sheet And Hollow Fiber Membranes .................................................................................................................. 48


vi

Method Development For Streaming Potential (Zeta Potential) Measurements .............. 48 PMMA Standard Surface ............................................................................................ 48 Streaming Potential (Zeta Potential) Analysis Of Flat Sheet Membrane Surfaces .... 49 Streaming Potential (Zeta Potential) Analysis Of Hollow Fiber Membrane Surfaces 52 Standard Method For Streaming Potential Measurements On Flat-Sheet And Hollow Fiber Membranes ........................................................................................................ 53

Method Feedback From Partner Utilities .......................................................................... 53 Guidance For Integrity Test Calculations ......................................................................... 61 Evaluating Correlations Between Membrane Characteristics And Performance ............. 65

Multiple Linear Regression (MLR) Methods ............................................................. 70 Assessing the Predictive Ability of MLR Model Inputs by Iteratively Withholding a Test Exemplar ............................................................................................................. 73 Assessing the Influence of MLR Model Input Parameters ......................................... 75 Modeling Conclusions ................................................................................................ 87

CHAPTER 4: CONCLUSIONS ................................................................................................... 89 Development Of Standard Techniques For Characterizing Membrane Surfaces ............. 89

Correlation Between Membrane Properties And Membrane Fouling ........................ 90 APPENDIX A LIST OF UTILITIES USING MEMBRANES AS A TREATMENT

TECHNOLOGY (AS OF 2010) ........................................................................................92 APPENDIX B SURVEY / QUESTIONNAIRE ON MEMBRANE CHARACTERIZATION

TECHNIQUES AND PROCEDURES ............................................................................117 APPENDIX C INSTRUCTIONS TO PARTICIPATING LABORATORIES FOR

CONDUCTING AFM, CONTACT ANGLE, AND STREAMING POTENTIAL MEASUREMENTS .........................................................................................................126

APPENDIX D STANDARD PROTOCOLS FOR CHARACTERIZING MEMBRANE SURFACES: CONTACT ANGLE, ZETA POTENTIAL, SURFACE ROUGHNESS .133

REFERENCES ........................................................................................................................... 152 ABBREVIATIONS .................................................................................................................... 154


vii

TABLES

Table 1.1. Distribution of adhesion across the HL and SG membrane surfaces as a function of

surface area coverage measured with methyl, carboxyl and hydroxyl functionalized probes (I =

0.01 M NaCl; pH 6; T = 20 °C). Taken from Brant et al. (2006). ................................................ 12

Table 1.2 Measured atomic concentration percentages (%) of C1s, O1s, N1s and S2p obtained by X-

ray photoelectron spectroscopy (XPS) for commercially available and experimental membranes.

Taken from Boussu et al. (2007). ................................................................................................. 15

Table 2.1 Names and select properties of membranes used in this investigation (MF-

microfiltration; UF- ultrafiltration; NF- nanofiltration; RO- reverse osmosis, PP- polypropylene,

PES- polyethersulfone, PA- polyamide, TFC – thin film composite). ......................................... 18

Table 2.2 Proposed standard surfaces for atomic force microscopy surface roughness

measurements. ............................................................................................................................... 21

Table 2.3 Proposed standard surfaces for contact angle measurements. ...................................... 21

Table 2.4 Summary of primary system components and accessories for the standard membrane

test unit. ......................................................................................................................................... 24

Table 2.5 Chemistry and composition of the feed solution used for membrane fouling

experiments………………………………………………………………………………………25

Table 2.6 Summarized temperature correction factors (TCFs) for the NF-270, ESNA1-LF,

ESPA-2, SW30HR, and MUNIRO-400 membranes, which were used in the membrane fouling

experiments. .................................................................................................................................. 27

Table 3.1 Surface roughness statistics generated from atomic force microscope (AFM) imaging

of the ESNA1-LF membrane surface by the OCWD and DU laboratories (resolution = 256 ×

256, measurement mode = tapping, medium = water). Numerals (1, 2, ..) correspond to a sample

site on the membrane. ................................................................................................................... 32

Table 3.2 Surface roughness statistics generated from atomic force microscope (AFM)

measurements of the SW30HR membrane surface from the OCWD and DU laboratories

(resolution = 256 × 256, measurement mode = tapping, medium = water). Numerals (1, 2, ..)

correspond to a sample site on the membrane. ............................................................................. 33

Table 3.3 Precision statistics for the surface roughness data that was collected for the ESNA1-LF

and SW30HR membranes (cell – test results from one laboratory on one material, repeatability

standard deviation (sr), reproducibility standard deviation (sR). ................................................... 34

Table 3.4 Surface roughness statistics as measured using an atomic force microscope (AFM) for

the NF-270, ESNA1-LF, ESPA-2, and SW30HR membranes. All measurements were performed

on flat sheet membrane samples by the Orange County Water District (OCWD) laboratory. All

measurements were performed on wet membrane samples in contact mode and using a liquid

cell. ................................................................................................................................................ 34

Table 3.5 Surface roughness statistics for Teflon tubing, which was being considered as a

standard surface for analyzing hollow fiber membranes using atomic force microscopy

(resolution = 256 × 256, medium = water, OCWD – contact mode, DU – tapping mode). ......... 39


viii

Table 3.6 Contact angle data for the Teflon standard surface from the three participating

laboratories (Colorado School of Mines - CSM, Duke University – DU, and the University of

Nevada, Reno – UNR). All contact angles were measured using the captive bubble method and

doubly deionized water as the contact angle probe liquid (T = 20°C). ........................................ 39

Table 3.7 Contact angle data for the ESNA1-LF membrane from the three participating




Table 3.8 Contact angle data for the SW30HR membrane from the three participating




Table 3.9 Average contact angle values for the PTFE standard surface, ESNA1-LF, and

SW30HR membrane. The associated statistical measures (repeatability and reproducibility) from

the inter-laboratory study are also reported for each test surface. ................................................ 45

Table 3.10 Reported and measured contact angle values for the 2D reference tool. All results

were acquired by the laboratory at the University of Nevada, Reno (UNR). ............................... 47

Table 3.11 Contact angle results for hollow fiber membranes. All results were obtained using the

experimental apparatus that was developed as part of this project. .............................................. 48

Table 3.12 Specific comments on the usefulness and technique for carrying out contact angle

measurements in membrane applications. .................................................................................... 56

Table 3.13 Specific comments on the usefulness and technique for carrying out streaming

potential measurements to calculate the zeta potential of membranes in water and wastewater

treatment applications. .................................................................................................................. 58

Table 3.14 Specific comments on the usefulness and technique for carrying out surface

roughness measurements using an atomic force microscope for membranes in water and

wastewater treatment applications. ............................................................................................... 61

Table 3.15 Multiple linear regression (MLR) model input parameters and values for describing

bentonite clay fouling. .................................................................................................................. 71

Table 3.16 Constants, coefficients and statistics for the best MLR model: J/Jo (at 5g m-2

Load) =

0.67233+ (2.29×10-3 × Jinitial, L m-2hr-1) – (2.12×10-3 × Rq, nm) + (3.00×10-3 ×θ, degrees).

....................................................................................................................................................... 72

Table 3.17 Values of K and J/Jo, Plateau obtained by fitting normalized permeate flux (J/Jo) data

and clay loading (g m-2

) data for each membrane to equation Eq. 3.3. R-squared values and 95%

asymptotic confidence intervals are reported for each nonlinear regression membrane model…80

Table 3.18 Membrane properties selected as independent variables for MLR analyses of K and

J/Jo, Plateau . The zeta potential values were determined at pH=5.3, 2mM KCl saturated with

ambient CO2. Surface roughness statistics were collected by the OCWD, while zeta potential and

water contact angle values were collected by UNR. Note: Rq was slightly cross-correlated with

Jo (r=0.5206) and with θ (r=0.5084)……………………………………..………………………81


ix

LIST OF FIGURES

Figure 1.1 (a) Representative digital image of a liquid droplet on a dry surface in which the

contact angle (θ) is measured according to the sessile drop method. (b) Representative digital

image of an air bubble on a wetted membrane surface (the membrane is immersed in a liquid)

and the contact angle (θ) is measured according to the captive bubble technique. ........................ 2

Figure 1.2 Techniques commonly used for carrying out contact angle measurements: (a) static

sessile drop – advancing and receding; (b) static captive bubble – advancing and receding; and

(c) dynamic captive bubble. Figure adapted from Drelich (1997). For those illustrations for the

sessile drop technique the term advancing refers to the advancement of the liquid (increase in the

volume of the liquid droplet) over a dry surface, while the term receding refers to the recession

of the liquid droplet (decrease the in the volume of the liquid droplet) over a previously wet

surface. A similar relationship exists for the captive bubble measurements, where the water in

which the surface is immersed either advances or recedes across the surface through a decrease

or increase in the volume of the air bubble. .................................................................................... 3

Figure 1.3 The effect of drop (bubble) size on advancing (Adv.) and receding (Rec.) contact

angles for the air /water/polyethylene film system as obtained with the static sessile-drop (SD),

static captive-bubble (CB), and dynamic captive-bubble (DCB) techniques. Taken from Drelich

et al. (1996). .................................................................................................................................... 4

Figure 1.4 Illustration of the relative locations of the Stern layer and Shear plane from a charged

surface in water and the associated change in surface potential with distance. Taken from

Chapman-Wilbert et al. (1999). ...................................................................................................... 7

Figure 1.5 Schematic illustration of the operating principles associated with the atomic force

microscope (AFM). Taken from Wyart et al. (2008). .................................................................... 8

Figure 1.6 Transmission electron microscope (TEM) image of a graft copolymer film. Taken

from Kim et al. (2008). ................................................................................................................... 9

Figure 1.7 Ordered and disordered co-polymer blend morphologies visualized using TEMT

method. Taken from Jinnai et al. (2006)....................................................................................... 10

Figure 1.8 Scanning electron microscope (SEM) images of cross-sections of polymer blend

membranes. Taken form Li et al. (2008). ..................................................................................... 11

Figure 1.9 Topographical and corresponding CFM images for the HL membrane acquired with a

CH3-, COOH-, and OH-functionalized tip (I = 0.01 M NaCl; pH = 6.1, and T = 20 °C). Taken

from Brant et al. (2006). ............................................................................................................... 13

Figure 1.10 Changes in adhesion force of carboxyl-modified microspheres (a) and hydroxyl-

modified microspheres (b) to PE membranes that were sampled during the pilot-filtration test.

An SEM image of a functionalized polystyrene bead glued to a cantilever tip used in this study is

shown on the right. Taken from Yamammura et al. (2008). ........................................................ 14

Figure 1.11 ATR-FTIR spectra of several commercial and one lab-made membrane, containing

sulfone groups. Ar-SO2-Ar typical frequencies are ν(S=O asym) = 1325 cm-1

, ν (S=O symm) =

1140 cm-1

. Differences in IR spectra stem from C(CH3)2 bridges present between aryl rings in

NTR7450 and Desal 5DL. Taken from Boussu et al. (2007). ...................................................... 16


x

Figure 2.1. Process flow diagram of the bench-scale membrane test system used in the

correlation experiments for membrane surface properties and perfromance (P – pressure gauge, T

– temperature probe, F – flow meter). .......................................................................................... 23

Figure 2.2. Normalized permeate flow (25°C, flow at t = 0 set to 1) for membranes tested as a

function of time (hours). ............................................................................................................... 29

Figure 3.1. Rendered 3D images of the ESNA1-LF membrane based on atomic force microscope

(AFM) data from (a) OCWD and (b) DU (scan size = 100 μm2, measurement mode = tapping,

medium = water). .......................................................................................................................... 32

Figure 3.2. Rendered 3D images of the SW30HR membrane based on atomic force microscopy

data from (a) OCWD and (b) DU (scan size = 100 μm2, measurement mode = tapping, medium =

air). Note that the z-scale is different between the rendered 3D images (0.35 μm/division for

Figure 3.2a compared to 0.305 μm/division for Figure 3.2b). ...................................................... 33

Figure 3.3. Rendered 2D atomic force microscope (AFM) images of the ESNA1-LF, SW30HR,

NF-270, ESPA-2, and MUNIRO-400 membranes. All images were generated by the AFM

OCWD laboratory. ........................................................................................................................ 35

Figure 3.4. Plot of Ra as a function of Rq for the membranes investigated. .................................. 36

Figure 3.5. Box and whiskers plot for Teflon contact angle data: central bar – mean, outside bars

–minimum/maximum, boxes – standard deviation range or confidence interval. The 95%

confidence interval for 6 measurements is very close to the sample standard deviation span, so

99% confidence intervals were plotted on the right. CSM – Colorado School of Mines, DU –

Duke University, UNR – University of Nevada, Reno. All contact angles were measured using

the captive bubble method and doubly deionized water as the contact angle probe liquid (T =

20°C). ............................................................................................................................................ 39

Figure 3.6. Digital images of air bubbles on the Teflon standard surface from each of the three

participating laboratories (Colorado School of Mines – CSM, Duke University – DU, and the

University of Nevada, Reno – UNR). In each case the contact angle was measured using the

captive bubble technique in which the Teflon surface was immersed in doubly deionized water

(T = 20°C). .................................................................................................................................... 40

Figure 3.7. Digital image of a water droplet (Vdrop = 20 μL) on a Teflon standard surface set at an

incline. ........................................................................................................................................... 41

Figure 3.8. Box and whiskers plot for the ESNA1-LF contact angle data, which was measured

using the captive bubble technique. The 95% confidence interval for 6 measurements is very

close to the sample standard deviation span, so 99% confidence intervals were plotted on the

right. CSM – Colorado School of Mines, DU – Duke University, UNR – University of Nevada,

Reno. ............................................................................................................................................. 42

Figure 3.9. Representative digital pictures of air bubbles from which contact angles were

measured on the ESNA1-LF membrane at each of the three participating laboratories (Colorado

School of Mines - CSM, Duke University – DU, and the University of Nevada, Reno – UNR). 43

Figure 3.10. Box and whiskers plot for the SW30HR contact angle data that was generated by the

Colorado School of Mines (CSM), Duke University (DU) and the University of Nevada, Reno

(UNR). All contact angles were measured using the captive bubble method and doubly deionized

water as the contact angle probe liquid (T = 20°C). ..................................................................... 44


xi

Figure 3.11. Representative digital images of air bubbles on the SW30HR membrane immersed

in water at the CSM, DU, and UNR laboratories. Each of these air bubbles are representative of

those from which contact angle measurements were performed. ................................................. 44

Figure 3.12. Picture of the 2D certified contact angle fixed-drop calibration reference tool

(Ramé-Hart, Netcong, NJ). ........................................................................................................... 46

Figure 3.13. (left) Picture of a 3D contact angle reference tool, which includes the sample surface

stage, flat stainless steel plate, and stainless steel sphere. (right) Schematic illustration and

equations associated with using the 3D contact angle reference tool (θ = contact angle measured

through the stainless steel sphere, D = diameter of the stainless steel sphere, H = distance from

the top of the sphere to the top elevation of the stainless steel cross member, and P = distance

from the top elevation of the sample stage to the top elevation of the stainless steel cross

member. ........................................................................................................................................ 46

Figure 3.14. Photographs of the experimental apparatus that was designed and used to measure

contact angles on hollow fiber membranes. (left and middle) Image of the PTFE tubing

sandwiched between two glass slides and secured in the sample stage. (right) Image of an air

bubble that has been placed on the PTFE tubing immersed in water. .......................................... 48

Figure 3.15. Zeta potential data collected for the PMMA control surface from UNR – University

of Nevada, Reno (I = 2 mM KCl) and Anton Paar (I = 1 mM KCl). Polynomial fits (cubic) to

both data sets are also provided. . ................................................................................................. 49

Figure 3.16. Zeta potential as a function of solution pH for the ESNA1-LF membrane (I = 2 mM

KCl). Streaming potential measurements were by the UCR – University of California Riverside,

UNR – University of Nevada, Reno, the FKKT - University of Maribor, and Anton Paar. Those

data points from UCR, FKKT, and Anton Paar represent the average of three separate tests on

three different membarne coupons. ............................................................................................... 50

Figure 3.17. Zeta potential as a function of solution pH for the SW30HR membrane (I = 2 mM

KCl). Streaming potential measurements were by the UCR – University of California Riverside,

UNR – University of Nevada, Reno, the FKKT - University of Maribor, and Anton Paar. Those

data points from UCR, FKKT, and Anton Paar represent the average of three separate tests on

three different membrane coupons. ............................................................................................... 51

Figure 3.18. Zeta potential as a function of solution pH for the inner surface of the Norit X-Flow

hollow fiber membrane (I = 2 mM KCl, n = 3). ........................................................................... 53

Figure 3.19 Distribution of survey responses amongst each of the four respondent categories

(water/wastewater utilities, membrane manufacturers, characterization equipment vendors, and

academia). The total number of survey responses that were received was eleven. ...................... 54

Figure 3.20. Distribution of responses to the question on the usefulness of contact angle

measurements in membrane applications. .................................................................................... 55

Figure 3.21. Distribution of responses to the question on the usefulness of zeta potential values,

acquired through streaming potential measurements, in membrane applications. ....................... 57

Figure 3.22. Distribution of responses to the question on the usefulness of surface roughness

measurements, performed using an atomic force microscope, in membrane applications. .......... 60


xii

Figure 3.23 Difference in the calculated values for the minimum direct integrity test pressure

(Ptest) for obtaining a resolution of 3 μm assuming a standard error in the contact angle value of

5°. The values for Ptest were calculated using Eq. 3.1 (κ = 1, = 74.9 dynes/cm, BPmax = 3 psi).

....................................................................................................................................................... 64

Figure 3.24. Results of simple regression analysis for the NF-270 membrane. 100 ml of permeate

corresponds to 1.94 g loaded m-2

membrane. Correlation Coefficient = -0.996635; R-squared =

99.3281 percent; R-squared (adjusted for d.f.) = 99.1042 percent; Standard Error of Est. =

0.02384; Mean absolute error = 0.015953; Durbin-Watson statistic = 2.77534 (P = 0.6576); Lag

1 residual autocorrelation = -0.434792. The central line represents the regression models, the

inner boundaries represent the 95% confidence limit and the outer boundaries represent the

model prediction limit. .................................................................................................................. 65

Figure 3.25. Results of simple regression analysis for the ESNA1-LF membrane:. 100 ml of

permeate corresponds to 1.94 g loaded m-2

membranemembrane. Correlation Coefficient = -

0.999234; R-squared = 99.8469 percent; R-squared (adjusted for d.f.) = 99.7959 percent;

Standard Error of Est. = 0.00762926; Mean absolute error = 0.00468329; Durbin-Watson

statistic = 2.83777 (P=0.7339); Lag 1 residual autocorrelation = -0.62428. The central line

represents the regression models, the inner boundaries represent the 95% confidence limit and

the outer boundaries represent the model prediction limit. ........................................................... 66

Figure 3.26. Results of simple regression analysis for the ESPA2 membrane. 100 ml of permeate

corresponds to 1.94 g loaded m-2

membrane. Correlation Coefficient = -0.998841; R-squared =

99.7683 percent; R-squared (adjusted for d.f.) = 99.691 percent; Standard Error of Est. =

0.0134216; Mean absolute error = 0.00948347; Durbin-Watson statistic = 1.50241 (P=0.0550);

Lag 1 residual autocorrelation = 0.0623037. The central line represents the regression models,

the inner boundaries represent the 95% confidence limit and the outer boundaries represent the


Figure 3.27. Results of simple regression analysis for the SW30HR membrane. 100 ml of


membrane. Correlation Coefficient = -0.996067; R-

squared = 99.215 percent; R-squared (adjusted for d.f.) = 98.9534 percent; Standard Error of Est.

= 0.00316966; Mean absolute error = 0.0020736; Durbin-Watson statistic = 2.6779 (P=0.5877);

Lag 1 residual autocorrelation = -0.561779. The central line represents the regression models, the

inner boundaries represent the 95% confidence limit and the outer boundaries represent the


Figure 3.28. Results of simple regression analysis for the MUNIRO-400 membrane. 100 ml of


membrane. Correlation Coefficient = -0.994937; R-

squared = 98.9899 percent; R-squared (adjusted for d.f.) = 98.8215 percent; Standard Error of

Est. = 0.00624946; Mean absolute error = 0.00416953; Durbin-Watson statistic = 2.42804

(P=0.5686); Lag 1 residual autocorrelation = -0.352731. The central line represents the

regression models, the inner boundaries represent the 95% confidence limit and the outer

boundaries represent the model prediction limit. .......................................................................... 69

Figure 3.29. Normalized membrane flux (J/Jo) predicted by the simple regression models as a

function of bentonite clay loading. From this, membrane performance of all five membranes at at

a given clay load can be determined. A 5g m-2

bentonite load was chosen with which to generate

performance data for MLR analysis because this falls within a nearly linear portion of all five


xiii

relationships and membrane performance could be determined in all cases at this point by

interpolation of experimental data. ............................................................................................... 70

Figure 3.30. Results: Best MLR model prediction and UNR membrane data determined from

laboratory measurements. Statistics: R-Squared = 99.2736%; Adj. R-Squared = 97.0943%; Std.

Error of Est. = .00718711; M.A.E = .00251155; P-Value = 0.1069. Horizontal bars = 95%

confidence interval for J/Jo @ 5 g m-2

clay load estimated from laboratory data; vertical bars =

MLR model standard error of the estimate. .................................................................................. 73

Figure 3.31. Comparison of the prediction of membrane behavior by the MLR model constructed

from all the exemplars (filled circles) with prediction of five MLR models that were constructed

using data missing each of the individual membranes indicated (open circles). Horizontal bars =

95% confidence interval for J/Jo @ 5 g m-2

clay load estimated from laboratory data; vertical

bars = MLR model standard error of the estimate. ....................................................................... 75

Figure 3.32. Illustration of the decline in normalized permeate flux rate (J/Jo) with increasing

clay load. The plateau value (J/Jo, Plateau) is taken as a measure of the severity of the impact of

bentonite clay on the permeate flux rate for a membrane and is determined as the point where

J/Jo becomes zero order with respect to clay loaded. .................................................................... 78

Figure 3.31. Intrinsic water flux (J/Jo) as a function of the clay loaded (g m2) for the a) NF-270

and b) MUNIRO-400 membranes. Lines show Eq. 3.3 fitted to the data by nonlinear regression.

....................................................................................................................................................... 79

Figure 3.33. Linearly regressed relationship between J/Jo and K. The intervals are the asymptotic

95% confidence intervals from the individual nonlinear models. ................................................ 80

Figure 3.34. Plot of the observed and predicted K values for each of the different membranes

fouled by bentonite clay. R-squared = 98.82%, adjusted R-squared = 95.27%, p-value = 0.1363.

The value of K determined in the nonlinear regression models for the individual membranes

loaded with bentonite clay could be predicted well from roughness, initial water flux and contact

angle data. Zeta potential data were not required for the prediction (probably due to small

variation amongst the test membranes)......................................................................................... 82

Figure 3.35. Plot of the observed and predicted J/Jo Plateau values for each of the different

membranes fouled by bentonite clay. R-squared = 97.83%, Adjusted R-squared = 91.33%, p-

value = 0.1843. The MLR model was able to describe the majority of the variance in J/Jo Plateau

using roughness, contact angle and initial water flux. As with K, zeta potential data were not

required to explain the variance observed in J/Jo Plateau. ............................................................... 86

Figure 3.36. Mechanistic explanation of fouling by bentonite clay. ............................................ 87


xv

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agenda, including resources, treatment and operations, distribution and storage, water quality and

analysis, toxicology, economics, and management. The ultimate purpose of the coordinated

effort is to assist water suppliers to provide a reliable supply of safe and affordable drinking

water to consumers. The true benefits of the Foundation’s research are realized when the results

are implemented at the utility level. The Foundation's staff and Board of Trustees are pleased to

offer this publication as a contribution toward that end.

Roy L. Wolfe, Ph.D. Robert C. Renner, P.E.

Chair, Board of Trustees Executive Director

Water Research Foundation Water Research Foundation


xvii

ACKNOWLEDGMENTS

The PIs and Key Research Team Members would like to acknowledge the participation

of Dr. Ryan Heck of UNR in confidentiality negotiations with Pall Microza and GE Water. We

would also like to thank OCWD participants, Jana Safarik, and Richard Bold for contact angle

and AFM imaging measurements. We would like to express our gratitude to the Duke

University team – Professor Mark Wiesner, Zachary Hendren, and Dr. Soryong Chae for contact

angle and AFM measurements; the Colorado School of Mines team – Professor Tzahi Cath and

Matt Bolt for zeta potential and contact angle measurements); the University of California,

Riverside team – Professor Sharon Walker, Olgun Zorlu for zeta potential measurements; and the

University of Maribor team – Dr. Irena Petrinić for zeta potential measurements. Consultation of

Dr. K.C. Khulbe (University of Ottawa) with regard to AFM measurements is also

acknowledged. We greatly appreciate the assistance of Dr. Thomas Luxbacher of Anton Parr in

evaluating the zeta potential results and initiating an additional round robin of measurements.

Assistance from additional UNR personnel, Viktoriya Weirauch and Serife Ozger, is also

acknowledged. Lastly, we would like to acknowledge the WaterRF Project Manager, Jonathan

Cuppett and our Project Advisory Committee members, Michelle Chapman, Jesús Garcia-

Aleman, Carl Spangenberg, and Shahram Tabe.


xix

EXECUTIVE SUMMARY

INTRODUCTION AND BACKGROUND

A variety of techniques and approaches are routinely used for characterizing the physical

and chemical properties of membrane surfaces. Measuring the contact angle that is formed when

a droplet of water is placed on the membrane surface is used to quantify the

hydrophobicity/hydrophilicity of the membrane surface. Atomic force microscopy is used to both

quantify and visualize the morphology or roughness of membrane surfaces. Streaming potential

measurements are used to calculate a zeta potential, an indicator of surface charge, for membrane

surfaces under variable solution chemistries. While numerous other characteristics may be

measured for membrane surfaces these three aforementioned ones have perhaps received the

greatest interest and use from scientists, engineers, and membrane manufacturers. This strong

interest is attributed to the relative importance of each of these properties in determining

permeate flux rates, solute rejection, and fouling characteristics for microporous (microfiltration,

ultrafiltration) and nonporous (nanofiltration, reverse osmosis) membranes. For example, it is

now widely accepted that the specific flux rate (m3

m-2

day-1

Pa-1

) increases with increasing

hydrophilicity of the material that makes up the membrane. Similarly, hydrophilic and smooth

membrane surfaces tend to be less prone to severe fouling events. Contact angle with water, and

other liquids, in addition to zeta potential values are instrumental for describing the chemical

interactions that occur between colloids, microorganisms, and other materials and the membrane

surface. Such descriptions are generally made within the context of the Derjaguin-Landau-

Verwey-Overbeek (DLVO) model and its various extensions. Using this approach more specific

surface energy parameters, for example the Lewis acid-base values, for membranes may be

calculated using contact angle measurements. Despite the wealth of information that may be

derived from the aforementioned surface characterization techniques a standard method for

applying these techniques to membrane surfaces is lacking.

Surface characterization techniques rely on rather exhaustive sample preparation

procedures and a thorough understanding of the properties of the sample material. In the absence

of either of these requirements it is difficult at best to generate reproducible results. The situation

for polymeric membranes is further complicated by the lack of specific information about

membrane material composition/chemistry and the use of preservatives for long-term storage.

Not surprisingly these complicating factors have resulted in numerous disagreements between

any two labs studying the same membrane under similar operating conditions. Furthermore, in

light of the often-conflicting results it is nearly impossible to derive usable information from

these laboratory studies for utilities and other end users. It is therefore necessary that a standard

method be developed for conducting and interpreting the results from contact angle, streaming

potential and atomic force microscope measurements.

OBJECTIVES

The overall objective of this project was to develop standard methods for both conducting

and interpreting the results from contact angle, streaming potential, and atomic force microscope

measurements for characterizing membrane surfaces. The motivation for this overall objective is

the improvement of our ability to evaluate the role(s) of membrane properties in determining


xx

membrane performance. The specific research goals that were pursued to achieve the overall

objective of this research project were as follows:

1. Identify three techniques for characterizing the physical and chemical properties of

membrane surfaces,

2. Quantify the variability between data generated using the selected characterization

techniques in order to develop the associated standard methods for their use by the

membrane community,

3. Provide drinking water utilities with information on the value of membrane

characterization methods, a means by which to compare membrane properties on an

equal basis, and guidance for accurate contact angle measurements for integrity test

calculations,

4. Evaluate the ability of those surface characteristics determined using the proposed

standard methods to accurately predict membrane performance and fouling.

APPROACH

The following research tasks were conducted in order to achieve the overall objective of

this project:

Task A: Identification and review of characterization methods

Task B: Identification and selection of membranes for analysis

Task C: Development of standard methods for characterizing membrane surfaces

Task D: Obtain feedback from partner utilities on the proposed standard methods

Task E: Characterize select membrane properties using the proposed standard methods

Task F: Conduct membrane fouling studies

Task G: Evaluate relationships between membrane characteristics and membrane

performance

Task H: Develop guidance for utilities doing integrity test calculations

Following a comprehensive review of the available literature on membrane surface

characteristics and performance the following three characterization techniques were selected for

inclusion in this project: contact angle (surface energy), atomic force microscopy (AFM)

(surface morphology), and streaming potential (zeta potential) measurements. Membrane

samples that included microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse

osmosis (RO) membranes were selected and acquired based on their use in the water treatment

industry. Standard (control) surfaces were also identified and acquired for each of

aforementioned characterization techniques that were studied as part of this project. Surface

samples (membranes and standard surfaces) were distributed, along with guidance on how to

conduct each type of characterization technique, to three separate laboratories. The participating

laboratories took measurements, assessed precision, and provided input on the proposed standard

techniques. Next, membrane fouling (or more specifically, membrane resistance to fouling) was

evaluated under laboratory conditions using a suspension of bentonite (clay) as the fouling

solution. Membrane properties were then correlated with membrane performance metrics.


Executive Summary | xxi

xxi

RESULTS AND CONCLUSIONS

The following conclusions were reached regarding the development and application of

standard techniques for characterizing membrane surfaces, and specifically for executing surface

characterization using contact angle, surface roughness, and streaming potential measurements:

Operating an AFM in contact mode produces more consistent roughness statistics when

imaging the outside of tubular structures. Therefore, when imaging hollow fiber

membrane surfaces, whether the concave or convex surface, the AFM should be operated

in contact rather than tapping mode.

A linear relationship exists between the root mean square and average roughness statistics.

Therefore, both parameters may be equally applied for drawing correlations between

membrane surface morphology and membrane fouling.

The inherent physical heterogeneity of membrane surfaces requires that greater than three

sites be imaged (i.e., their roughness measured) in order to develop an accurate assessment

of membrane surface roughness.

Surface roughness measurements using the AFM technique is a precise measure of

membrane surface morphology based on the reproducibility and repeatability of surface

roughness statistics between two laboratories. The precision of the measurement is,

however, based on the two laboratories using the standard technique.

Using the standard technique, the captive bubble method produces precise contact angle

results for membrane surfaces with a repeatability and reproducibility between any two

laboratories of approximately 5°. This level of precision allows for equal comparison of

relative assessments of membrane surfaces with regards to their hydrophobicity (i.e., is the

membrane hydrophobic or hydrophilic). However, the current level of precision does not

lend itself well to the comparison of surface energy parameters calculated between two

laboratories using contact angles measured at each laboratory. Further study is needed to

determine how variations in contact angle results affect the magnitude and sign of surface

energy parameters determined through contact angle analysis to establish the broader

significance of these values to the membrane community (i.e., are these surface energy

parameters useful in membrane operations?).

The proposed standard method for determining the zeta potential of membrane surfaces

using streaming potential measurements produces relatively reproducible results

independent of the type of electrokinetic analyzer used. However, minor deviations from

the proposed standard method (e.g., differences in sample pretreatment) can result in

substantial errors.

The availability of characterization equipment and operator skill level requirements are the

greatest impediment to membrane characterization tests (contact angle, streaming

potential, surface roughness) being carried out by utilities.

From the perspective of utilities, characterization results, such as contact angle, surface

roughness, and streaming potential, are only useful during membrane selection and lose


xxii

their usefulness once membranes are put into service. A key challenge that was identified

in this regard is how to determine membrane characteristics without damaging the

membrane modules or elements (i.e., how to determine these characteristics without

conducting a destructive autopsy).

The following conclusions were reached regarding the correlation between membrane

surface properties determined using the proposed standard techniques and membrane fouling (or

more specifically, membrane resistance to fouling):

The permeate flux loss that was observed upon fouling of the five membrane samples with

a bentonite clay suspension at a constant feed pressure could be modeled statistically to

express normalized permeate flux as a function of the clay loading rate (5 g m-2

). Based on

these observations a multiple linear regression (MLR) model was developed and found to

be capable of describing >97% of the observed variance in normalized product flux.

Those membrane surface properties that were determined to be necessary for describing

the observed membrane performance included root mean square roughness, contact angle

and initial permeate flux but not zeta potential. This implies that surface roughness and

hydrophobicity have a greater influence on bentonite clay cake density/water permeability

than do surface charge interactions. Overall, better membrane performance (higher

observed normalized flux after 5 g m-2

bentonite loading) was associated with greater

RMS roughness, higher initial water flux and greater contact angle (greater surface

hydrophobicity).

Membrane surface properties (roughness, hydrophobicity and charge) could be used to

predict membrane performance in the presence of foulant materials using numerical

modeling approaches.

APPLICATIONS AND RECOMMENDATIONS

The membrane and standard surface characterization results demonstrate the reproducible

and accurate AFM generated surface roughness, contact angle, and zeta potential results can be

generated independent of the type of characterization equipment used. The generation of such

data is however dependent on the use of standard methods and experimental techniques, which

are presented in this report. The absence of such standard methods and techniques is likely to

result in variability in the aforementioned measures of membrane surface characteristics.

Process Design and Operational Impacts

Improving the accuracy and reliability of membrane characterization results, in particular

contact angle with water (hydrophobicity) and zeta potential (charge) is expected to further

improve the process by which membranes are selected for different applications. While further

research is needed to fully realize the implications of improved characterization results for

predicting membrane performance and predicting membrane fouling, the preliminary results

given in this report suggest that it could substantially improve the accuracy of predictive fouling

models. The development, and ultimate application, of more accurate predictive membrane

performance models is likely to facilitate better membrane selection and operation. For example,


Executive Summary | xxiii

xxiii

membranes may be selected whose surface chemistry is suited for minimizing fouling under a

given set of feed water quality conditions.

Data Interpretation

Use of the standard methods developed as part of this project can facilitate the generation

of reproducible contact angle, streaming potential (zeta potential), and AFM produced surface

roughness statistics for membrane surfaces. These findings appear to be independent of the make

and model of the respective characterization equipment used. However, data that is produced in

the absence of these standard methods are open to substantial variability and error. Therefore,

membrane characterization data, and any extensions (interfacial modeling), must be analyzed

and interpreted with great caution. This is particularly important in the absence of any

accompanying detailed information on the methods and techniques used. Based on these findings

it is highly recommended that laboratories carrying out membrane characterization studies

strictly adhere to the standard methods given in this report. Furthermore, consideration should be

given to developing a certification program for those laboratories seeking to carry out membrane

characterization work. Implementing these, and perhaps additional, standardization measures

will greatly improve the reproducibility and accuracy of membrane characterization results

(contact angle with water, zeta potential, and surface roughness statistics).

Regulatory Impacts

Based on the data collected as part of this effort the standard error in the measured

contact angle value for a given surface between any two labs is approximately 5°. This

variability in contact angle results has implications for the calculated minimum direct integrity

test pressure for obtaining a resolution of 3 μm (Ptest), which is required for direct integrity

testing of hollow fiber membranes. The difference in calculated Ptest values, assuming an error in

the measured contact angle of 5°, is dependent on the magnitude of the measured contact angle.

Over a range on contact angle values of 0 to 90° and an error in the contact angle value of 5°, the

error in the calculated Ptest value may range from 0.1 to 1.3 psi.


xxiv

PARTICIPANTS

A list of subcontractors, participating utilities, and other individuals who contributed to

this project are listed below. We gratefully acknowledge the valuable contributions to this project

from all of the participants.

Subcontractors, Participating

Utilities and Other Participants

Pierre Kwan, P.E.

HDR Engineering, Inc. (HDR)

Prof. Jonathan A. Brant

University of Wyoming

Don Phipps

The Orange County Water District (OCWD)

Greg Turman

City of Clearwater

Annika M. Bankston, P.E.

Minneapolis Water Works

Uzi Daniel

West Basin Municipal Water District

Prof. Mark Wiesner

Duke University

Prof. Tzahi Cath

Colorado School of Mines

Prof. Erik Hoek

University of California, Los Angeles

Carl Clegg

Ramé-Hart Instruments Company

Thomas Luxbacher, Ph.D.

Anton Paar GmbH


1

CHAPTER 1 INTRODUCTION AND BACKGROUND

Membrane-based pressure driven processes constitute essential and mature technology with numerous applications in water treatment and drinking water production. Despite advances in membrane science, the application of membrane processes continues to be hampered by the persistent problem of membrane fouling. Membrane fouling results from the deposition, adsorption, and/or accumulation of rejected species on, or within the membrane, resulting in the deterioration of water flux and quality.

Membrane processes are playing an ever-increasing role as environmental engineers continue to seek sustainable methods for water treatment, wastewater reclamation, groundwater restoration, and pollution prevention. Mitigation of membrane fouling and maximization of water productivity are essential for optimizing membrane processes. Membrane fouling results from the attachment, accumulation, or adsorption of substances onto the membrane surface and/or within the membrane pores; it hinders membrane performance and shortens membrane life. Physical and chemical interactions between solutes or particles and the membrane interface substantially affect water productivity and the rate at which membrane fouling occurs. Previous research has unambiguously linked membrane characteristics to the rate and extent of membrane fouling, and therefore, to the water productivity and overall performance of membranes. Contact angle, surface roughness, and streaming potential are three surface characterization methods that appear to be relatively simple; the measurements appear to be routine and the physics behind the equations describing these properties appears to be straight-forward. However, literature on these surface characterization methods has shown that they are quite complex and far from being completely understood, especially zeta potential via streaming potential measurements (Yaroshchuk and Ribitsch, 2002).

CONTACT ANGLE MEASUREMENTS

The contact angle (θ) that is formed at the three-phase interface between solid, liquid, and gas/vapor phases (Figure 1.1) may be used to elicit information regarding membrane surface energy properties. There are two accepted techniques for measuring contact angle, sessile drop and captive bubble, as shown in Figure 1a and 1b, respectively. The information that is gathered from contact angle analysis may be used to calculate specific surface energy properties (van der Waals, Lewis acid-base) for detailed interfacial analyses, as well as for qualitatively assessing the wettability, or hydrophobicity/hydrophilicity, of a membrane surface. This latter application is perhaps the most common use for contact angle data by utilities and other membrane users. Perhaps the greatest challenge with goniometric contact angle measurements is contact angle hysteresis, which is the difference in the measured contact angle depending on whether it is an advancing or receding measurement. Advancing measurements involve the spreading of a liquid drop over a dry surface. Conversely, receding measurements involve the liquid droplet shrinking and thus the contact angle is measured on an already ‘wet’ surface. Receding measurements are most commonly associated with the captive bubble technique in which an air bubble is placed on a surface that is immersed in water. In this way, the water recedes from that volume that is


2

occupied by the air bubble (Figure 1.1b). The effect can be as large as a 20° difference in advancing and receding contact angle values, but can also be negligible (Adamson and Gast, 1997; Garbassi et al., 1998). For a brief, recent account of the origin of contact angle hysteresis see Gao and McCarthy (2006). Although theories of contact angle and contact angle hysteresis are still controversial, the measurement does have numerous practical applications (e.g., calculation of surface energy components and pore-liquid entry pressures).

Figure 1.1 (a) Representative digital image of a liquid droplet on a dry surface in which the contact angle (θ) is measured according to the sessile drop method. (b) Representative digital image of an air bubble on a wetted membrane surface (the membrane is immersed in a liquid) and the contact angle (θ) is measured according to the captive bubble technique.

Perhaps the greatest advantages of contact angle measurements are the relatively simple principles of the measurement technique and that the required equipment (goniometer) is relatively inexpensive. Note that these relative comparisons are all being made to the requirements for other characterization measurements, such as streaming potential and electron microscopy techniques. That said, details associated with the algorithms that are used for tangent line determination in digital images by vendor supplied software can be complicated and are not usually revealed by the vendors.

From a practical point of view, the captive bubble technique, in which the gaseous phase is introduced as a bubble under the membrane surface, is more viable than the sessile drop technique, which requires rigorous drying of the membrane surface. However, with the captive bubble technique, it is sometimes confusing as to which angle should be reported. As contact angle is defined as the angle measured through the denser fluid phase, the contact angle through the liquid phase should always be reported. This means that if the goniometer (because of object configuration software requirements) measures the angle through the gas phase (θ*) then the supplementary angle to the θ* angle (180°- θ*) should be reported. Interestingly, two references on this subject cited here (Adamson and Gast, 1997; Garbassi et al., 1998) have figures suggesting otherwise; also an example in recent literature (Roudman and DiGiano, 2000) is further indication of confusion on this issue. Another aspect of the contact angle measurement that can be confusing is the distinction between advancing and receding angles. The use of these terms is apparent for sessile drop measurements, but is sometimes confused for captive bubble measurements. As can be seen, in Figure 1.2 attention should not be placed on whether the drop

a) b)


3

or bubble is growing or shrinking, but instead on whether the liquid phase is advancing or receding, because it is the solid-liquid contact angle that is of interest (Drelich, 1997).

Figure 1.2 Techniques commonly used for carrying out contact angle measurements: (a) static sessile drop – advancing and receding; (b) static captive bubble – advancing and receding; and (c) dynamic captive bubble. Figure adapted from Drelich (1997). For those illustrations for the sessile drop technique the term advancing refers to the advancement of the liquid (increase in the volume of the liquid droplet) over a dry surface, while the term receding refers to the recession of the liquid droplet (decrease the in the volume of the liquid droplet) over a previously wet surface. A similar relationship exists for the captive bubble measurements, where the water in which the surface is immersed either advances or recedes across the surface through a decrease or increase in the volume of the air bubble.

Another important issue for contact angle measurement reproducibility is the effect of drop and bubble size on contact angle. Drelich et al. (1996; 1997) performed a study on a variety of surfaces (polymers, monolayers on gold, minerals) and made several conclusions. First, for smooth, homogenous, nearly-ideal surfaces, contact angle does not depend on drop size (Figure 1.3), although hysteresis can remain detectable. Second, dynamic captive bubble results approach results for receding sessile drop or bubble. Third, receding sessile drop results are most

Gas

Liquid

c)

Receding or intermediate contact angle

Pressure air

Buoyancy force

Gas

Liquid

b)

Advancing contact angle

Receding contact angle

Gas

Liquid

a)

Advancing contact angle

Receding contact angle

Liquid

Gas


4

prone to change with drop size on non-ideal surfaces. The advancing angle should be more reproducible, as it depends on bubble/drop size to a lesser degree, on both smooth and imperfect surfaces. However, information on surface quality that can be gained from receding angle/bubble size relationship will be lost if only advancing angle is measured. Drelich et al. (1996; 1997) offered explanations for some of these facts based on the concept of line tension. Line tension is the energy associated with the line where the three phases meet and has dimensions of energy/length, as opposed to energy/surface units for surface tension. It was concluded that further discussion and research is necessary to better understand observed phenomena.

Tadmor (2004) used the line tension (energy) concept to express the experimentally elusive equilibrium contact angle as a function of the advancing and receding contact angles. However, calculations of the equilibrium value require precise measurement of both the advancing and receding angles, something that is not practical in non-laboratory applications.

Figure 1.3 The effect of drop (bubble) size on advancing (Adv.) and receding (Rec.) contact angles for the air /water/polyethylene film system as obtained with the static sessile-drop (SD), static captive-bubble (CB), and dynamic captive-bubble (DCB) techniques. Source: Reprinted from Journal of Colloid and Interface Science, 179 (1), Drelich, J., Miller, J.D., Good, R.J., The Effect of Drop (Bubble) Size on Advancing and Receding Contact Angles for Heterogeneous and Rough Solid Surfaces as Observed with Sessile-Drop and Captive-Bubble Techniques,37-50, 1996, with permission from Elsevier.

Another interesting factor is the exposure time during which contact angle is measured. For special cases, when the solvent alters the surface being characterized (e.g., when swelling occurs) significant time drift of contact angles (both advancing and receding) can be observed (Adamson and Gast, 1997). However, use of the captive bubble technique and a conditioned membrane can eliminate the time drift problem. Measurement temperature is another factor that may potentially affect the measured contact angle value. For example, Schonhorn (1966) concluded based on theoretical considerations, that for polar liquids and polypropylene, the dependence of contact angle on temperature should be minimal (Δθ = 2° for T ≤ 100°C). He further concluded that contact angle should decrease as the temperature of the probe liquid increases. Such a dependence of contact angle on temperature is likely attributed to the


5

dependence of liquid viscosity on temperature. Surface titration can provide information on the dependence of contact angle on the chemical interactions of a solution with a solid surface (Adamson and Gast, 1997). Mänttäri et al. (2006) report contact angle changes as function of pH for several commercial membranes. In the same paper, zeta potentials as functions of pH are also shown. It is interesting to note that these plots of contact angle as a function of pH correlated with zeta potentials plotted as absolute values. See also Brant et al. (2006) for further examples of contact angle titration. Even though performing surface titration bears prohibitive labor cost for routine analysis (multiple samples and solutions to assure statistically significant data), it underscores the need for control of pH of the water used in the study.

Boussu et al. (2008) used multiple linear regression (MLR) to study the influence of surface hydrophobicity, surface roughness, surface charge, molecular weight cut-off, permeability, and porosity of the top layer on nanofiltration membrane performance. Contact angle, volume fraction of small pores, and membrane charge were the most significant variables in predicting relative flux and adsorbed amount as well as retention of dissolved organic compounds by five commercial NF membranes. Also, contact angle measurements are useful in membrane integrity testing (if one wishes to use a realistic estimate, rather than a worst case scenario value of 1 for cos θ) as outlined by EPA in Membrane Filtration Guidance Manual (Alklaibi and Lior, 2005). The need for determination of the pressure necessary for integrity testing stems from requirements of EPA’s Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) (Allcock et al., 2006). For example, Cryptosporidium oocysts constitute a public health risk as they cause gastro-intestinal illness and resist chlorine-based disinfectants. The size of Cryptosporidium oocysts (spores) is approximately 3 μm. The net pressure applied during direct integrity tests must ensure that any breach large enough to pass Cryptosporidium oocysts (3 μm) would also pass air during the test (EPA, 2005). The minimum direct integrity test pressure, otherwise known as the pore liquid entry pressure, is a function of both the liquid characteristics (surface tension) and the membrane characteristics (hydrophobicity and pore structure):

maxcos193.0 BPPtest += θκσ (1.1)

where Ptest is the minimum direct integrity test pressure (psi), κ is the pore shape correction factor (dimensionless), σ is the surface tension at the air-liquid interface (dynes/cm), θ is the liquid-membrane contact angle (degrees), BPmax is the maximum backpressure on the system during the test (psi), and 0.193 is the constant that includes the defect diameter (i.e., 3-µm resolution requirement) and unit conversion factors (EPA, 2005). This equation appears as Equation 4.1 in the Membrane Filtration Guidance Manual (EPA, 2005):

“The LT2ESWTR does not establish the minimum test pressure to be used during a pressure-based direct integrity test, but rather only requires that the test achieve a 3-µm resolution. If a membrane manufacturer has information to support the use of values other than κ = 1 and θ = 0, and these less conservative values are approved by the State, then Equation 4.1 can be used to calculate the minimum required test pressure. It is essential that the use of values other than κ = 1 and θ = 0 be scientifically defensible, since the use of inappropriate values could result in the use of a test pressure that does not meet the resolution criterion established by the rule.


6

One approach for determining membrane-specific values for κ and θ is through direct experimental evaluation. Because these parameters can have a significant effect on the required direct integrity test pressure, it is strongly recommended that States require sufficient justification from a membrane manufacturer prior to approving the use of values other than κ = 1 and θ = 0, such as independent third party testing results using a method accepted by the scientific community and demonstrating statistically significant data.”

It is clear that in order to reduce the pressure difference necessary for integrity testing and to achieve the resolution requirement, reliable estimation of membrane contact angle is required. The linear dependence of pressure drop on cos θ (Eq. 1.1) implies that for θ = 30°, a pressure drop 13% less than the worst case scenario estimate (θ = 0) can be used. An uncertainty of 1° for θ = 30° (i.e., θ = 30 ± 1°) will translate into 1% error bars for the required pressure drop. The same absolute error bar for θ will translate into greater absolute and relative cos θ uncertainty for more hydrophobic membranes (increasing θ in degrees: |d cos θ| = (π/180)|sinθ||d θ|).

ZETA POTENTIAL MEASUREMENTS

Membrane surface charge is not directly experimentally accessible. Instead, zeta potential is used as an approximation for both the magnitude and sign (+ or –) of membrane surface charge. Zeta potential (ζ) is the potential difference between the bulk of solution and the sheer (slipping) plane of the interfacial double layer [Figure 1.4] (Delgado et al., 2005). Zeta potential is a function of surface and solution chemistry (pH, ionic composition, and ionic strength) at the solid-liquid interface and is an important membrane characteristic for assessing membrane fouling potential and developing chemical cleaning protocols. Membrane zeta potential is typically determined from streaming potential measurements. A streaming potential is generated when an electrolyte solution flows through a thin channel or porous media (e.g., a sand column) and is related to zeta potential by the Helmholtz-Smoluchowski equation:

0Ep

εε ζλη

= (1.2)

where E is the streaming potential due to electrolyte flow through a capillary channel, p is the applied pressure driving the flow, ζ is the zeta potential, λ is the electrolyte conductivity, η is the viscosity of the electrolyte solution, ε is the relative permittivity of the solution (dimensionless), and ε0 is the vacuum permittivity (fundamental constant). Values of E, p, and λ are measured by the streaming potential analyzer; while ε and η are calculated based on temperature measurement (empirical fit functions for pure water data are used).

In experimental investigations of membrane charge, streaming potential measurements have typically been used to calculate zeta potential. Streaming potential is the potential induced when an electrolyte solution is pumped across a stationary, charged surface. Streaming potential can be used to calculate zeta potential using the Helmholtz-Smoluchowski equation, which relates the pressure dependence of streaming potential to the properties of the solution (i.e., conductivity and viscosity). Unlike earlier streaming potential investigations, the majority of more recent investigations (e.g., (Nyström et al., 1995; Childress and Elimelech, 1996;


7

Hagmeyer and Gimbel, 1999; Peeters et al., 1999; Schäfer, 1999; Childress and Elimelech, 2000; Ernst et al., 2000; Tay et al., 2002)) measure streaming potential over a range of solution pH. Investigating surface charge as a function of pH is crucial for understanding acid-base properties of membrane surface functional groups.

Although streaming potential measurements are the most frequently used method for evaluating charge properties, they have also been criticized. Results from prior studies reveal uncertainty in individual measurements as well as data scatter (Chapman-Wilbert et al., 1999). Further, the question of overall reproducibility has not been addressed. Other concerns include the effect of membrane roughness on the measurement, vagueness of the relationship between the measured zeta potential and the double layer structure, the inherent assumptions of the Helmholtz-Smoluchowski equation (i.e., laminar flow), and the lack of a calibration standard.

Figure 1.4 Illustration of the relative locations of the Stern layer and Shear plane from a charged surface in water and the associated change in surface potential with distance. Source: Reprinted from Journal of Membrane Science, 161 (1-2), Chapman-Wilbert, M., Delagah, S., Pellegrino, J., Variance of streaming potential measurements, 247-261, 1999, with permission from Elsevier.

Although the operating principles and requirements for measuring streaming potential (e.g., voltage, pressure) are relatively straightforward, commercial instruments are much more common than lab-made instruments. However, due to the small market for these instruments, they are relatively expensive. This is an additional disadvantage of the technique. However, zeta potential from streaming potential measurement is one of few techniques capable of describing the charge properties of membranes.


8

SURFACE ROUGHNESS – ATOMIC FORCE MICROSCOPY (AFM)

Surface roughness may be measured with atomic-level resolution using atomic force microscopy (AFM). AFM is a non-optical surface imaging technique which approaches atomic resolution. In the most common implementation, a sharp tip on a cantilever is scanned at constant force across a surface to provide a surface profile. The force between the tip and the surface always has a van der Waals (dispersion) force component, but cantilevers carrying charge are also available and can be used to image electric potential. A laser beam is reflected off the back of a cantilever and its position determined using a photo-diode. A surface may be scanned using any number of operating modes (e.g., contact, tapping, non-contact). Tapping mode is most commonly used to characterize membrane surfaces as contact mode may result in damage to the membrane surface. Tapping mode uses a rapidly oscillating cantilever in the vicinity of the surface, and amplitude damping is used for imaging. Only short, intermittent contact of the AFM tip with the sample (tapping) occurs, which is especially suitable for membrane surfaces. Measuring the position and movement of the cantilever as it is scanned over a membrane surface allows for the direct measurement of surface features.

The membrane surface can be scanned in vacuum, air, or water and no sample preparation is necessary (Mulder, 1996). An example illustration of an AFM experimental setup is given in Figure 1.5. Based on the sample profile, h(x, y), it is possible to calculate mean and RMS roughness (Ra and Rrms, respectively) and 10-point mean roughness (Rz) parameters (Chung et al., 2002). Chung et al. (2002) determined the relationship between Ra and the water flux and separation performance of hollow-fiber UF membranes. As Ra decreased, flux decreased and rejection increased. This example illustrates the relevance of AFM characterization to membrane performance.

Figure 1.5 Schematic illustration of the operating principles associated with the atomic force microscope (AFM). Taken from Wyart et al. (2008). Source: Reprinted from Journal of Membrane Science, 315 (1-2), Wyart, Y., Georges, G., Demie, C., Amra, C., Moulin, P., Membrane characterization by microscopic methods: Multiscale structure, 82-92, 2008, with permission from Elsevier.


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TRANSMISSION ELECTRON MICROSCOPY (TEM)

In transmission electron microscopy (TEM) an image is produced by passing electrons under high vacuum through a sample. Near-atomic resolution is attainable using TEM; however this technique is limited to thin samples that are at most several tens of nanometers in thickness. Therefore, TEM is restricted to imaging thin-film samples rather than fully constructed membranes. An example TEM image of a graft copolymer is shown in Figure 1.6. Kim et al. (2008) concluded that the differences in electron densities between the two components of this amphiphilic graft copolymer were sufficient to be observed as image contrast. Dark regions represent the hydrophobic crystalline regions of the main chains, whereas lighter regions represent the hydrophilic side chains. In another example, use of sophisticated cutting tools (e.g., a diamond knife ultramicrotome) enabled Blom et al. (2003) to study cross-sectional samples of a proton exchange membrane.

Figure 1.6 Transmission electron microscope (TEM) image of a graft copolymer film. Taken from Kim et al. (2008). Source: Reprinted from Journal of Membrane Science, 325 (1), Kim, Y., Park, J.T., Koh, J.H., Roh, D.K., Kim, J.H., Anhydrous proton conducting membranes based on crosslinked graft copolymer electrolytes, 319-325, 2008, with permission from Elsevier.

More recently, tomography capability has been added to the TEM technique (TEMT). The majority of publications using TEMT pertain to polymers. In TEMT, TEM images are collected from single specimens at different tilt angles and are processed to generate 3D representations of nano-structures. In a paper by Jinnai et al. (2006), 200-nm thick co-polymer samples were studied. Images clearly show the porous structure of the material (Figure 1.7). No reports have been found pertaining to TEMT for polymeric membranes for water treatment. In terms of sample preparation, the vacuum environment requires dry samples. Also, coating may


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be necessary to prevent sample destruction by the electron beam. These requirements constitute disadvantages of TEM as they can lead to artifacts.

Figure 1.7 Ordered and disordered co-polymer blend morphologies visualized using TEMT method. Taken from Jinnai et al. (2006).

SCANNING ELECTRON MICROSCOPY (SEM)

For scanning electron microscopy (SEM) measurements a sample surface is exposed to a narrow beam of electrons in vacuum. Secondary electrons are liberated form the surface and detected. These conditions require sample preparation – drying and coating with conductive surface (in most cases). Sample preparation can lead to artifacts in observed images (Mulder, 1996). Resolution of 10 nm or better can be achieved, but varies with equipment available. Pore size, surface porosity, and pore geometry can be visualized (Mulder, 1996) and porosity and pore size distribution can be estimated. Asymmetry in composite membranes can also be readily observed. Li et al. (2008) used SEM to study polysulfone/sulfonated PEEK membranes. Changes in pore structure resulting from different casting conditions and composition are shown in Figure 1.8.


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Figure 1.8 Scanning electron microscope (SEM) images of cross-sections of polymer blend membranes. Taken form Li et al. (2008). Source: Reprinted from Journal of Membrane Science, 324, Li, X., De Feyter, S., Vankelecom, I.F.J., Poly(sulfone)/sulfonated poly(ether ether ketone) blend membranes: Morphology study and application in the filtration of alcohol based feeds, 67-75, 2008, with permission from Elsevier.

Obtaining cross-section samples usually involves razor blades and can result in compression and tearing. The direct freeze fracture method, where a membrane is made brittle in liquid nitrogen and is then broken, can also be used. Co-principal investigator Phipps was involved in developing a new cryo-snap technique, where the membrane is embedded in ice before a fracture is made. Perhaps the resolving power of SEM measurements is the greatest advantage of this technique – not reaching atomic level, but well beyond optical microscope resolution, it is perfectly suited for the study of membrane pores. Very rough samples that can cause a large amount of tip artifacts in AFM (Müller and Mehnert, 1997) are not a problem in SEM. Also, the preparation of a thin film, as is required in TEM, is not required for SEM. The


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only disadvantage is that membrane pore structure can be affected by drying and coating, so SEM-observed porosity may not exactly correspond to in situ membrane porosity.

CHEMICAL FORCE MICROSCOPY (CFM)

Chemical force microscopy (CFM) is an innovative extension of AFM. CFM uses a chemically modified AFM tip to investigate specific interactions with, and ultimately the chemistry of, a membrane surface. Often a microsphere, or a colloidal particle, with specific functional groups is placed on the tip to create a functionalized probe. For example, a tip covered with acidic carboxylic groups will exhibit enhanced attraction to polar, rather than hydrophobic, sites on the surface. CFM experiments on membranes are frequently performed in aqueous solutions in order to best simulate in situ interactions. There is no need for surface preparation as in SEM. The distribution of adhesion values that were measured across the HL and SG membrane surfaces using various (hydrophobic and hydrophilic) probes are reported in Table 1.1. This data may be used to characterize the relative chemical heterogeneity across a membrane surface (i.e., the distribution of hydrophilic [low adhesion] and hydrophobic [high adhesion] areas). This data may be further supported using CFM resolved images of the membrane surface (Figure 1.9), which are similar to the 3D rendered images of membrane surfaces that are produced using conventional AFM. For example, Brant et al. (2006) used CFM images of two membrane surfaces in order to understand their associated fouling tendencies.

Table 1.1 Distribution of adhesion across the HL and SG membrane surfaces as a function of surface

area coverage measured with methyl, carboxyl and hydroxyl functionalized probes (I = 0.01 M NaCl; pH 6; T = 20 °C).

Probe High Adhesion Moderate Adhesion Low Adhesion

Surface area coverage (%)

HL Membrane

Methyl 5.2 88.0 6.8

Carboxyl 97.2 1.6 1.2

Hydroxyl 7.8 88.9 3.3

SG Membrane

Methyl 5.1 22.2 72.7

Carboxyl 93.0 6.7 0.4

Hydroxyl 0.3 21.7 78.0 Source: Reprinted from Colloids and Surfaces A:- Physicochemical and Engineering Aspects, 280 (1-3), Brant, J.A., Johnson, K.M., Childress, A.E., Characterizing NF and RO membrane surface heterogeneity using chemical force microscopy,45-57, 2006, with permission from Elsevier.


http://www.sciencedirect.com/science/article/pii/S0927775706000689?_rdoc=8&_fmt=high&_origin=browse&_srch=hubEid(1-s2.0-S0927775706X03011)&_docanchor=&_ct=37&_refLink=Y&_zone=rslt_list_item&md5=48f56cdc0c5011fc5389a77e9fd16325


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Figure 1.9 Topographical and corresponding CFM images for the HL membrane acquired with a CH3-, COOH-, and OH-functionalized tip (I = 0.01 M NaCl; pH = 6.1, and T = 20 °C). Source: Reprinted from Colloids and Surfaces A:- Physicochemical and Engineering Aspects, 280 (1-3), Brant, J.A., Johnson, K.M., Childress, A.E., Characterizing NF and RO membrane surface heterogeneity using chemical force microscopy,45-57, 2006, with permission from Elsevier.

Yamamura et al. (2008) performed CFM experiments on water treatment membranes using hydroxyl- and carboxyl-modified microspheres. The hydroxyl-coated microspheres exhibited greater affinity for the membrane surface than the carboxyl-modified microspheres. Exponential decay of the affinity was observed with time as a result of membrane fouling (Figure 1.10).




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Figure 1.10 Changes in adhesion force of carboxyl-modified microspheres (a) and hydroxyl-modified microspheres (b) to PE membranes that were sampled during the pilot-filtration test. An SEM image of a functionalized polystyrene bead glued to a cantilever tip used in this study is shown on the right. Taken from Yamammura et al. (2008).

Alsteens et al. (2007) used CFM with hydrophobic tips to measure forces on organic self-assembled monolayers and on bacteria surfaces; contact-mode images and force-distance curves were reported. A linear relationship was observed between the adhesion force and cos θ, where θ is the macroscopic surface-water contact angle. Li et al. (Li and Elimelech, 2004) used carboxyl-modified colloid probes to study NF-270 membranes. Correlations between the measured adhesion forces and the fouling and cleaning behaviors of the membrane were observed. The authors used CFM measurements to investigate the role of calcium ions in natural organic matter fouling and concluded that Ca2+ ions enhance fouling through complexation.

X-RAY PHOTOELECTRON SPECTROSCOPY (XPS)

In X-ray photoelectron spectroscopy (XPS), the sample surface is subjected to X-ray radiation capable of removing electrons from the inner shells of the atoms (except H and He). The amount of emitted electrons is recorded as a function of binding energy. Surface depths of 0.5 -10 nm can be probed, depending on incident beam angle (Mulder, 1996). The inner shell energies are characteristic for a given element, but somewhat sensitive to the external chemical environment. For carbon, this chemical shift can amount to a few eV: for the carbonyl group (C=O), the binding energy is 285 eV and for the methylene group (CH2), this binding energy is


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287.8 eV (Mulder, 1996). Routine detection limits are 0.1%. From the emitted electron intensities surface atomic concentrations (empirical formulas) can be deduced. XPS results for a variety of membranes are reported in Table 1.2. Notably, all three polyether sulfone membranes (NTR7450, N30F, and NFPES10) showed a prominent percentage of nitrogen, in disagreement with the composition of the bulk material. Boussu et al. (2007) assigned this discrepancy to the presence of an additive, polyvinylpyrroli (or solvent dimethylformamide).

Table 1.2 Measured atomic concentration percentages (%) of C1s, O1s, N1s and S2p obtained by X-ray

photoelectron spectroscopy (XPS) for commercially available and experimental membranes. Taken from Boussu et al. (2007).

NF270 Desal51HL Desal5DL NTR7450 N30F NFPES10 D71 N13

C1s 72.0 75.9 72.4 72.1 79.9 77.3 77.0 79.7

O1s 17.0 12.6 17.6 19.2 14.4 16.8 16.3 14.4

N1s 10.9 10.7 9.5 5.2 1.8 2.4 2.6 2.3

S2p 0.1 0.8 0.5 3.5 3.9 3.5 4.1 3.6

The greatest advantage of XPS is the shallow probing depth that is required (i.e., it obtains information about the near membrane surface rather than the membrane interior) and its ability to characterize the elemental composition of the membrane surface. However, chemical identity of the polymer (or additives) cannot be deduced based on elemental composition alone [even if supported by atom type assignments (Boussu et al., 2007)].

ATTENUATED TOTAL REFLECTION FOURIER TRANSFORM INFRARED SPECTROSCOPY (ATR-FTIR)

Attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR) allows for infrared (IR) analysis of surfaces. The IR spectrum can provide determination of vibrational frequencies and transition intensities of most molecules (with the exception of diatomics such as N2 and O2), including characteristic functional group frequencies. Knowledge of vibrational frequencies of functional groups (or reference spectra) allows for chemical identification of at least a class of compounds (e.g., aromatic amides), which may be less apparent in XPS spectra. Example images of ATR-FTIR spectra for a variety of membranes are given in Figure 1.11. The IR radiation typically penetrates 1 μm into the surface. This is a disadvantage because this is deeper than the active layer thickness of most composite membranes, so the top layer is not necessarily isolated (Boussu et al., 2007). The penetration depth can be decreased by careful selection of crystal and incident angle (for Ge ATR element and 3000 cm-1 wavenumber it can be estimated as 200 nm (Müller and Mehnert, 1997) at 45° incident angle). Alternatively, it is possible to perform ATR-FTIR measurements of both sides of an asymmetric membrane and then to essentially subtract out the support layer spectral regions.


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Figure 1.11 ATR-FTIR spectra of several commercial and one lab-made membrane, containing sulfone groups. Ar-SO2-Ar typical frequencies are ν(S=O asym) = 1325 cm-1, ν (S=O symm) = 1140 cm-1. Differences in IR spectra stem from C(CH3)2 bridges present between aryl rings in NTR7450 and Desal 5DL. Taken from Boussu et al. (2007).

Operation of ATR-FTIR is based on the total internal reflection phenomenon. A block of material with greater optical density than the sample is put into close contact with the sample surface. The incident radiation angle must be greater than the critical angle value. Evanescent standing waves penetrating the sample surface are present, though no refracted ray appears due to total internal reflection conditions. The evanescent field decays exponentially into the sample and constitutes specifics of the method. The IR information about the surface is weighted by the exponential decay function – the layers closest to the surface have the strongest representation in the acquired spectrum (Müller and Mehnert, 1997).

Interest in ATR-FTIR for membrane characterization has recently increased, but this technique seems to be underutilized as compared to XPS. Access to the surface vibrational frequencies, as opposed to bulk material, is an advantage of ATR-FTIR, albeit diminished by the somewhat large penetration depth. Vibrational frequencies cannot be easily converted into complete chemical structure of the material, but can provide more molecular structure information than XPS.

OBJECTIVES

The overall objective of this project was to use a comprehensive approach for standardizing select membrane characterization methods and to quantitatively evaluate the combined effects of select membrane properties on membrane performance. The specific goals of this research were to:


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1. Identify and evaluate various available methods for characterizing the physical and chemical characteristics of membrane surfaces,

2. Quantify the variability between data generated using select characterization techniques in order to develop a series of recommended standard methods for characterizing membrane surfaces,

3. Provide drinking water utilities with information on the value of membrane characterization methods, a means to compare membrane properties on an equal basis, and guidance for accurate contact angle measurements for integrity test calculations

4. Evaluate how surface characteristics determined using the proposed standard characterization methods accurately predict membrane performance and fouling.

The focus of this investigation was on pressure-driven membrane processes that are used for municipal drinking water treatment (i.e., MF, UF, NF, and RO). Results and insights from this research are expected to benefit industry, academics, and consultants by facilitating and providing guidance for: comparing published information and data on membrane contact angle, surface roughness, and streaming potential; improving the precision of contact angle, surface roughness, and streaming potential measurements; determining the relation of membrane characteristics to membrane performance; interpreting membrane characteristic information provided by manufacturers; and using contact angle measurements for integrity test calculations as required to receive membrane treatment credits under the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR).


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CHAPTER 2 MATERIALS AND METHODS

MEMBRANE SAMPLES

Membranes from each of the four different membrane categories [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO)] were selected for evaluation in this study (Table 2.1). Membrane selection was based on discussions with partner utilities, as well as on input from other engineering professionals and PAC members. A list of utility installations where the selected membranes are currently in use is included in Appendix A.

Table 2.1 Names and select properties of membranes used in this investigation (MF- microfiltration;

UF- ultrafiltration; NF- nanofiltration; RO- reverse osmosis, PP- polypropylene, PES – polyethersulfone, PA- polyamide, TFC – thin film composite).

Membrane Category Selected Membrane Justification for Selection

MF Siemens Memcor CMF-S hollow fiber PP, outside-in

The CMF membrane series is a very common membrane. It is used by Orange County Water District (OCWD) and the West Basin Municipal Water District, two of the research partners.

UF Norit X-Flow hollow fiber PES, inside-out

Norit X-Flow is also very common. It is used by Minneapolis Water Works, one of the partner utilities.

NF

Dow Filmtec NF-270 spiral wound PA, TFC

The NF-270 and ESNA1-LF membranes are fairly commonly used in NF applications. The ESNA1-LF membrane is used by the City of Hollywood, FL. Hydranautics ESNA1-LF

spiral wound

RO

Hydranautics ESPA-2 spiral wound PA, TFC

The EPSA membrane is a brackish water membrane used by OCWD.

Dow Filmtec SW30HR-380 spiral wound PA, TFC

The SW30 and HR-380 membranes are used at the Tampa Bay seawater desalination facility.

GE Water MUNI-RO-400 spiral wound

The MUNI-RO-400 membrane is used by the City of Clearwater, FL, one of the research partners


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STATISTICAL ANALYSIS

Two statistical measurements were used in evaluating the precision of the characterization data collected in this investigation, the repeatability standard deviation (sr):

2 /= ∑rs s p (2.1)

and the reproducibility standard deviation (sR):

2 2( ) ( ) ( / ( 1))= + −R x rs s s n n (2.2)

where p is the number of separate laboratories from which data were collected, s is the cell standard deviation (standard deviation for results from a single laboratory), sx is the standard deviation of cell averages, and n is the number of measurements. These two measures of precision are described in detail in ASTM E-691-09 ((ASTM), 2009).

MEMBRANE COMMUNITY SURVEY ON MEMBRANE CHARACTERIZATION TECHNIQUES

A questionnaire was sent out to membrane manufacturers, utilities using membrane processes, academics, and membrane scientists in order to gain their input and thoughts on the standard membrane characterization methods developed as part of this project. A copy of the questionnaire that was sent out to all participants may be found in Appendix B. Survey responses were compiled and interpreted to determine and/or obtain the following information:

1. The usefulness of contact angle, surface roughness, and zeta potential data in various membrane applications and fields of study.

2. The level of interest by the membrane community for conducting contact angle, surface roughness, and zeta potential measurements.

3. Acquire feedback on, and suggestions for, improving the proposed standard techniques for conducting contact angle, AFM surface roughness, and streaming potential (zeta potential) measurements.

DEVELOPMENT OF STANDARD TECHNIQUES FOR CHARACTERIZING MEMBRANE SURFACES

Two membranes (ESNA1-LF nanofiltration and SW30HR reverse osmosis) were distributed to the original three partner laboratories: Colorado School of Mines (CSM), Duke University (DU), and Orange County Water District (OCWD) and eventually, to the additional partner laboratories, University of California, Riverside (UCR) and the University of Maribor (FKKT). Each laboratory was asked to conduct specific characterization measurements (contact angle, surface roughness using AFM, and streaming potential) using a set of guidelines provided by UNR. Once the raw data were collected they were returned to UNR for statistical analysis and


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interpretation. Based on this data analysis and input from the partner laboratories, standard methods were developed for each of the characterization measurements (see Appendix C). Once the membrane characterization standard techniques were developed for this subset of membranes, the techniques were then applied to all of the acquired flat sheet membrane samples in Table 2.1.

AFM Surface Roughness Measurements

Data collected by the AFM during imaging can be analyzed to calculate a variety of surface roughness statistics and used to produce 3D rendered images of the membrane surface. Those roughness statistics that are most commonly used to characterize membrane surfaces include average roughness (Ra), root mean square (RMS) roughness (Rq), and surface area difference (Khulbe et al., 2008). Each of these statistical parameters was used to interpret the AFM generated data in this investigation. The RMS roughness, Rq, is a population standard deviation for a set of z values (depth coordinate) collected for a given area, where zi are heights (z-coordinates) from the AFM scan, N is number of data points (i = 1, 2, …, N), and i is the data index.

2( )i avgq

z zR

N−

= ∑ (2.3)

The average surface roughness, Ra, is calculated using averaged absolute values of deviations from the average surface plane:

| |−= ∑ i avg

a

z zR

N (2.4)

Surface roughness data from the DU and OCWD laboratories on two membranes (ESNA1-LF and SW380HR membranes) were acquired and used to estimate inter-laboratory precision. In addition to the previously mentioned membranes, the OCWD laboratory also analyzed three other flat sheet membranes (NF-270, ESPA, MUNIRO-400). Results from OCWD for all five flat sheet membrane samples were used for developing correlations between membrane surface properties and performance.

In addition to the membrane samples, standard surfaces (Table 2.2) were sent to each of the participating laboratories for analysis. The standard surfaces were used for characterizing the surface roughness of a material with a well-defined surface morphology. Flat, concave (to represent the inside of a hollow fiber membrane) and convex (to represent the outside of a hollow fiber membrane) standards were selected. While the flat standard surface (i.e., the z-axis calibration standard) is not strictly a roughness standard, it does allow for calibrating the AFM instrument in the z-direction. It was assumed that calibration in the x and y directions (i.e., the sample plane) were not necessary. The z-axis standard was intended to help correct for instrument drift in the direction that is most critical for calculating roughness (i.e., the z-direction).


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Table 2.2 Proposed standard surfaces for atomic force microscopy surface roughness measurements. Measurement \ Sample

A Flat

B Concave

C Convex

AFM Roughness

HS-20MG nanostructure array (20 nm step height) for z-axis calibration from budgetsensors.com, or similar grid.

Teflon tubing ~1 mm outer diameter


Contact Angle Measurements

This study involved developing standard methods for measuring the contact angle with water on both flat-sheet and hollow fiber membranes. To accomplish this it was necessary to identify and evaluate candidate standard surfaces for three separate conditions: flat surfaces, concave surfaces, and convex surfaces. These three surfaces correspond to a flat membrane, the outside of a hollow fiber membrane, and the inside of a hollow fiber membrane, respectively. Those surfaces that were considered for each of these conditions are summarized in Table 2.3.

A similar approach to that which was previously described for the AFM measurements was followed for developing the standard technique for contact angle measurements. This approach involved distributing standard surface and membrane samples (the Teflon standard surface, ESNA1-LF membrane, and SW30HR membrane) to DU and CSM. Contact angle measurements were also performed at the UNR laboratory. All laboratories were provided with guidelines for conducting the contact angle measurements (see Appendix C). However, variations existed in the type of goniometer used by each laboratory for measuring contact angle, and other laboratory techniques, amongst the three laboratories. Both the DU and CSM laboratories were only responsible for conducting contact angle measurements on flat-sheet membrane samples. Therefore, they were not provided samples of the concave and convex standard surfaces. Developing a standard method for measuring contact angles on hollow fiber membranes was the sole responsibility of the UNR laboratory.

Table 2.3 Proposed standard surfaces for contact angle measurements.

Measurement \ Sample

A Flat

B Concave

C Convex

Contact Angle Teflon flat sheet from Ramé-Hart with contact angle range and material origin certificate.

Cleaned glass capillary painted black on the outside

Glass capillary covered with PMMA on the outside

PMMA – poly(methyl methacrylate)


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Streaming Potential Measurements

An approach similar to that which was previously described for the surface roughness and contact angle measurements was followed for developing the standard technique for streaming potential measurements in order to calculate zeta potential. This approach involved distributing three different sample surfaces to the DU and CSM laboratories and having them measure streaming potential as a function of solution pH and ionic strength. The three surfaces that were distributed included a PMMA standard surface, the ESNA1-LF membrane, and the SW30HR membrane. Streaming potential measurements were also performed at the UNR laboratory. All laboratories were provided with guidelines for conducting the streaming potential measurements (see Appendix C). However, variations existed in the type of electrokinetic analyzer used by each laboratory for measuring streaming potential, and other laboratory techniques, amongst the three laboratories.

After a first round of testing, and interpretation of the subsequent streaming potential results, it was determined that a number of equipment and procedural errors resulted in erroneous results. Following discussions with the partner laboratories and representatives from Anton Paar (manufacturer of the SurPASS electrokinetic analyzer) it was determined that the streaming potential measurements should be repeated. During this second round of testing the membrane samples were distributed to and analyzed by the following parties: Anton Paar, the University of Maribor (FKKT), the University of California, Riverside (UCR), and UNR. Only UNR and Anton Paar provided results for the PMMA standard surface. Both DU and CSM did not conduct measurements for the second round of streaming potential measurements. For this round robin of tests the following streaming potential analyzers were used:

• University of California, Riverside (UCR) – Electro Kinetic Analyzer (EKA) from Anton Parr (Ashland, VA)

• University of Nevada, Reno (UNR) – ZetaCAD from Colloidal Dynamics (Ponte Vedra Beach, FL)

• University of Maribor (FKKT) and Anton Paar – SurPASS Electrokinetic Analyzer from Anton Parr (Ashland, VA)

ASSESSMENT OF MEMBRANE PERFORMANCE AND FOULING

Membrane characteristics are often used to estimate or predict membrane performance, and specifically, the occurrence or severity of membrane fouling. A standard bench-scale membrane test unit was used to measure the performance of the membranes previously characterized in this study. A process flow diagram for the standard membrane test unit is given in Figure 2.1. Process components and their associated properties are summarized in


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Table 2.4. Originally, membrane fouling experiments were planned to be performed at both UNR and at a Clearwater, FL desalination facility. However, testing using the actual RO feed from the Clearwater site resulted in no significant permeate flux decline after 5 days of testing. This result was independent of the membrane being evaluated. Therefore, testing using the RO feed from the Clearwater plant was discontinued.

Figure 2.1. Process flow diagram of the bench-scale membrane test system used in the correlation experiments for membrane surface properties and perfromance (P – pressure gauge, T – temperature probe, F – flow meter).


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Table 2.4 Summary of primary system components and accessories for the standard membrane test

unit. Item Quantity Notes

120 Volt Water Pumps 2

One pump supplies feedwater to two test cells (flow is split between two test cells) Fluid-O-Tech Model 311, Stainless steel vane pumps, 101 GPH at 200 psi, 120 VAC. Pumps are equipped with on/off switches on power cords.

Membrane Test Cells 4

Stainless steel cells that house flat-sheet membrane samples operating in tangential flow configuration Each cell consists of two plates, a Teflon gasket, 10 bolts, and 10 washers Each cell is labeled using a number from 7 to 10 Feed channel dimensions (determined by Teflon washer): 146 mm × 95 mm × 0.8 mm. Membrane active area (determined by stainless steel frit area): 127 mm ×81 mm

Tubing 17 sections +

1 spare section

Feed water pressure tubing (reservoir to pump, pump to cell, flow meter to waste): Polyethylene 3/8’’ outer diameter, total length ~ 36 ft + spare. Fittings: stainless steel Swagelok for cell inlet (shipped on tubing), custom flow meter black plastic fittings (present on tubing), quick connect on pumps. Permeate recycling: polyethylene (1/4’’), total length ~20 ft, plastic compression fittings (shipped on tubing) Permeate collection: Black rubber tubing (4x~1 ft) inserted on stainless steel adapter of each valve. Spare tubing (3/8’’) is also supplied in the event that it is required

Test Cell Inlet Assembly 4

Inlet assembly consists of a pressure gauge and a needle valve Fittings included in the inlet assembly are 3/8’’ stainless steel compression (Swagelok)

Test Cell Outlet Assembly 4

Outlet assembly consists of a needle valve and a flowmeter Fittings included in the outlet assembly are 3/8’’ stainless steel Swagelok compression fittings (cell- assembly) and plastic custom compression fitting


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for flow meter – 3/8’’tubing (shipped on tubing)

Thermocouple 4

To be installed in each cell inlet channel – brass 3/8’’ NPT fitting, thermocouple installed inside needle with epoxy resin connecting to male brass fitting

Digital Thermometer 2 Calibrated to be used with thermocouples supplied

Templates 2

One template for cutting the membrane samples to the correct dimensions for the test cells One template for cutting the membrane feed spacer mesh to the correct dimensions for the test cells

The chemistry and composition of the feed solution used in all membrane fouling experiments is summarized in Table 2.5.

Table 2.5 Chemistry and composition of the feed solution used for membrane fouling experiments.

Parameter Concentration mg/L

Sodium Silicate (Na2SiO3·9H2O) 118.3

Calcium chloride (CaCl2·2H2O) 190.9

Potassium chloride (KCl) 142.8

Sodium carbonate (Na2CO3) 140.0

Sodium sulfate (Na2SO4) 239.4

Starch1 4

Hydrochloric acid (HCl)

Bentonite clay 200 1 – Starch was added in order to adjust the total organic carbon (TOC) content of the feed solution. Temperature Correction Factors for Normalizing Membrane Permeate Flux

Temperature correction factors (TCFs) are needed to compare permeate flux values measured at different temperatures. For example, a temperature increase of 1 °C will result in a permeate flux increase of approximately 3% (with all other factors, especially pressure, remaining the same). In order to standardize the permeate flux values acquired as part of these experiments the measured permeate fluxes were corrected and reported at a temperature of 25°C using the following expression:

(2.5)


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where JT is the permeate flux measured at some temperature (T), TCF is the temperature correction factor, and J25 is the permeate flux at a temperature of 25 °C. Using an Arrhenius-type functional dependence for water viscosity on temperature, one could expect the following dependence for the inverse of the pure water permeate flux at a constant feed pressure:

(2.6)

where T is the temperature (°C), E is the viscosity activation energy, and R is the universal gas constant. Letting U = E/R, then Eq. 7 may be linearized, if the inverse of T is taken as an independent variable:

(2.7)

The values of lnA (and A) and U can be determined using linear regression for ln(q) vs the inverse of absolute temperature. The resulting TCF should have the following form:

(2.8)

The regression parameters (A, U) were determined by measuring the pure water permeate flux as a function of feed pressure (3 to 4 runs, each consisting of 4-5 data points) for each membrane. The regression parameters were then averaged and used to calculate the TCFs for each membrane (Table 2.5).


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Table 2.6 Summarized temperature correction factors (TCFs) for the NF-270, ESNA1-LF, ESPA-2,

SW30HR, and MUNIRO-400 membranes, which were used in the membrane fouling experiments.

T [OC] NF270 ESNA1-LF ESPA-2 SW30HR MUNIRO-400 Dow Brochure1

15 1.459 1.384 1.417 1.512 1.408 1.422 16 1.403 1.339 1.367 1.449 1.359 1.371 17 1.350 1.295 1.319 1.389 1.312 1.323 18 1.299 1.253 1.273 1.332 1.267 1.276 19 1.251 1.212 1.229 1.277 1.224 1.232 20 1.204 1.173 1.187 1.225 1.183 1.189 21 1.160 1.136 1.146 1.176 1.143 1.148 22 1.117 1.100 1.108 1.129 1.105 1.109 23 1.076 1.065 1.070 1.084 1.069 1.071 24 1.037 1.032 1.034 1.041 1.034 1.035 25 1.000 1.000 1.000 1.000 1.000 1.000 26 0.964 0.969 0.967 0.961 0.968 0.971 27 0.930 0.939 0.935 0.924 0.936 0.943 28 0.897 0.911 0.905 0.888 0.906 0.915 29 0.866 0.883 0.875 0.854 0.878 0.889

29.9 0.839 0.859 0.850 0.825 0.853 0.866

A [min/mL] 1.86E-06 6.43E-06 7.01E-06 9.74E-06 1.03E-05

U [K] 3246 2794 2996 3552 2938

RMSE2

0.020 0.017 0.0066 0.044 0.0083

SU [K] 560 63 124 346 171 1 - Dow, Form No. 609-02129-804, Tech Manual Excerpt, FILMTEC™ Membranes, Addendum:

Temperature Correction Factor. 2 - RMSE: root mean square error with respect to Dow data: RMS=[(1/N)·Sum(Dow value –

predicted)2]1/2

A value for U is needed to determine the TCFA, and is called the “membrane specific manufacturer-supplied constant” (EPA Membrane Filtration Guidance Manual, EPA 815-R-06-009, 2005). A best fit to the data that was supplied by Dow Filmtec gave a value for U of 2,982 K. The experimental standard deviation, SU, is the difference between the Dow Filmtec value (U = 2,982 K) and that obtained experimentally for any given membrane. Establishing more precise U values for each membrane would require more data points. Nevertheless the experimental values of U for TCF can be used:

(2.9)


28

Alternatively, substituting the universal expression with UM = 2,982 K, which is a convenient formula to substitute for Dow tabulated data, would be also acceptable. In this study, the universal correction was applied.

Membrane Performance Testing

Membranes were installed with mesh feed spacers, as some membranes were susceptible to damage without spacers. After compacting membranes with DI water overnight at 690 kPa, tests were run using a bentonite suspension as the feed source (see Table 2.5). Permeate flow was monitored at 690 kPa for a short period of time (several minutes), cell pressure was then adjusted to maintain an initial (starting) permeate flow rate of 4 mL min-1. Once the feed solution was switched to the bentonite suspension the system was operated in a constant pressure, variable flux configuration. The feed flow rate was maintained at 379 mL min-1. Temperature was recorded at the beginning and at the end of permeate flow measurement. The average water temperature was used for calculating the TCF for each system. Water temperature was maintained between 22 and 25°C. The SW30HR membrane was an exception to the 4 mL min-1 target flow, as a sufficient feed pressure could not be attained due to low permeability of this membrane. The normalized permeate flow as function of time for each membrane evaluated here is reported in Figure 2.2.


29

Figure 2.2. Normalized permeate flow (25°C, flow at t = 0 set to 1) for membranes tested as a function of time (hours).

Overview of Membrane Performance Test Method The experimental setup described above was chosen for its simplicity; however, it is important to note that since the membranes were operated at constant pressure as opposed to constant water flux, and all had differing initial permeability, the mass transport of foulant to each membrane differed significantly as a function of time, making it impossible to use the relationship between observed water flux and run time as a means of assessing membrane performance.

Normalizing Membrane Performance Response

In order to compare the influence of processing a constant mass of foulant at each membrane’s water flux, the mass of clay that was carried to the membrane surface was first determined based on the permeate passing the membrane. Regression analysis using Statgraphics Centurion XV (Statpoint Inc., Herndon, VA, USA) software was used for each membrane to establish the relationship between the total amount of permeate volume recovered and the resulting membrane performance (as normalized water flux, J/Jo). Permeate volume was then converted to bentonite clay loaded per square meter of membrane surface based on the concentration of bentonite clay in experimental suspensions (200 mg L-1) and the active area of


30

membrane swatches (0.0103 m2). From these models, normalized membrane fluxes at exact bentonite clay loads of 2.5, 5, 10, and 20 g m-2 were determined for each membrane. Plotting these results for each membrane revealed that initially normalized water flux decreased with clay load in a more or less linear fashion, but that as clay continued to be loaded, water flux seemed to reach a plateau. A bentonite mass loading value used to normalize membrane performance data was chosen from the region of the curves where 1) the relationship between normalized water flux and bentonite loading appeared most linear and 2) was within the range of measured experimental data for all five membrane experiments.

Once this was accomplished, the intrinsic water flux predicted by the simple regression models at that chosen constant clay load was used as the dependent variable in a multiple linear regression (MLR) study relating J/Jo to membrane contact angle, zeta potential, roughness, and initial flux. Each membrane type provided an exemplar to construct this MLR model.


31

CHAPTER 3 RESULTS AND DISCUSSION

METHOD DEVELOPMENT FOR AFM SURFACE ROUGHNESS MEASUREMENTS Imaging and Roughness Analysis of Flat Membrane Surfaces ESNA1-LF Membrane

Surface roughness statistics for the ESNA1-LF membrane are summarized in Table 3.1. Representative 3-D rendered AFM images of the ESNA1-LF membrane surface are shown in Figure 3.1. Relative standard deviations for repeated measurements at different locations on the membrane are approximately 15% for the OCWD results, and 30% for the DU results. Results overlap within 95% confidence limits, but only as a result of the large spread in the DU data set. There is a stark difference in the surface roughness statistics Rq and Ra that were generated for the same membrane by the two laboratories. Considering both parameters, the data from OCWD suggests a smoother membrane surface than that from DU. The source for this disagreement in the data is unclear. Both laboratories analyzed membrane coupons from the same sample and used the same measurement mode for the AFM (i.e., tapping mode). A noticeable trend in the DU data is that both surface roughness statistics varied considerably from one sample site to the next (ΔRq ~ 15 to 42 nm). In contrast, the variability from one site to another for the OCWD data was less substantial (ΔRq ~ 10 nm). The small data set precludes the development of hard conclusions for this single membrane; however, there are several possibilities for explaining the observed large variation in the DU surface roughness data, including: i) the sites selected for analysis by DU were more heterogeneous than those selected for analysis by OCWD, ii) some unknown experimental protocol used by DU resulted in greater variation in the roughness data, and iii) the sample analyzed by DU became partially contaminated, resulting in a higher surface roughness being measured at two of the three sites that were analyzed. Speculation aside, it is clear that collecting data from multiple sites (> 3) on the membrane surface is necessary in order to account for the natural variation in surface morphology between surface sites.

In Figure 3.1, it is difficult to compare the rendered images of the ESNA1-LF membrane from the two laboratories because the images were developed using different z-scales. This observation brings an important conclusion for developing a standard technique for comparing AFM-generated images for membranes, which is that both images must utilize the same x-, y-, and z-scales in order to facilitate easier visual comparison. That said, the roughness statistics (Rq, Ra) should be relied on much more than visual comparison.

One reason for the differences in AFM data from the two laboratories could be differences in AFM tips used. It is known that probe tip diameter and composition profoundly influence the AFM image x/y resolution. The OCWD laboratory used a silicon nitride MSNC-MT-B tip (nominal tip radius 10 nm, maximum tip radius 40 nm; Bruker AFM Probes, Camarillo, CA) and the DU team used a silicon nitride ORC8-10 tip (nominal tip radius 15 nm, maximum tip radius 20 nm; Bruker AFM Probes, Camarillo, CA). While the tips are made of the


32

same material and their geometric features are similar, they are not identical, particularly when the variability in tip radius (nominal vs. maximum value) is considered.

Table 3.1 Surface roughness statistics generated from atomic force microscope (AFM) imaging of the ESNA1-LF membrane surface by the OCWD and DU laboratories (resolution = 256 × 256, measurement mode = tapping, medium = water). Numerals (1, 2, ..) correspond to a sample

site on the membrane.

Laboratory / Roughness Statistic 1 2 3 4 5 6 Avg.

[nm]

Stan. Dev. [nm]

OCWD Rq [nm] 35.6 37.5 33.7 30.3 36.1 46.1 36.6 5.3 Ra [nm] 28.2 29.7 26.4 23.7 28.3 34.9 28.5 3.7 Size [μm] 5 × 5 5 × 5 5 × 5 5 × 5 10 × 10 10 × 10

DU Rq [nm] 96.0 80.9 53.7 76.9 21.4 Ra [nm] 73.8 63.7 41.1 59.5 16.7 Size [μm] 10 × 10 10 × 10 10 × 10

(a)

(b)

Figure 3.1. Rendered 3D images of the ESNA1-LF membrane based on atomic force microscope (AFM) data from (a) OCWD and (b) DU (scan size = 100 μm2, measurement mode = tapping, medium = water).

SW30HR Membrane

Surface roughness statistics for the SW30HR membrane are summarized in Table 3.2. Representative 3D rendered AFM images of the SW30HR membrane surface are given in Figure 3.2. Overall, the SW30HR membrane may be characterized as a relatively rough membrane based on the data from both laboratories. This observation is supported by the 3D rendered


33

image of the membrane surface (Figure 3.2a). The agreement between the roughness statistics for the SW30HR membrane was considerably better than that for the ESNA1LF membrane. Relative standard deviations are approximately 8% and 5% for Rq and Ra by the DU and OCWD laboratories, respectively.

Table 3.2 Surface roughness statistics generated from atomic force microscope (AFM) measurements of the SW30HR membrane surface from the OCWD and DU laboratories (resolution = 256 × 256, measurement mode = tapping, medium = water). Numerals (1, 2, ..) correspond to a

sample site on the membrane.

Laboratory / Roughness Statistic 1 2 3 4 5 Avg.

[nm]

Stan. Dev. [nm]

95% Conf.

OCWD Rq [nm] 86.8 89.6 91.8 77.0 77.5 84.5 6.9 8.5 Ra [nm] 69.4 71.6 72.4 61.3 61.6 67.3 5.4 6.8 Size [μm] 10 × 10 10 × 10 10 × 10 5 × 5 5 × 5

DU Rq [nm] 87.1 87.7 95.1 90.0 4.5 12 Ra [nm] 68.5 68.3 75.9 70.9 4.3 11 Size [μm] 10 × 10 10 × 10 10 × 10

(a)

(b)

Figure 3.2. Rendered 3D images of the SW30HR membrane based on atomic force microscopy data from (a) OCWD and (b) DU (scan size = 100 μm2, measurement mode = tapping, medium = air). Note that the z-scale is different between the rendered 3D images (0.35 μm/division for Figure 3.2a compared to 0.305 μm/division for Figure 3.2b).

Repeatability and Reproducibility

The surface roughness statistics for the ESNA1-LF and SW30HR membranes that were measured by the OCWD and DU laboratories were further analyzed to assess the precision of the measurements between the two laboratories (i.e., an inter-laboratory comparison). Precision


34

statistics for both Rq and Ra are summarized in Table 3.3. Despite differences in average values determined between the two laboratories for the SW30 membrane, the repeatability and reproducibility of the two roughness statistics are reasonably good. The higher repeatability (sr) and reproducibility (sR) standard deviations for the ESNA1-LF membrane may be attributed due to greater surface heterogeneity of this membrane. Interestingly, however the repeatability and reproducibility of the statistics was better for the rougher membrane surface.

Table 3.3 Precision statistics for the surface roughness data that was collected for the ESNA1-LF and SW30HR membranes (cell – test results from one laboratory on one material, repeatability

standard deviation (sr), reproducibility standard deviation (sR). Rq Roughness Precision Data

Material Average of Cell Averages, [nm]

Repeatability (sr) [nm]

Reproducibility (sR) [nm]

ESNA1-LF 56.7 16 31 SW30HR 86.0 5.8 5.2 Ra Roughness Precision Data

Material Average of Cell Averages, [nm]

Repeatability [nm]

Reproducibility [nm]

ESNA1-LF 44.0 12 24 SW30HR 69.1 4.9 2.6 Surface roughness statistics for all five of the membranes that were analyzed by OCWD are summarized in Table 3.4. Representative 2D images of the five membranes are shown in Figure 3.3. Results are discussed here for each of the five membranes in order to provide a basis for analyzing the membrane fouling results that are presented later in this report.

Table 3.4 Surface roughness statistics as measured using an atomic force microscope (AFM) for the

NF-270, ESNA1-LF, ESPA-2, and SW30HR membranes. All measurements were performed on flat sheet membrane samples by the Orange County Water District (OCWD) laboratory. All measurements were performed on wet membrane samples in contact mode

and using a liquid cell. No. of

Scans Rq

[nm] St Dev [nm]

95% conf. [nm]

Ra [nm]

St Dev [nm]

95% conf. [nm]

NF-270 8 3.8 1.9 1.8 2.5 0.9 0.8 ESNA1-LF 6 36.6 5.3 5.6 28.5 3.7 3.9 ESPA-2 9 38.8 12.9 11.1 30.8 10.3 8.8 SW30HR 5 84.5 6.9 8.5 67.3 5.4 6.8 MUNIRO-400 7 40.7 4.6 4.5 31.8 3.1 3.0


35

ESNA1-LF

SW30HR

NF-270

ESPA-2

MUNIRO-400

Figure 3.3. Rendered 2D atomic force microscope (AFM) images of the ESNA1-LF, SW30HR, NF-270, ESPA-2, and MUNIRO-400 membranes. All images were generated by the AFM OCWD laboratory.

Of the five membranes analyzed by OCWD, the NF-270 membrane was characterized as having the smoothest surface. Both the Rq and Ra for the NF-270 membrane were smaller by an order of magnitude than those for the four other membranes. This observation is further supported by the 2D image of the NF-270 surface, which lacks the grain structures that are seen in the images for the other membrane surfaces. The SW30HR membrane was determined to have the roughest surface, with the remaining membranes having similar Rq values of approximately 30 nm. The standard deviation in the values for Rq and Ra for all of the membranes was relatively low (< 9 nm), with the exception of that for the ESPA-2 membrane.

The Ra value for all of the membranes was always smaller than the Rq values. Plotting Ra as a function of Rq revealed that the relationship between these two parameters is linear (Figure 3.4). A possible explanation for this relationship is that distributions of populations of zi coordinates that describe membranes exhibit some mathematical regularity that is not dependent on the range of the z variable. Also, this indicates that either value could be used for correlation of membrane properties with membrane performance.


36

Figure 3.4. Plot of Ra as a function of Rq for the membranes investigated. Imaging and Roughness Analysis of Curved (Hollow Fiber) Membrane Surfaces Teflon (PTFE) Standard Surface

Surface roughness statistics for the polytetrafluoroethylene (PTFE), or Teflon, tubing, which was investigated for its suitability as a standard surface for AFM imaging of hollow fiber membranes, are reported in Table 3.5. The OCWD laboratory imaged only the outside (convex portion) of the Teflon tubing, while the DU laboratory imaged both the inside (concave portion) and outside of the Teflon tubing. It is important to note that both laboratories analyzed the same Teflon tubing. When seeking out a standard flat surface for the AFM surface roughness measurements Teflon was considered to be an ideal surface as a result of its various characteristics. The nature of the carbon-fluorine bond makes Teflon a non-stick (low adhesion) material, which would facilitate more intimate characterization of surface features without interference from interfacial chemical interactions.

The two laboratories produced dramatically different roughness statistics for the outside of the Teflon tubing, with the data from OCWD suggesting a smooth surface while that from DU indicated a rough surface. Of note is the substantial standard deviation in the results from DU for the outside of the Teflon tubing (Table 3.5), which suggests some error in the measurement. The source of this disagreement is unclear; however, one would expect the Teflon surface to in fact be smooth as suggested by the OCWD data. For this reason, the differences in measurement modes used by the two laboratories should be considered. OCWD utilized contact mode, while


37

DU used tapping mode. The need for contact mode at the OCWD laboratory was attributed to the small size of the AFM tip (10 nm radius) and the weak interactions that occurred between the tip and the Teflon surface. The weak interactions between the tip and surface resulted in the tip crashing into the surface on approach. This problem could be resolved in the future by using AFM tips with larger tip diameters, which would result in an increase in the overall force that is detected by the tip on approach to the sample surface (interaction force scales with the size of the AFM tip). In the absence of larger tips, it may also be possible that tapping mode is not suitable for imaging curved surfaces like hollow fiber membranes. This point will however require further research to determine the ability of either mode to accurately analyze curved surfaces. DU roughness statistics for the inside of the tubing indicate that it is smoother than the outside. If true (no comparative data from OCWD was available), then it will be important to consider the flow orientation for hollow fiber membranes (outside-in or inside-out) when selecting which surface to image. This is an important note as even for a symmetric material such as the Teflon tubing studied here, significant differences in the surface morphology from the outside to the inside were observed.

Inspection of 2D rendered AFM images from the OCWD laboratory (data not shown) found that there was a great variability in the morphology of the Teflon surface. This was attributed to extrusion nozzle imperfection during Teflon manufacture. This observation, coupled with the substantial inter-laboratory discrepancies, suggests that additional testing is required to demonstrate that Teflon tubing is a suitable standard surface for the imaging of hollow fiber membranes.

Table 3.5

Surface roughness statistics for Teflon tubing, which was being considered as a standard surface for analyzing hollow fiber membranes using atomic force microscopy (resolution =

256 × 256, medium = water, OCWD – contact mode, DU – tapping mode).

Sample Site # / Laboratory 1 2 3 4 5 6 Avg.

[nm]

Stan. Dev. [nm]

95% Conf. [nm]

OCWD-outside Rq [nm] 22.0 9.9 12.5 18.1 24.7 28.6 19.3 7.2 7.6

Ra [nm] 15.5 7.3 9.7 13.4 16.3 20.8 13.8 4.8 5.1

Size [μm] 5×5 5×5 5×5 5×5 10×10 10×10

DU – outside Rq [nm] 64.8 166.0 115.4 71.6 643

Ra [nm] 50.9 126.0 88.5 53.1 477

Size [μm] 10×10 10×10

DU – inside Rq [nm] 34.4 79.7 34.0 49.4 26.3 66

Ra [nm] 25.6 66.8 26.5 39.6 23.5 59

Size [μm] 10×10 10×10 10×10


38

Standard Method for AFM Surface Roughness Measurements on Flat-Sheet and Hollow Fiber Membranes

Based on advice received from Dr. K. Khulbe (an AFM expert from the University of Ottawa), feedback from participants (particularly OCWD), and information from Agilent experts, a standard technique for conducting AFM surface roughness measurements on flat-sheet and hollow fiber membranes was developed and is presented in Appendix C.

METHOD DEVELOPMENT FOR CONTACT ANGLE MEASUREMENTS

A flat-sheet Teflon surface was determined to be an appropriate standard surface for contact angle measurements (as will be shown in subsequent sections of this report). This conclusion was based on the consistency in the data that were acquired from each of the participating laboratories (excluding one outlier, likely resulting from operator error). However, it should be noted that the contact angle value for which the Teflon is certified is the advancing sessile drop value and the value recommended for use in the standard technique is the receding captive bubble value; thus re-certification of the Teflon for the receding value is required. For the curved standard surface it is recommended that the Teflon tubing be pursued, as the PMMA film that was investigated here was unstable when exposed to water.

Contact Angle Analysis of the Flat-Sheet Teflon Standard Surface

The contact angle for the Teflon standard surface is reported by the manufacturer to range between 102.7° and 105.9°. Contact angle data for the Teflon standard surface measured by the participating laboratories are summarized in Table 3.6 and Figure 3.5. The standard deviation boxes for each laboratory represent the standard deviation of the data collected at each respective laboratory and are a measure of the internal precision for each participant and sample. The confidence intervals at 95% confidence (barring systematic error, there is a 0.95 probability that the true average is within these intervals) overlap with standard deviations within 4% for a given sample size (6 measurements); thus, 99% confidence intervals are shown in the right panel of Figure 3.5. The contact angle values from DU and UNR are lower than the reported certified values for Teflon but are consistent with a hydrophobic surface. The deviation from the certified values may be attributed to the fact that receding contact angles were measured at both DU and UNR, while the manufacturer reported the advancing contact angle. However, because Teflon is strongly hydrophobic, and thus should not ‘wet out’, surface hydration would not appear to be a complete explanation for the observed difference in contact angle results. In contrast to the DU and UNR results, those from CSM were indicative of a hydrophilic surface (θ << 90°). The source for such a low contact angle with water for a known hydrophobic material is unknown, though surface contamination was ruled out as a possibility.


39

Table 3.6 Contact angle data for the Teflon standard surface from three the participating

laboratories (Colorado School of Mines - CSM, Duke University – DU, and the University of Nevada, Reno – UNR). All contact angles were measured using the captive bubble

method and doubly deionized water as the contact angle probe liquid (T = 20°C). Lab / Air Bubble # 1 2 3 4 5 6

CSM 44.8 49.3 46.5 54.3 56.6 56.3

DU 95.2 91.7 95.7 90.5 92.7 87.0

UNR 100.0 100.3 100.3 98.4 100.6 98.2

Figure 3.5. Box and whiskers plot for Teflon contact angle data: central bar – mean, outside bars –minimum/maximum, boxes – standard deviation range or confidence interval. The 95% confidence interval for 6 measurements is very close to the sample standard deviation span, so 99% confidence intervals were plotted on the right. CSM – Colorado School of Mines, DU – Duke University, UNR – University of Nevada, Reno. All contact angles were measured using the captive bubble method and doubly deionized water as the contact angle probe liquid (T = 20°C).

Statistical analyses were conducted on the contact angle data from the three laboratories to determine if both DU and UNR determined the same average contact angle value for the


40

Teflon standard. Excluding the contact angle data from CSM the P-value for the ANOVA analysis is 6.44×10-5. Because the P-value is less than a significance level of 0.05, the hypothesis that the DU and UNR results are in agreement is rejected. Therefore, while both the DU and UNR laboratories produced similar results, they are in fact statistically different. Thus, three separate laboratories produced statistically different average contact angle values for the Teflon standard surface. The source of this disagreement is unclear but may result from physical deformation of the Teflon material (e.g., the angle between the camera axis and the sample plane is perhaps different than zero as shown in Figure 3.6). In Figure 3.7 the tear-drop shape shows two contact angles: a larger (advancing) angle in the front toward the sliding direction, and a smaller (receding) angle in the back. The advancing angle is consistent with the value reported by the manufacturer (102.7° to 105.9°). Conversely, the receding contact angle (θ = 90°) is consistent with the contact angle results determined using the captive bubble method, which measures the receding angle (Table 3.6). Surface contamination is a less likely scenario as the Teflon surface was cleaned prior to the measurements and because it is a homogeneous material, thus, impacts associated with surface chemical heterogeneities should be minimal. It is therefore most plausible that differences in instrumentation and procedural protocols between the laboratories were the most significant contributors to the observed variations in the reported contact angle values. The substantial difference between those results from DU and UNR with that from CSM, in addition to the difference between the results from CSM and those in the literature for Teflon, suggest that the CSM results likely suffer from some significant procedural error (e.g., mistaken sample identity).

CSM DU UNR

Figure 3.6. Digital images of air bubbles on the Teflon standard surface from each of the three participating laboratories (Colorado School of Mines – CSM, Duke University – DU, and the University of Nevada, Reno – UNR). In each case the contact angle was measured using the captive bubble technique in which the Teflon surface was immersed in doubly deionized water (T = 20°C).


41

Figure 3.7. Digital image of a water droplet (Vdrop = 20 μL) on a Teflon standard surface set at an incline.

Contact Angle Analysis of Flat Sheet Membrane Surfaces Hydranautics ESNA1-LF Membrane

Contact angle data collected from the participating laboratories for the ESNA1-LF membrane are summarized in Table 3.7 and Figure 3.8. Additionally, representative captive bubble images for the ESNA1-LF membrane from each laboratory are shown in Figure 11. Both CSM and UNR produced average contact angle values for the ESNA1-LF membrane that were statistically the same (P-value 0.14 > 0.05), while DU generated data that was statistically different (P-value 1.1×10-5). Considering the entire contact angle data set, the membrane may be characterized as being hydrophilic, as indicated by the relatively low contact angle with water that was measured at each laboratory. Therefore, while the specific contact angle value differs amongst the three laboratories the overall qualitative outcome does not. The standard deviation for the contact angle data was rather large (> 5°), with the exception of that for the CSM data set (1.5°). Because each laboratory studied the same membrane sample it is possible to eliminate chemical heterogeneity of the membrane surface as the source of the large standard deviation in the contact angle data from DU and UNR. Thus, the deviation in the data may be attributed to contamination of the membrane surface, instrument error, and/or procedural error (insufficient conditioning, improper sample storage). Of these possibilities, the latter two are most likely as each laboratory was instructed to handle the membrane samples with powder-free gloves and to clean them with deionized water.

Table 3.7 Contact angle data for the ESNA1-LF membrane from the three participating laboratories

(Colorado School of Mines - CSM, Duke University – DU, and the University of Nevada, Reno – UNR). All contact angles were measured using the captive bubble method and

doubly deionized water as the contact angle probe liquid (T = 20°C). Lab/bubble 1 2 3 4 5 6 Avg.

CSM 25.1 23.9 22.4 23.1 22.6 20.8 23.0

DU 34.1 41.2 49.3 40.8 36.1 44.2 41.0

UNR 26.0 25.9 35.5 27.5 19.0 25.4 25.4


42

Figure 3.8. Box and whiskers plot for the ESNA1-LF contact angle data, which was measured using the captive bubble technique. The 95% confidence interval for 6 measurements is very close to the sample standard deviation span, so 99% confidence intervals were plotted on the right. CSM – Colorado School of Mines, DU – Duke University, UNR – University of Nevada, Reno.

Inspection of the representative captive bubble images from each laboratory (Figure 3.9) suggests that inclination of the sample stage and the use of non-optimal lighting may have contributed to discrepancies in the contact angle data. The lack of sufficient sample lighting is evident in the image from DU. Inclination of the sample at both CSM and DU is indicated by the lack of a clear interface between the membrane surface and the water. Both of these specific error sources are of a critical nature due to the dependence of the bubble shape, and thus, the contact angle on the sample orientation and interpretation of the three-phase interface by the digital software. Any deviation of the sample from a planar orientation will result in errors in the measured contact angle, with the magnitude of the error increasing the farther the sample is from planar orientation. Mitigating these sample preparation and procedural errors would likely reduce the internal standard deviation in the data as well as the global deviation in the entire data set. It is interesting to note here that visual observations of the contact angle measurements are key to identifying possible errors and discrepancies in the results.


43

CSM DU UNR

Figure 3.9. Representative digital pictures of air bubbles from which contact angles were measured on the ESNA1-LF membrane at each of the three participating laboratories (Colorado School of Mines - CSM, Duke University – DU, and the University of Nevada, Reno – UNR).

Dow Filmtec SW30HR

Contact angle data from the participating laboratories for the SW30HR membrane (Dow Filmtec) are summarized in Table 3.8 and Figure 3.10. Overall, the contact angle data indicate that the SW30HR membrane is hydrophilic, with reasonably good qualitative agreement between each of the three data sets. Both DU and UNR produced statistically similar results (P-value = 0.64 > 0.05), while that from CSM was different (P-value = 8.8×10-4 < 0.05). Inspection of representative digital images of the air bubbles from the three laboratories (Figure 3.11) suggests that the instrument’s camera at CSM may have been placed in a way that truncates the bubble base, which resulted in the measured contact angle being larger than the actual value. With the exception of the data set from UNR (STDEV > 5°), the standard deviation amongst the contact angle data from any given laboratory was relatively low (STDEV < 1.8°). Although the UNR laboratory measured large standard deviations (STDEV > 5°) for the contact angle data for both membranes, it measured a relatively small standard deviation (STDEV = 1°) for the Teflon standard. It is therefore most likely that the source of the high standard deviation is due to techniques or procedures specific to the membranes, rather than an inherent instrument error.

Table 3.8 Contact angle data for the SW30HR membrane from the three participating laboratories (Colorado School of Mines - CSM, Duke University – DU, and the University of Nevada,

Reno – UNR). All contact angles were measured using the captive bubble method and doubly deionized water as the contact angle probe liquid (T = 20°C).

Lab/bubble 1 2 3 4 5 6 Avg.

CSM 40.3 40.0 39.7 42.3 40.4 44.1 41.1

DU 34.2 33.4 33.7 32.5 31.3 32.3 32.9

UNR 30.0 29.2 29.0 35.8 39.9 39.7 39.7


44

Figure 3.10. Box and whiskers plot for the SW30HR contact angle data that was generated by the Colorado School of Mines (CSM), Duke University (DU) and the University of Nevada, Reno (UNR). All contact angles were measured using the captive bubble method and doubly deionized water as the contact angle probe liquid (T = 20°C).

CSM DU UNR

Figure 3.11. Representative digital images of air bubbles on the SW30HR membrane immersed in water at the CSM, DU, and UNR laboratories. Each of these air bubbles are representative of those from which contact angle measurements were performed.

Statistical Analysis of Contact Angle Results for Flat Surfaces

Average values, in addition to reproducibility and repeatability statistics, for the contact angle data that was collected from the three participating laboratories is summarized in Table 3.9. The repeatability was highest for the Teflon standard and lowest for the ESNA1-LF


45

membrane. Reproducibility was highest for the SW30HR membrane and lowest for the ESNA1-LF membrane. Notably the repeatability and reproducibility did not correspond with surface roughness or chemical heterogeneity. Here, the ESNA1-LF membrane was characterized by a smoother surface relative to the SW30HR membrane, but had the lowest degree of reproducibility and repeatability with regards to its contact angle data. Similarly, the Teflon standard would be thought to have the most chemically homogeneous surface relative to the two membranes, but had a lower degree of reproducibility compared to the SW30HR membrane.

Table 3.9 Average contact angle values for the PTFE standard surface, ESNA1-LF, and SW30HR

membrane. The associated statistical measures (repeatability and reproducibility) from the inter-laboratory study are also reported for each test surface.

Material Average of Cell Averages [°]

Repeatability [°]

Reproducibility [°]

PTFE 95.9 2.4 5.6 ESNA1-LF 30.2 4.5 10.4 SW30HR 36.0 3.3 5.2

A certified contact angle fixed-drop calibration reference tool (Figure 3.12) from Ramé-Hart (Netcong, NJ) was used at the UNR laboratory to investigate systematic errors in the proposed standard technique. Contact angle results for the calibration tool were compared to contact angles measured for five membrane samples at the UNR laboratory (Table 3.10). The RMS error for the calibration tool contact angle values was 3.8°, which is comparable to the random error that was observed for the membrane contact angle data. This result suggests that the error associated with the membrane contact angles is likely systematic. User error cannot be ruled out here, though it is less likely as it would be reasonable to expect user error to be more random. Nevertheless, additional testing using the calibration tool would be required to completely rule out user error.

Although the 2D calibration tool is vey convenient, it has a baseline and drop image occurring at the same plane, which reduces error sensitivity to stage-camera misalignment. Another 3D contact angle reference tool that is based on gauge balls and gauge blocks with dimensions traceable to NIST was also evaluated (Figure 3.13). The gauge balls are characterized by a diameter (D) that can be only slightly larger than the block thickness (P), from which spherical caps having relatively small contact angles can be produced. It turns out that front-back inclination of the stage with respect to the camera axis can diminish the appearance of the spherical cap (smaller contact angle) and even make it disappear (θ = 0°). This observation strongly suggests that front-back inclination of the stage with respect to the camera axis can be a major contributor to the experimental error. Recall that sample inclination was cited as a possible source of error in the contact angle results from the three laboratories, particularly for the Teflon standard surface where CSM produced considerably lower results than DU and UNR (Table 3.6). Considering the importance of sample orientation, it is proposed that the 3D tool shown in Figure 3.13 be used to verify stage alignment prior to contact angle measurement. A possible stage/sample alignment technique would be to search for that position which results in a maximum contact angle value in sessile drop mode (or minimum contact angle in captive bubble mode) using the 3D calibration tool. Alternatively, fabricating a new type of sample holder, that


46

would include a 3D fixed standard at the edge of the membrane mounting area would provide for convenient stage alignment verification each time a sample is measured.

Figure 3.12. Picture of the 2D certified contact angle fixed-drop calibration reference tool (Ramé-Hart, Netcong, NJ).

1 12 tan 2tanH D P

D H Pθ − − −

= =−

Figure 3.13. (left) Picture of a 3D contact angle reference tool, which includes the sample surface stage, flat stainless steel plate, and stainless steel sphere. (right) Schematic illustration and equations associated with using the 3D contact angle reference tool (θ = contact angle measured through the stainless steel sphere, D = diameter of the stainless steel sphere, H = distance from the top of the sphere to the top elevation of the stainless steel cross member, and P = distance from the top elevation of the sample stage to the top elevation of the stainless steel cross member.


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Table 3.10 Reported and measured contact angle values for the 2D reference tool. All results were

acquired by the laboratory at the University of Nevada, Reno (UNR).

Drop Actual Measured Measured - Actual

Right [°] Left [°] Right [°] Left [°] Right [°] Left [°] A 31.5 30.0 26.2 25.2 -5.3 -4.8 B 61.2 60.4 61.3 62.1 0.1 1.7 C 90.9 89.0 87.7 84.3 -3.2 -4.7 D 117.2 120.7 115.0 116.0 -2.2 -4.7 Contact Angle Analysis of Curved Surfaces

A new method for measuring contact angles on curved surfaces, such as hollow fiber membranes, is proposed. The proposed method involves conditioning the membrane and placing it between two flat pieces cut out of microscope slides that are held in place by a holder with adjustable screws. The captive bubble accessory from the UNR goniometer setup was incidentally appropriate for the task. The membrane is stretched gently (to provide a straight horizontal baseline in the image) and the slides are tightened just enough to hold it in place. The glass slides should be parallel and enclose empty space below the membrane. The hollow fiber axis is mounted perpendicular to the camera axis in a reservoir with water and a bubble is introduced via a U-shaped needle between the slides, where it floats upward and attaches itself to the membrane (see Figure 3.14).

Contact angle values for hollow fiber membrane samples are reported in Table 3.11. Results for the Norit-X-Flow membrane (θ = 39.5 ± 8.8°) are lower than expected based on the matrix material, PES. However, when comparing to measurements on the matrix material, the type of measurement (advancing or receding contact angle) would have to be considered as well as the effects of surface roughness and porosity that would occur for membrane samples. Furthermore, it is typical for membrane formulations to contain hydrophilic components that make the membrane more hydrophilic than the matrix materials. A similar contact angle value was measured for the CMF-S membrane. Again, this value is lower than expected based on the matrix material.


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Figure 3.14. Photographs of the experimental apparatus that was designed and used to measure contact angles on hollow fiber membranes. (left and middle) Image of the PTFE tubing sandwiched between two glass slides and secured in the sample stage. (right) Image of an air bubble that has been placed on the PTFE tubing immersed in water.

Table 3.11 Contact angle results for hollow fiber membranes. All results were obtained using the

experimental apparatus that was developed as part of this project. Membrane Contact Angle, ° 95% confidence error bar, ° Norit X-Flow 39.5 8.8 Siemens Memcor CMF-S 43.4 5.0 Standard Method for Contact Angle Measurements on Flat-Sheet and Hollow Fiber Membranes

Based on the contact angle analysis carried out by the three participating laboratories, a standard technique for measuring contact angles on membrane surfaces was developed and is given in Appendix D. Additional recommendations for measuring contact angles on membrane surfaces are also given in Appendix D.

METHOD DEVELOPMENT FOR STREAMING POTENTIAL (ZETA POTENTIAL) MEASUREMENTS PMMA Standard Surface

Zeta potential results for the PMMA standard surface from UNR and Anton Paar are given in Figure 3.15. It should be noted that the ionic strength values used by each laboratory varied. The tests by UNR were at an ionic strength of 2 mM KCl, while those by Anton Paar were at an ionic strength of 1 mM KCl. The relatively small difference in ionic strengths is not expected to significantly affect the reported results. The reported data points from UNR represent averages of three trials on three different membrane coupons. The following observations can be made regarding the zeta potential data that are presented in Figure 3.15:

• Both data sets demonstrate a similar isoelectric point for PMMA (pHiep ~ 4.0 to 4.2), and


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• The two data sets are nearly identical from pH 3 to pH 7, before diverging. The data from Anton Paar indicates a slightly more negatively charged surface at pH > 7.

Based on the two data sets reported here it appears that PMMA is an appropriate standard surface for streaming potential measurements. Furthermore, comparing the zeta potential results that were produced by the different electrokinetic analyzers that were used at the two laboratories suggests that uniform results may be achieved regardless of the type of instrument used. In other words, variations in streaming potential results between labs may be minimized through the use of standardized test procedures developed in this project.

Figure 3.15. Zeta potential data collected for the PMMA control surface from UNR – University of Nevada, Reno (I = 2 mM KCl) and Anton Paar (I = 1 mM KCl). Polynomial fits (cubic) to both data sets are also provided. .

Streaming Potential (Zeta Potential) Analysis of Flat Sheet Membrane Surfaces ESNA1-LF Membrane

Zeta potential data as a function of solution pH for the ESNA1-LF membrane are reported in Figure 3.16. The following observations may be made for these data:

• The zeta potential results between the four laboratories all follow a similar trend and demonstrate good reproducibility independent of the electrokinetic analyser used,


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• The results from UCR and Anton Paar overlap over the entire pH range tested here; in comparison the results from UNR were less negatively charged at pH 4, but more strongly negatively charged at pH > 5,

• Those zeta potential results from FKKT were more strongly negatively charged than those results from UCR and Anton Paar at pH > 3,

• The measured pHiep for the ESNA1-LF membrane based on the reported zeta potential results is between 3.1 and 3.9, and

• The ESNA1-LF membrane is strongly negatively charged over a pH range of 4 to 9.

Figure 3.16. Zeta potential as a function of solution pH for the ESNA1-LF membrane (I = 2 mM KCl). Streaming potential measurements were by the UCR – University of California Riverside, UNR – University of Nevada, Reno, the FKKT - University of Maribor, and Anton Paar. Those data points from UCR, FKKT, and Anton Paar represent the average of three separate tests on three different membarne coupons.


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SW30HR Membrane

Zeta potential as a function of solution pH for the SW30HR membrane is reported in Figure 3.17. The following observations may be made for these data:

• The zeta potential results for the SW30HR membrane were less reproducible between all four laboratories relative to those results for the ESNA1-LF membrane,

• With the exception of those results from UCR (higher deviation in results) the individual results for each laboratory were reproducible,

• Results from the UCR, UNR, and FKKT all showed a more negatively charged membrane surface relative to those zeta potential values measured by Anton Paar at pH > 3,

• The measured pHiep for the SW30HR membrane ranged from 2.4 to 3.9, and • The SW30HR membrane is less negatively charged relative to the ESNA1-LF membrane

over the same pH range (4 to 9).

Figure 3.17. Zeta potential as a function of solution pH for the SW30HR membrane (I = 2 mM KCl). Streaming potential measurements were by the UCR – University of California Riverside, UNR – University of Nevada, Reno, the FKKT - University of Maribor, and Anton Paar. Those data points from UCR, FKKT, and Anton Paar represent the average of three separate tests on three different membrane coupons.


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Interpretation of the zeta potential results for the two membranes indicates that relatively reproducible results may be obtained between laboratories. This observation is independent of the types of electrokinetic analyzer used, but dependent on the use of a standard method for conducting the streaming potential measurements. Additional tests by Anton Paar (data not shown) suggest that the differences in zeta potential results between the laboratories may be attributed to slight variations in membrane pretreatment conditions (i.e., soaking time and conditioning solution). For example, the measured zeta potential for both the ESNA1-LF and SW30HR membrane decreased (became less negative) when the membrane was allowed to soak in an electrolyte solution for 24 hours prior to conducting the streaming potential measurements. In other words, analyzing dry versus wet membranes, and specifically the soaking time and chemistry of the soaking solution, may result in different zeta potential values. Although the specific mechanisms by which the soaking conditions affect the measured zeta potential remain unclear at this point, specific membrane pretreatment conditions were addressed in the final version of the standard method for streaming potential measurements (Appendix D).

Streaming Potential (Zeta Potential) Analysis of Hollow Fiber Membrane Surfaces Norit X-Flow Membrane

Streaming potential measurements for hollow fiber membranes has in the past proven to be a challenge. This is due to the fact that the standard measurement cells for electrokinetic analyzers cannot be used for hollow fibers. This challenge was overcome here by modifying a streaming potential test cell to accommodate a bundle of hollow fibers, which were potted in place (see Appendix D for further details). Using this approach, streaming potential measurements were using the Norit X-flow hollow fiber membrane. In these measurements the streaming potential was measured for the interior of the hollow fiber; however, this same technique could be applied to the exterior portion of the fiber as well. The zeta potential results as a function of solution pH for this hollow fiber membrane are reported in Figure 3.18. The Norit X-flow membrane was characterized by an isoelectric point (pHiep) of approximately 6. It was positively charged below pH 6 and increasingly negatively charged as solution pH became more basic. Overall, it can be said that this membrane is relatively weakly charged (ζ < ± 10 mV) over the pH range tested here (3 to 7). The reported zeta potential results were highly reproducible as indicated by the small error bars in Figure 3.18. These results indicate that the proposed method for measuring streaming potential for hollow fiber membranes is viable and capable of producing reproducible results.


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Figure 3.18. Zeta potential as a function of solution pH for the inner surface of the Norit X-Flow hollow fiber membrane (I = 2 mM KCl, n = 3).

Standard Method for Streaming Potential Measurements on Flat-Sheet and Hollow Fiber Membranes

The reproducibility in zeta potential values between different laboratories using different electrokinetic analyzers was determined to be good when using the proposed standard method for streaming potential measurements. The proposed standard method was slightly modified to highlight the importance of uniform membrane pretreatment conditions prior to carrying out the streaming potential measurements. The final version of the standard method for conducting streaming potential measurements on both flat sheet and hollow fiber membrane surfaces is presented in Appendix D.

METHOD FEEDBACK FROM PARTNER UTILITIES

The distribution of surveys that were returned and incorporated into this report is given in Figure 3.19. A total of 25 surveys were distributed and 11 were completed and returned. The largest number of responses to the survey was from academia followed by water/wastewater utilities that are currently using membranes in their treatment plants.


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Figure 3.19 Distribution of survey responses amongst each of the four respondent categories (water/wastewater utilities, membrane manufacturers, characterization equipment vendors, and academia). The total number of survey responses that were received was eleven.

The distribution of responses to the question “Given our description of the technique and the full test method, would you envision finding the results of the contact angle test useful?” are reported in Figure 3.20. All respondents to this question stated that they thought that contact angle measurements are useful for some purpose in membrane applications. Specific application areas are summarized in Table 3.12. In general, academics and researchers felt that contact angle measurements are useful for characterizing the interfacial properties of membranes, while those in the industry felt that contact angle measurements are best suited for use in selecting an appropriate membrane for a given application. In fact, all of the academic respondents stated that they use contact angle measurements frequently in their respective research efforts. Utility respondents did not feel that they are equipped to perform contact angle measurements for various reasons (see Table 3.12). Similarly, the responding utilities do not use contact angle results in the day-to-day operation of their systems. Instead, it was commented that contact angle results are only useful during membrane selection. These results suggest that while the importance of understanding the hydrophobicity of a membrane is important to all those involved in membrane applications, the ability to perform these measurements, and use of the subsequent results, is restricted to academics and researchers.


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Figure 3.20. Distribution of responses to the question on the usefulness of contact angle measurements in membrane applications.

All of the academic respondents felt that they could follow and conduct the contact angle measurements as detailed in the draft standard procedure. Conversely, all utilities and one out of two membrane manufacturers responded that they could not conduct the contact angle measurement. This response is attributed to the fact that the utilities and membrane manufacturer lacked the necessary equipment (i.e., a goniometer) for conducting these measurements. Surveying the utilities found that like most installations, with the exception of the Orange County Water District, they are not equipped with research facilities beyond those basic ones required for water quality testing. Indeed, a lack of available resources and personnel appear to be the largest hurdles for utilities to take advantage of contact angle measurements (see Table 3.12).

In response to their thoughts on the precision of the presented contact angle method all of the respondents (the utilities, membrane manufacturers and equipment vendors, and two from academia) were unsure and in general preferred to withhold judgment until data from various independent labs could be compared. Those from academia believed that the method was precise in the manner in which it was presented, though they could not comment on the data that would be generated.


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Table 3.12 Specific comments on the usefulness and technique for carrying out contact angle

measurements in membrane applications. Question Area Comments

Would the information that could be gained from contact angle measurements be useful to you? How would you foresee using the results and how often do you think you would like to have the contact angle test conducted?

• Academia• Cited uses included: (6 respondents) calculate

interfacial components, (1 respondent) calculate surface charge / zeta potential.

–

• Membrane Utility• (2 respondents) The information obtained from the

tests would be useful to membrane designers when selecting a membrane for a given application / source water.

-

• The data gathered from these tests are something seen as a membrane selection tool and are not really important to actual treatment operations.

• We would require a membrane supplier to provide this information (contact angle) as certified test results as a required submittal for membrane procurement. The test method would be specified.

• We would only perform (contact angle measurements) again if Federal or State regulations required the test be performed on “used” membranes after a pre-determined time of service. The intent being we would be required to change the pressure set points of our full-scale plant’s membrane integrity test system if the contact angle significantly changed.

• Membrane Manufacturers• Yes, because hydrophilicity is an important material

property for water filtration membranes

–

Does your laboratory have the capability of conducting contact angle measurements?

• Academia• (6 respondents) Yes.

–

• Membrane Manufacturers• Yes

–

The distribution of responses to the question “Given our description of the technique and

the full test method, would you envision finding the results of the zeta potential test useful?” are reported in Figure 3.21. No responses from manufacturers of streaming potential instruments were received and therefore “Equipment Vendor” is omitted from Figure 3.21. Similar to what was observed for contact angle measurements all of the respondents felt that zeta potential measurements are useful in their respective applications (Table 3.13). However, as was also observed for contact angle measurements, no utilities were capable or equipped to conduct these


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measurements (i.e., streaming potential measurements). Coincidently, only those respondents from academia responded that they use zeta potential measurements on a frequent basis.

Figure 3.21. Distribution of responses to the question on the usefulness of zeta potential values, acquired through streaming potential measurements, in membrane applications.

Overall, the survey respondents felt that the learning curve for conducting the streaming potential measurements would be steeper than that for the contact angle measurements. However, the learning curve was not thought to be insurmountable.


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Table 3.13 Specific comments on the usefulness and technique for carrying out streaming potential

measurements to calculate the zeta potential of membranes in water and wastewater treatment applications.

Question Area Comments1

Would the information that could be gained from the zeta potential measurements be useful to you?

• Academia• (6 respondents) Yes, zeta potential is useful for

characterizing membrane-solute interactions.

–

• Membrane Utility• It (zeta potential) would be useful but there are some

challenges.

–

• Membrane Manufacturer• Yes because it is undisputed that the charge of the

surface has an influence on the fouling potential and even the separation performance of a membrane.

–

How would you foresee using the results and how often do you think you would like to have the streaming potential (zeta potential) test conducted?

• Academia• (3 respondents) They would be used anytime a

new/novel membrane is made/tested.

–

• Zeta potential tests would be highly useful to gather insight into membrane fouling and rejection performance. I imagine tests could be performed as often as once a week.

• Yes, the zeta potential can show surface charge density of a membrane, which can help predict or explain results of particular tests. The zeta potential test should be used to characterize different film coatings on membranes.

• Membrane Utility• We would require the membrane supplier to

provide this information as certified test result for “virgin membrane” as a required submittal for membrane procurement. The test method would be specified.

–

• I would need to read more and understand what initial “virgin” measures of zeta potential mean for a membrane fiber and then understand what relative changes in the zeta potential mean with respect to membrane fouling and changes in cleaning regimes. Assuming such information exists, it would be useful to perform (or have performed) the zeta potential analysis to assess change in membrane fouling characteristics over time.


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• Membrane Manufacturer• Periodically depending on the project requirements.

–

Does your laboratory/facility have the capability of conducting streaming potential (zeta potential) measurements?

• Membrane Utility• No – sample preparation for hollow fiber analysis

seems prone to fiber plugging. We would need training in preparation of the porous plug that was described in the procedure.

–

• Membrane Manufacturer• We have an apparatus of our own that could

measure the streaming potential of a membrane. This apparatus is however still in the development stage, hence it will be difficult to measure this in-house on a regular basis.

–

The distribution of responses to the question “Given our description of the technique and the full test method, would you envision finding the results of the surface roughness (AFM) test useful?” are reported in Figure 3.22. Of the three different types of characterization techniques / results that were presented in the survey it was the surface roughness measurements that were the least familiar to the respondents from the membrane utilities. This result was reflected in the specific comments that were received from the utilities (Table 3.14). Interestingly, even those utilities that responded that the measurements would be useful to them questioned how the results would be used in a practical application. Similar to the other characterization results, it appears that the prevailing thought outside of academia is that membrane characterization data would only be useful during membrane selection and that acquiring this characterization data becomes impractical once the membrane is in use. The greatest hurdle to characterizing the membrane properties once it is in service is the removal of a membrane from the operating element or module. This brings to question the need to implement “sacrificial” membranes into full-scale operations that may be easily removed without hindering the operation of the entire membrane system.


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Figure 3.22. Distribution of responses to the question on the usefulness of surface roughness measurements, performed using an atomic force microscope, in membrane applications.


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Table 3.14

Specific comments on the usefulness and technique for carrying out surface roughness measurements using an atomic force microscope for membranes in water and wastewater

treatment applications. Question Area Comments

Would the information that could be gained from the membrane surface roughness measurements be useful to you and/or your organization?

• Membrane Utility• I am unfamiliar with the correlation between surface

roughness and membrane fouling potential. Surface roughness is not listed on a typical membrane supplier’s technical data sheet.

–

• Membrane Manufacturer• It would depend on the type/characteristics of the

membrane in question.

–

How would you foresee using the results and how often do you think you would like to have the surface roughness test conducted?

• Academia• Roughness data is important for qualitative

interpretations of surface dependent forces, such as surface charge, etc. Roughness characterization should be a standard procedure for all new materials and should be used as often as a lab studies new membrane materials.

–

• Membrane Utility• To perform this test after initial membrane

procurement would require a means to harvest fibers from an encased full-scale module which has been in service. Would need to either sacrifice a module (adding to the overall cost of doing the analysis) or work with membrane supplier on a way to harvest fibers and patch the module casing to be able to return the module to service.

–

Does your laboratory/facility have the capability of conducting AFM measurements?

• Academia• I believe of all the characterization techniques

discussed, AFM is by far the most difficult to learn. I think our whole lab group will be capable of conducting the experiment, but it may take some of us much longer.

–

GUIDANCE FOR INTEGRITY TEST CALCULATIONS

Pressure integrity testing is a stipulated requirement in the EPA’s Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) (EPA, 2006). Integrity tests are required to identify holes, breaks or other losses of structural integrity for MF and UF membranes. This testing is in place and is necessary to insure the optimal performance of MF/UF membrane processes. These


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tests are primarily geared toward identifying integrity breaches that would allow Cryptosporidium oocysts (dp = 3 μm) to pass through the membrane and into the filtrate. The basis for pressure integrity tests is that any loss of structural integrity would result in a reduction in the pressure that would be required to observe air bubbling through the pores of the hollow fiber membrane. The net pressure applied during membrane integrity tests must ensure that any breach in the membrane structure that is large enough to pass Cryptosporidium oocysts would also pass air during the test (EPA, 2005). The minimum direct integrity test pressure for obtaining a resolution of 3 μm for the removal of Cryptosporidium may be calculated according to Eq. 3.1.

maxcos193.0 BPPtest += θκσ (3.1)

where Ptest is the minimum direct integrity test pressure (psi), κ is the pore shape correction factor (= 0 to 1), σ is the surface tension at the air-liquid interface (dynes/cm), θ is the liquid-membrane contact angle (= 0 to 90°), BPmax is the maximum backpressure on the system during the test (psi), and 0.193 is the constant that includes the defect diameter (i.e., 3 μm resolution requirement) and unit conversion factors (EPA, 2005). A value of 1 for κ represents a perfectly cylindrical pore, while values approaching 0 are indicative of greater deviations from this ideal structure. Theoretically, cylindrical pores are characterized by higher pore liquid entry pressures relative to non-cylindrical pore geometries. It is current practice to assume that the membrane is perfectly hydrophilic (i.e., θ = 0°) and that κ = 1 so as to produce the most conservative estimate for Ptest. However, in reality most hollow fiber MF and UF membranes are characterized as being less than perfectly hydrophilic (i.e., more hydrophobic) and as having non-cylindrical pores. On this subject the following exert is taken from the Membrane Filtration Guidance Manual (EPA, 2005):

“The LT2ESWTR does not establish the minimum test pressure to be used during a pressure-based direct integrity test, but rather only requires that the test achieve a 3-µm resolution. If a membrane manufacturer has information to support the use of values other than κ = 1 and θ = 0, and these less conservative values are approved by the State, then Equation 4.1 can be used to calculate the minimum required test pressure. It is essential that the use of values other than κ = 1 and θ = 0 be scientifically defensible, since the use of inappropriate values could result in the use of a test pressure that does not meet the resolution criterion established by the rule. One approach for determining membrane-specific values for κ and θ is through direct experimental evaluation. Because these parameters can have a significant effect on the required direct integrity test pressure, it is strongly recommended that States require sufficient justification from a membrane manufacturer prior to approving the use of values other than κ = 1 and θ = 0, such as independent third party testing results using a method accepted by the scientific community and demonstrating statistically significant data.”

In order to determine a defensible value for θ, which may be used to calculate an accurate value for Ptest it will be necessary to apply a standard method for measuring contact angle with water on hollow fiber MF/UF membranes. On this subject the following general recommendations are made:


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• Contact angle results measurements must be made on the same membrane configuration for which the results are to be applied. In other words, should the results be intended for calculating Ptest for a hollow fiber membrane, then the contact angle measurements must be made on a hollow fiber membrane (contact angle results obtained for a flat-sheet sample cannot be used for calculating the Ptest value for a hollow fiber membrane).

• Contact angle results on a certified sample surface, such as those recommended as part of this report (e.g., PTFE, 2D and 3D calibration tools) should be made alongside the laboratory results for contact angle measurements on hollow fiber samples.

• For a more conservative contact angle measurement, the standard method for measuring receding contact angle with water on unsealed hollow fiber membranes (as reported in Appendix D), should be used to determine θ for use in Eq. 3.1 to calculate Ptest.

• If a tensiometric method (e.g., Wilhelmy Plate technique) is used with sealed fibers, then the less conservative (higher) advancing contact angle will be determined. Results for receding and advancing contact angle will not agree for membranes due to surface roughness and porosity.

• Further research is recommended to determine the impact(s) of surface roughness and porosity on the contact angle results, and hence, on the required Ptest. Application of existing models, such as the Wenzel and Cassie Baxter models, for correcting the measured contact angle for membrane surface roughness should be examined with emphasis on how well these corrections improve the accuracy of the resultant Ptest value.

• The repeatability/reproducibility in contact angle values between any two laboratories as determined in this project was 5°. Such a difference in θwater would result in variations in the required Ptest as determined by an end user or regulatory agency. The difference in the calculated Ptest value will vary depending on the magnitude of the contact angle (see Figure 3.23). As the contact angle increases from 0 to 90° the 5° variation results in an increase in the error that is associated with the calculated Ptest value. For a membrane characterized by a contact angle of 80° at one lab and 75° at a separate lab the resulting difference in Ptest values would be 1.2 psi. Future research and consideration by an appropriate regulatory agency is required in order to determine if such an error is acceptable.


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Figure 3.23 Difference in the calculated values for the minimum direct integrity test pressure (Ptest) for obtaining a resolution (i.e., minimum defect diameter) of 3 μm assuming a standard error in the contact angle value of 5°. The values for Ptest were calculated using Eq. 3.1 (κ = 1, σ = 74.9 dynes/cm, BPmax = 3 psi).

• Membrane surfaces become fouled over time with any number of different materials (organics, inorganics, biological materials), which subsequently alters the hydrophilic and hydrophobic properties of the membrane surface. The extent of this alteration will be system specific, as it will depend on the feed water composition and chemistry, in addition to the properties of the membranes being used. Therefore, θ values will change over time and in turn those θ values measured on virgin membrane samples are not likely to be accurate as operation time progresses. It is therefore recommended that if measured values for θ be used to determine Ptest then new contact angle measurements be performed on hollow fiber samples harvested from currently operating modules. In order to alleviate concerns that are likely to be associated with removing fibers from functioning modules it is suggested that sacrificial membrane modules be included into system designs that treat side streams of the feed water. Such a design will allow for the membranes to become similarly fouled as those in the full-scale process, while allowing for greater flexibility in harvesting membrane samples for contact angle analysis. Additional research is needed on this subject given the complex relationship between fouling and its effects on membrane surface chemistry (hydrophilicity/hydrophobicity). Experimental data is required to characterize the heterogeneity in changes in membrane

Difference in direct integrity test pressure due to 5° standard error in contact angle measurement. The middle value represents the integrity test pressure that would be calculated from the average contact angle value.


65

surface chemistry (e.g., heterogeneity resulting from the distribution and varying severity of foulant deposits in a single membrane module) that results from fouling and the extent to which this heterogeneity affects the reliability of contact angle measurements on fouled hollow fiber membranes.

EVALUATING CORRELATIONS BETWEEN MEMBRANE CHARACTERISTICS AND PERFORMANCE

Figures 3.24-28 show the results of flux model fitting for each of the NF and RO membranes. In these figures, the central lines represent the regression models, the inner boundaries represent the 95% confidence limits and the outer boundaries represent the model prediction limits. Figure 3.29 compares the relationship between predicted normalized membrane flux and bentonite clay loading for all membranes. A clay loading of 5 g m-2 was chosen as the load used for relating membrane performance to membrane parameters for MLR modeling.

NF270_Perm_Volume

NF270_N

OR

M_FLU

X

0 200 400 600 800 1000

0.6

0.7

0.8

0.9

1

Figure 3.24. Results of simple regression analysis for the NF-270 membrane. 100 ml of permeate corresponds to 1.94 g loaded m-2 membrane. Correlation Coefficient = -0.996635; R-squared = 99.3281 percent; R-squared (adjusted for d.f.) = 99.1042 percent; Standard Error of Est. = 0.02384; Mean absolute error = 0.015953; Durbin-Watson statistic = 2.77534 (P = 0.6576); Lag 1 residual autocorrelation = -0.434792. The central line represents the regression models, the inner boundaries represent the 95% confidence limit and the outer boundaries represent the model prediction limit.


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ESNA_1LF_Perm_Volume

ES

NA

_1LF_N

OR

M_FLU

X

0 200 400 600 800 1000

0.6

0.7

0.8

0.9

1

Figure 3.25. Results of simple regression analysis for the ESNA1-LF membrane:. 100 ml of permeate corresponds to 1.94 g loaded m-2 membranemembrane. Correlation Coefficient = -0.999234; R-squared = 99.8469 percent; R-squared (adjusted for d.f.) = 99.7959 percent; Standard Error of Est. = 0.00762926; Mean absolute error = 0.00468329; Durbin-Watson statistic = 2.83777 (P=0.7339); Lag 1 residual autocorrelation = -0.62428. The central line represents the regression models, the inner boundaries represent the 95% confidence limit and the outer boundaries represent the model prediction limit.


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ESPA_2_Perm_Volume

ES

PA

_2_N

OR

M_FLU

X

0 200 400 600 800 1000

0.6

0.7

0.8

0.9

1

Figure 3.26. Results of simple regression analysis for the ESPA2 membrane. 100 ml of permeate corresponds to 1.94 g loaded m-2 membrane. Correlation Coefficient = -0.998841; R-squared = 99.7683 percent; R-squared (adjusted for d.f.) = 99.691 percent; Standard Error of Est. = 0.0134216; Mean absolute error = 0.00948347; Durbin-Watson statistic = 1.50241 (P=0.0550); Lag 1 residual autocorrelation = 0.0623037. The central line represents the regression models, the inner boundaries represent the 95% confidence limit and the outer boundaries represent the model prediction limit.


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SW30HR_Perm_Volume

SW

30H

R_N

OR

M_FLU

X

0 100 200 300 400

0.6

0.7

0.8

0.9

1

Figure 3.27. Results of simple regression analysis for the SW30HR membrane. 100 ml of permeate corresponds to 1.94 g loaded m-2 membrane. Correlation Coefficient = -0.996067; R-squared = 99.215 percent; R-squared (adjusted for d.f.) = 98.9534 percent; Standard Error of Est. = 0.00316966; Mean absolute error = 0.0020736; Durbin-Watson statistic = 2.6779 (P=0.5877); Lag 1 residual autocorrelation = -0.561779. The central line represents the regression models, the inner boundaries represent the 95% confidence limit and the outer boundaries represent the model prediction limit.


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MUNIRO400_Perm_Volume

MU

NIR

O_N

OR

M_FLU

X

0 500 1000 1500 2000 2500

0.6

0.7

0.8

0.9

1

Figure 3.28. Results of simple regression analysis for the MUNIRO-400 membrane. 100 ml of permeate corresponds to 1.94 g loaded m-2 membrane. Correlation Coefficient = -0.994937; R-squared = 98.9899 percent; R-squared (adjusted for d.f.) = 98.8215 percent; Standard Error of Est. = 0.00624946; Mean absolute error = 0.00416953; Durbin-Watson statistic = 2.42804 (P=0.5686); Lag 1 residual autocorrelation = -0.352731. The central line represents the regression models, the inner boundaries represent the 95% confidence limit and the outer boundaries represent the model prediction limit.


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Figure 3.29. Normalized membrane flux (J/Jo) predicted by the simple regression models as a function of bentonite clay loading. From this, membrane performance of all five membranes at at a given clay load can be determined. A 5g m-2 bentonite load was chosen with which to generate performance data for MLR analysis because this falls within a nearly linear portion of all five relationships and membrane performance could be determined in all cases at this point by interpolation of experimental data.

Multiple Linear Regression (MLR) Methods

The relationship between normalized flux at 5 g m-2 bentonite clay loading and membrane properties were investigated using multiple linear regression (MLR) analysis (Statgraphics Centurion XV, Statpoint Inc., Herndon, VA, USA). Several MLR models were constructed using combinations of membrane properties in order to discover the best set of independent input variables. The following selection criteria were considered for including membrane parameters in the MLR analysis:

• Minimizing the number of inputs

• Maximizing the R-Squared value (maximizing explanation of dependent variable variation) and seeking a Mallow’s Cp index value near to the number of model inputs

• Minimizing P-value for each variable in the model (≤ 0.05 indicating significance in model ≥ 95% confidence level)


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• Minimizing P-value for overall model ANOVA (≤0.05 desirable)

• Minimizing Pearson’s r correlation coefficient between independent variables (< 0.5 desirable)

MLR Model Input Variable Selection

Statgraphics Centurion XV provides a tool for simultaneously constructing and evaluating several possible MLR models. Unfortunately, the paucity of exemplars available for model construction limited the application of this tool, necessitating the manual construction and comparison of clusters of three inputs at a time. Inputs considered for inclusion in the model included:

• average roughness (Ra in nm)

• RMS roughness (Rq in nm)

• zeta potential (ζ/mV)

• zeta potential slope (slope/mV·(pH unit)-1)

• contact angle (θ in °)

• initial membrane water flux (Jo, Lm-2hr-1)

The MLR dependent variable was always the estimated normalized water flux at 5g m-2 bentonite clay load. Input parameter values that were used in the MLR analysis and corresponding intrinsic fluxes are reported in Table 3.15.

Table 3.15 Multiple linear regression (MLR) model input parameters and values for describing

bentonite clay fouling.

Membrane Roughness

Rq nm-1 Contact Angle

θ, degrees

Initial Water Flux Jo L/m2 hr

J/Jo @ 5 g m-2 Clay Load

NF-270 3.8 57.4 21.34 0.903

ESNA1-LF 36.6 26.5 20.75 0.880

ESPA-2 38.8 37.4 20.64 0.908

SW30HR 84.5 33.9 6.82 0.970

MUNIRO-400 40.7 46.9 31.54 0.973


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Selected MLR Model Input Variables

The best MLR model found (the model with the largest R-squared and Mallow’s Cp Index with closest match to the number of input variables) utilized RMS roughness, initial water flux and contact angle, but not zeta potential. Table 3.16 shows the best MLR model parameters and Figure 3.30 illustrates the model fit (Eq. 3.2) to the experiental data.

( ) ( ) ( )θ333 1000.31012.21029.267233.0 −−− ×+×+×+= qinitialNorm RJJ (3.2)

where JNorm is the normalized permeate flux and Jinitial is the initial or starting permeate flux (L m-2 hr-1).

Table 3.16 Constants, coefficients and statistics for the best MLR model: J/Jo (at 5g m-2 Load) = 0.67233+ (2.29×10-3 × Jinitial, L m-2hr-1) + (2.12×10-3 × Rq, nm) + (3.00×10-3 ×θ, degrees).

Estimate Std. Error T-Statistic P-Value

Constant 6.723E-01 2.47E-02 27.2511 0.0234

Jo, L m-2 hr-1 Slope 2.29E-03 5.24E-04 4.3748 0.1431

Rq, nm Slope 2.12E-03 1.86E-04 11.3951 0.0557

θ, degrees Slope 3.00E-03 3.81E-04 7.8996 0.0802


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Figure 3.30. Results: Best MLR model prediction and UNR membrane data determined from laboratory measurements. Statistics: R-Squared = 99.2736%; Adj. R-Squared = 97.0943%; Std. Error of Est. = .00718711; M.A.E = .00251155; P-Value = 0.1069. Horizontal bars = 95% confidence interval for J/Jo @ 5 g m-2 clay load estimated from laboratory data; vertical bars = MLR model standard error of the estimate.

Several of the independent variables are not completely independent, strictly speaking; they shared some degree of cross-correlation. Typically, with multiple linear regressions it is desirable to see the Pearson r between the variables < 0.5 (except for the constant). In this case, initial flux and contact angle were both correlated with RMS roughness > 0.5; however, removal of either term from the model resulted in serious reduction of the R-squared value. Assessing the Predictive Ability of MLR Model Inputs by Iteratively Withholding a Test Exemplar

In order to determine whether or not the MLR model approach has sufficient predictive ability with this very small exemplar set and degree of input variable intercorrelation, five additional MLR models were constructed, each built using just four of the five membrane exemplars as a training set, with one membrane exemplar withheld and then predicted by the resulting model (as a validation set). The performance of the withheld exemplar was then compared for each of the MLR models to the results of the model constructed from the complete data set.

0.85

0.9

0.95

1

1.05

0.85 0.9 0.95 1 1.05

Actual J/Jo

Pre

dict

ed J

/J o

ESNA1-LF

NF-270

SW30HR

ESPA-2

MUNIRO-400


74

If the original MLR model was not generalizing well (was over-fitting the small data set), then removal of any one training exemplar would result in serious degradation of the model ability to predict the validation exemplar. If, however, the MLR model was fitting on more general principals, the model should have sufficient predictive ability to estimate the value of the missing exemplar. With a very small training data set like this, it is additionally possible that removal of one exemplar may result in loss of critical information that will seriously degrade model performance; however, it was anticipated that at least one or more exemplars should be redundant enough so loss of their information does not seriously affect model predictability, and those exemplars when presented as validation examples ought to be predicted well.

Figure 3.31 shows the results of withholding individual exemplars by comparing the membrane behavior predicted by the MLR model constructed from all the exemplars (filled circles) with that where the chosen exemplar was held back from model training, and its behavior predicted by an MLR model constructed with only the remaining four exemplars (open circles).

In the case of the ESPA-2 and ESNA1-LF membranes, omission of the exemplar from the training data resulted in models still largely capable of predicting membrane behavior, suggesting that this MLR approach was, overall, not just fitting noise but was in fact responding to a relationship between the model independent variables and the membrane J/Jo. This lends credence to the validity of the complete data set MLR model, though it is only statistically significant at slightly less than the 90% confidence level. In the cases where the other membrane exemplars were withheld, the deviations were more extreme, suggesting that training data provided by the NF-270, SW30HR and MUNIRO-400 membranes were all critical to constructing an MLR model capable of predicting membrane behavior.


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0.8

0.85

0.9

0.95

1

1.05

0.8 0.85 0.9 0.95 1 1.05

Measured J/Jo

Pred

icte

d J/

J

Model with Exemplar Omitted Model With All Exemplars

ESNA-1LF

SW30HR

ESPA-2

NF-270

MUNIRO-400

Figure 3.31. Comparison of the prediction of membrane behavior by the MLR model constructed from all the exemplars (filled circles) with prediction of five MLR models that were constructed using data missing each of the individual membranes indicated (open circles). Horizontal bars = 95% confidence interval for J/Jo @ 5 g m-2 clay load estimated from laboratory data; vertical bars = MLR model standard error of the estimate.

Assessing the Influence of MLR Model Input Parameters

It is possible to evaluate the influence that individual input parameters have on model output by examination of specific component effects. In this approach, the relative influence that each input has over the model output is assessed over the range of values of that particular input. This analysis was performed for all of the model inputs, and the inputs were than classified based on the magnitude of overall influence in the model (below).

RMS Roughness

RMS roughness was the most influential of the input variables, ranging from -0.0774 (NF-270) to +0.0935 (SW30HR), with a full range of 0.171. This input variable was positively related to the predicted normalized water flux (J/Jo) after loading 5g m-2 of bentonite clay, which suggests that more rugose membrane surfaces fared better in dealing with the clay foulant than smoother ones. Though it has been often observed that smoother membranes have been related to fouling resistance (at least biological fouling), the geometry of the clay particles may explain this result. Clay particles are thin plate-like structures that tend to stack in close, organized layers on


76

smooth surfaces; on rougher surfaces it is possible that the stacking geometry becomes disrupted, resulting in a cake layer that is less organized. Also, for larger clay particles, access to the surface of more rugose membranes may be impared as the particles are only able to associate with the “tops” of membrane features, with reduced opportunity for adhesion.

Initial Water Flux

Initial water flux (Jo, L m-2 hr-1) was the second most influential input parameter, with a component effect ranging from -0.0297 (SW30HR) to +0.0271 (MUNIRO-400), with a full range of 0.0971. Initial water flux was positively related to the predicted membrane normalized water flux after loading 5 g of bentonite clay. At the moment, the exact mechanism by which initial water flux may be related to bentonite clay membrane fouling is unclear. As with roughness, this effect seems a little counterintuitive at the outset, because a higher water flux suggests a higher force vector toward the membrane surface, which should cause suspended clay particles to be pressed in tighter. However, higher membrane Jo might also increase the membrane polarization layer, increasing the localized concentration of nonovalent and divalent cations. Higher salt levels, especially higher monovalent salt levels, tend to favor increased binding of clay particles. If the particles are arrayed on the rougher surfaces to begin with, the tighter binding might prevent clay particles from slipping against one another, and maintain a more open cake structure as more particles are deposited near the membrane surface.

Contact Angle

Contact angle was the least influencial of the model input variables; however as previously explained its removal from the MLR model resulted in a significant degradation in explanatory ability. The component effect of contact angle ranged from -0.0389 (ESNA1-LF) to +.0522 (NF-270) with a total range of 0.0568. Contact angle was positively related to the predicted membrane normalized water flux after loading 5 g of bentonite clay, meaning that water flux through the clay cake tended to improve when the membrane surface was more hydrophobic. Clay particles are microscopic to nanoscopic plate-like particles carrying a diffuse negative charge, and effectively attract water molecules that can form a layer from 10 nm to up to 40 nm over the particle surface. This effectively makes the colloidal clay particles quite hydrophilic, and as such they would tend not to adhere well to the more hydrophobic membrane surfaces (surfaces with higher contact angles). This would lead to formation of thinner and less stable clay cake layers on these more hydrophobic membranes, which should result in higher observed intrinsic water flux. This may well be the principal reason that contact angle appears positively related to intrinsic water flux during fouling with bentonite clay.

Use of a Log-Decay Nonlinear Model coupled with MLR to Describe Bentonite Clay Membrane Fouling in Terms of Membrane Properties

It was observed that, with more extensive bentonite clay exposure, the normalized permeate flux rate for some membranes being fouled did not always decay to zero. Instead, for several of the membranes it appeared that with increasing clay loading the normalized permeate flux decayed in a log-like fashion to some plateau value, below which it did not decrease further regardless of the additional clay loading. It might be postulated that for these membranes a critical flux had been reached. It was therefore determined that the normalized permeate flux (J/Jo) decline data for these membranes might be fitted with a classical flux decline model using


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nonlinear methods in order to resolve the governing parameters for the observed membrane fouling process. The log decay function that was determined to be most appropriate for this approach is given in Equation 3.3.

3.3

where K is a decay rate coefficient whose value is hypothetically based on the impact of a given foulant on the normalized permeate flux rate (J/Jo); Clay Load is the mass of clay impinged per unit area of the membrane (g m-2); and J/Jo Plateau is the minimal value to which the normalized permeate flux declined to following fouling by the clay particles.

Hypothetical Meaning of K

The K value is postulated to represent the immediate interaction between the clay particles and the membrane surface that leads to a decline in the normalized permeate flux (J/Jo). For a given amount of clay impinging on the membrane surface, the larger the value of K the more rapid the decline in J/Jo. Put another way, system variables that increase the value of K increase the severity of the initial decline in J/Jo (i.e., greater fouling), while parameters that reduce K lessen the severity of the initial fouling. It is expected that the K value will be most sensitive to interactions between the clean membrane surface and clay particles, since it describes the kinetics of fouling where clay is presumably just beginning to coat the membrane surface as opposed to building up on layers of previously deposited clay particles. Thus, K can be a good measure of how resistant the membrane is to bentonite clay fouling. It is also presumed that K will be especially sensitive to membrane surface parameters (e.g., hydrophobicity/hydrophilicity, surface roughness).

Hypothetical Meaning of J/Jo Plateau

The method by which the J/Jo Plateau value in Equation 3.3 was determined is illustrated in Figure 3.31. This value hypothetically represents equilibrium between the addition of bentonite clay particles to the cake structure on the membrane surface and the removal of clay particles from the cake by cross flow shear. It may be expected at first to be less related to membrane surface properties than to the properties of the cake itself, namely its thickness and permeability. However, a complicating factor is that because of the nature of bentonite clay particles, the overall structure of the cake as it accumulates on the membrane surface may be significantly influenced by the geometry of the initial deposition layer. Additional factors that may affect cake stability and water permeability may include cake compression, ionic effects of concentration polarization (i.e., cake enhanced concentration polarization), as well as loss of net hydraulic pressure caused by increased osmotic pressure at the membrane surface. Thus, many factors may interactively influence the observed J/Jo Plateau value and as a result, it may relate to membrane parameters in a very complex way. Nevertheless, the lower the observed value of the J/Jo Plateau the more prone the membrane is to fouling by the clay.

Because membrane surface properties [e.g., roughness, contact angle with water (hydrophobicity), and zeta potential] have been shown to determine the initial deposition and attachment of foulant materials to membrane surfaces, at least to some extent, they are expected


78

to affect the values of both K and J/Jo Plateau. The relative impact that each of the aforementioned membrane surface properties has on determining the values of K and J/Jo Plateau was investigated by using multiple linear regression (MLR) methods to determine how well one or more of these membrane properties can predict the observed variations in K and J/Jo Plateau for the test membranes.

Figure 3.32. Illustration of the decline in normalized permeate flux rate (J/Jo) with increasing clay load. The plateau value (J/Jo, Plateau) is taken as a measure of the severity of the impact of bentonite clay on the permeate flux rate for a membrane and is determined as the point where J/Jo becomes zero order with respect to clay loaded.

The specific objectives of this MLR analysis were to first determine values of K and of J/Jo, Plateau for each of the test membranes by applying the laboratory data to Equation 3.3. Once this was achieved, the observed variations in K and J/Jo, Plateau were related to the previously described membrane parameters (initial water flux, roughness, contact angle and zeta potential) using MLR methods.


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Using Nonlinear Regression to Determine K and J/Jo, Plateau for the Test Membranes

For each of the five membranes evaluated here, the membrane performance data collected at the UNR laboratory were used to determine J/Jo as a function of clay impinging on the membrane (g m-2), and these data fitted to Equation 3.3 using the Method of Marquardt (Statgraphics Centurion VX, Statpoint, Inc., Herndon, VA). The normalized permeate flux decline data that was collected for each membrane generally could be well-fitted to the log decay equation (Eq. 3.3), with R-squared values ranging from 74 to 98%. Based on the fitted K values for each membrane, it appears that the ESNA-1LF membrane suffered the most severe initial fouling from the bentonite clay (i.e., the steepest initial slope in the plot of J/Jo as a function of the clay loaded), followed by NF-270, ESPA-2 and SW30HR membranes. The MUNIRO-400 appeared the most resistant to initial fouling by the clay particles. This point may be best illustrated through inspection of Figure 3.31 where the data for the NF-270 membrane shows a much steeper slope in the initial portion of the curve (Figure 3.31a) when compared to that for the MUNIRO-400 (Figure 3.31b).

ClayLoad

Intrin

sic

Wate

r Flu

x

0 30 60 90 120 150

0.56

0.66

0.76

0.86

0.96

1.06

Figure 3.33. Intrinsic water flux (J/Jo) as a function of the clay loaded (g m2) for the a) NF-270 and b) MUNIRO-400 membranes. Lines show Eq. 3.3 fitted to the data by nonlinear regression.

With regards to the resulting J/Jo, Plateau values for the different membranes tested here, it appeared that the lowest value (i.e., the greatest overall reduction in plateau permeate flux) was observed for the SW30HR membrane, followed by the MUNIRO-400 and ESPA-2/NF-270 membranes (Table 3.16). Note that the J/Jo, Plateau is a different measure of the impact of membrane fouling on membrane performance than is the previously discussed K value. The J/Jo,

Plateau corresponds to the overall flux loss encountered by a membrane upon fouling, while the K value corresponds to the rate of initial flux loss. The least overall reduction in plateau permeate flux (i.e., the highest J/Jo, Plateau value) was observed for the ESNA-1LF membrane. The resulting best fit values of K and J/Jo, Plateau for all of the test membranes, along with the 95% asymptotic confidence intervals for the regression models, are summarized in Table 3.16.

ClayLoad

Intrin

sic

Wate

r Flu

x

0 20 40 60 80 100 120

0.46

0.56

0.66

0.76

0.86

0.96

1.06a) b)


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Table 3.17 Values of K and J/Jo, Plateau obtained by fitting normalized permeate flux (J/Jo) data and clay

loading (g m-2) data for each membrane to equation Eq. 3.3. R-squared values and 95% asymptotic confidence intervals are reported for each nonlinear regression membrane

model.

Membrane K +/- Asymptotic

95% Confidence Interval

J/Jo Plateau +/-Asymptotic

95% Confidence Interval

R2 J/Jo vs. Clay Load g m-2

NF-270 1.24E-01 1.88E-02 0.490 0.040 98.5% ESNA-1LF 1.64E-01 7.98E-02 0.639 0.107 74.5% ESPA-2 1.20E-01 2.12E-02 0.490 0.047 98.0% SW30HR 1.58E-03 5.10E-04 -0.020 0.012 95.3%

MUNIRO-400 7.82E-03 9.74E-04 0.013 0.019 99.0%

Relationship Observed between K and J/Jo Plateau and a Hypothetical Explanation

Interestingly, the J/Jo Plateau values are relatively strongly positively and linearly related to the K values for all the membrane exemplars used in the study (Figure 3.32), (J/Jo Plateau = 3.96*K, R-squared = 99.64%). This suggests that the membranes that initially fouled the quickest with bentonite clay also tended to maintain greater permeate flux once the clay cake equilibrated on the membrane surface. This suggests a strong link between rate of loss of water flux upon clay loading and the observed permeability of the clay cake at equilibrium.

Figure 3.34. Linearly regressed relationship between J/Jo and K. The intervals are the asymptotic 95% confidence intervals from the individual nonlinear models.


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The observed relationship between the K and J/Jo Plateau values could be most readily explained by a fouling mechanism in which membranes with larger K values tend to more rapidly accumulate thicker, but more water permeable cakes (clay platelets rapidly accumulate but in highly haphazard structures), which when equilibrated with cross flow shear result in a more permeable clay layer on the membrane (greater J/Jo Plateau values). Membranes that exhibit lower K values, by contrast, would tend to accumulate platelets more slowly, but the cake formed by these platelets would be less water permeable (the platelets would tend to closely align in parallel). As a result of this sort of accumulation, the cake resulting at equilibrium would tend to be far less permeable, even though it grew more slowly. The observed laboratory data readily fit this bentonite clay fouling model, and it will be considered as a good basis for explaining membrane surface interactions with bentonite clay.

Relating K and J/Jo, Plateau to Membrane Properties by Multiple Linear Regression

Since the membranes were all operated using a similar test configuration (swatch size, feed spacer geometry, etc.) with a similar feed water matrix (including the clay concentration), at a similar pressure and with similar crossflow velocity, it is not unreasonable to presume that differences observed with respect to K and J/Jo Plateau were thus related solely to differences in the membranes, and likely could be explained by one or more of the fundamental membrane properties (initial water flux, roughness, contact angle and zeta potential) by MLR analysis using the previously determined membrane properties as the independent variables. Due to the paucity of exemplars for model construction, membrane properties chosen for inclusion in MLR analysis were pre-screened using simple regression analyses with the K or J/Jo, Plateau value as the dependent variable, and the best correlated membrane properties were subsequently selected for MLR modeling (data not shown). The resulting membrane properties chosen for use in MLR analysis are summarized in Table 3.16.

Table 3.18 Membrane properties selected as independent variables for MLR analyses of K and J/Jo,

Plateau . The zeta potential values were determined at pH=5.3, 2mM KCl saturated with ambient CO2. Surface roughness statistics were collected by the OCWD, while zeta

potential and water contact angle values were collected by UNR. Note: Rq was slightly cross-correlated with Jo (r=0.5206) and with θ (r=0.5084).

Membrane Initial Water

Flux, Jo

L m-2 hr-1

Rq nm

ζ mV

θ Degrees

NF-270 21.34 3.8 ± 1.6 -29.4 ± 3.3 57.4 ± 4.7 ESNA-1LF 20.75 36.6 ± 5.6 -29.3 ± 2.5 26.5 ± 5.6 ESPA-2 20.64 38.8 ± 9.9 -21.5 ± 1.9 37.4 ± 4.0

SW30HR 6.82 84.5 ± 8.5 -23.4 ± 2.2 33.9 ± 5.4

MUNIRO-400 31.54 40.7 ± 4.3 -25.6 ± 3.5 46.9 ± 6.8


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K Related to Membrane Properties by MLR Analysis

Relationships between the K values shown in Table 3.16 and the membrane properties reported in Table 3.16 were investigated using MLR analysis. The best model resulting from this analysis included as independent variables membrane surface roughness (Rq/nm), initial water flux (Jo) and contact angle [θ] (degrees) as shown in Equation 3.4 Note that the K value was found to be insensitive to membrane zeta potential and was thus not included in Equation 3.4.

( ) ( ) ( )θ*0.00527614 -J*0.0034793-R*0.00368041 - 0.517426 K oq= 3.4

The R-squared value for this MLR model (Eq. 3.4) was 98.82%, the adjusted R-squared was 95.27% and the p-value was 0.1363 (model was significant only at an 86% confidence level). The observed and predicted K values for each of the different membranes that were fouled using the bentonite clay plotted as a function of one another are shown in Figure 3.34.

Figure 3.35. Plot of the observed and predicted K values for each of the different membranes fouled by bentonite clay. R-squared = 98.82%, adjusted R-squared = 95.27%, p-value = 0.1363. The value of K determined in the nonlinear regression models for the individual membranes loaded with bentonite clay could be predicted well from roughness, initial water flux and contact angle data. Zeta potential data were not required for the prediction (probably due to small variation amongst the test membranes).

Membrane zeta potential was not required as a model input to describe the observed variations in K. This may not be so surprising, as at the pH with which the clay loading experiments were performed the zeta potentials of these membranes did not vary over nearly as great a range as the other membrane parameters (Table 3.16). Therefore, there was likely not enough variation in the membrane zeta potential values to allow the model to differentiate the observed permeate flux decline results.

predicted

observ

ed

0 0.03 0.06 0.09 0.12 0.15 0.18

0

0.03

0.06

0.09

0.12

0.15

0.18

ESNA-1LF

NF-270

ESPA-2

MUNIRO-400

SW30HR


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Analyses of component effects revealed that the K value was most influenced by membrane surface roughness [Rq; -0.163 (SW30HR) to +0.134 (NF-270)], second most affected by contact angle [θ, degrees; -0.0923 (NF-270) to +0.0670 (ESNA-1LF)] and least influenced by the initial permeate flux rate [Jo; -0.0420 (MUNIRO-400) to +0.0444 (SW30HR)]. Membrane surface roughness was negatively related to the K value, meaning that smoother membranes experienced the most rapid decline in permeate flux. The contact angle with water (θ) was also negatively related to the K value, indicating that more hydrophobic membrane surfaces were characterized by a more gradual declines in permeate flux upon fouling with the bentonite clay (lower fouling rates). Finally, the initial membrane flux Jo was negatively related to K; indicating membranes with higher initial flux rates exhibited greater flux decline rates.

Clearly membrane properties could be successfully related to the bentonite clay fouling coefficient K, albeit at less than a 95% confidence level. Due to the potential for complex particle interactions in bentonite clay cakes and a lack of additional data regarding the structure of the cakes on the membrane surfaces, it is difficult to indicate with certainty what mechanism(s) may be responsible for the relationship. However, if the relationship between K and J/Jo Plateau that was hypothesized earlier is considered, then some explanations for the relationship between membrane properties and the observed K values might include the following:

• Surface Roughness

•

: Studies with other classes of colloidal foulants (see e.g., Zhu and Elimelech, 1997) have indicated that rougher membrane surfaces tend to be characterized by greater foulant accumulation (enhanced fouling rates). In this case, the opposite scenario appeared to occur. It is important to note that many earlier studies evaluated fouling in terms of a net permeate flux loss rather than the initial decline in flux (i.e., what is discussed here in terms of a K value). Analysis of component effects shows that K was increased by a decrease in membrane roughness (less loss of permeate water flux). This relationship between membrane roughness and K is consistent with the findings of the previous MLR analysis in this report in which J/Jo was modeled at a constant clay loading rate of 5 g m-2. If, as hypothesized, larger K values correspond to more rapid accumulation of a porous bentonite clay cake, then apparently the clay platelets more rapidly attached to, and form organized structures on, the smoother membrane surfaces as compared to the rougher ones. Bentonite clay platelets are unusual colloidal particles in that they are extremely thin mineral plates as opposed to more globoid structures. The flat surfaces are negatively charged, which would be repelled by the membrane, but the edges actually have a positive charge, which could be attracted to it and allow the platelets to sit edge-on to the membrane surface if is smooth enough to allow sufficient platelet perimeter contact to hold the particle in place (this is also a mechanism by which platelets can orient 90 degrees to each other in clay gels). Possibly with a smoother membrane, platelets could better approach the surface edge-on; rougher surfaces might hamper the ability of the thin plate-like clay platelets to bind, and the negative surface charges would then more effectively repel them. An edge-on orientation would also favor the buildup of a more randomly ordered (and water permeable) cake structure that would ultimately lead to greater water permeability at equilibrium seen in the membranes with larger K values.

Contact Angle with Water (Hydrophobicity/Hydrophilicity): Analysis of component effects suggested that contact angle was negatively related to K, suggesting that more hydrophilic membranes (smaller contact angles with water) tended to favor more rapid


84

water flux loss for a given clay load. If the previous hypothesis regarding bentonite clay cake formation and water permeability are considered, this suggests that more hydrophilic surfaces tend to also be associated with a more rapid accumulation of clay platelets. Hydrated bentonite clay platelets have a surface layer of water associated with them, making them effectively very hydrophilic, and theoretically capable of participating in hydrogen bonding as well. Thus, the clay platelets would tend to more quickly and firmly associate with more hydrophilic surfaces and as a result may accumulate more rapidly than on more hydrophobic membrane surfaces (surfaces with larger contact angles) for a given clay loading in the feed. The hypothesis regarding clay platelet accumulation at larger K values also suggests that the water permeability of the rapidly formed cake should be greater (the cake composed of more randomly oriented platelets), but unlike with surface roughness, it’s less clear how the nature of surface binding alone can influence platelet orientation. One complicating factor is that membrane roughness is slightly cross-correlated with contact angle, so that some of the apparent relationship between contact angle and K may be mediated via covariance with roughness. If, however, contact angle is omitted from the MLR model as an input, the R-squared value is seriously degraded (data not shown).

• Initial Permeate Flux Rate: Analysis of its component effect suggested that K is positively influenced by lower initial permeate flux (Jo) values. As with contact angle, the initial water flux was cross-correlated with membrane roughness (r = 0.5206), and so it’s possible that some of the apparent influence of initial water flux is complicated by this relationship. However, removal of water flux from the MLR model resulted in significant degradation of R-squared (data not shown). If the hypothesis that higher K values correspond with more rapid accumulation of a more porous cake is considered, this would suggest that increased deposition of more randomly oriented clay platelets is associated with lower initial membrane water flux. This relationship at first seems counterintuitive. A previously made observation for negatively charged colloidal silica particles suggests that accumulation on a membrane surface is a balance between transport to the membrane surface by permeate flow (permeation drag) and repulsion at the surface by the electric double layer (Zhu and Elimelech, 1997). In this case, it appears that with higher Jo the resulting bentonite cake is either more permeable or it forms more slowly. One possible explanation would be that if platelets are brought slowly to the membrane by lower initial water fluxes, they can more readily associate edge-on and form the foundation for the more porous cake structure. On the other hand, if they are forced in more rapidly with a higher water flux, the tendency may be to turn face-on, exposing the negatively charges platelet surface to the membrane, which doesn’t as strongly associate with the surface. Unfortunately, since the geometry of deposition of bentonite platelets were not elucidated in this study, the apparent effects of water flux on K will require future more scrutiny for elucidation.


85

J/Jo Plateau Related to Membrane Properties by MLR Analysis

As previously stated, the plateau value is zero order with respect to clay impinged on the membrane surface, and appears most likely to represent a final equilibrium between accumulation of clay at the membrane surface by convective transport and removal of clay from the surface by cross flow hydrodynamic shear. Also, the water transport properties through the clay layer at this equilibrium are expected to be important to determining the observed value of J/Jo Plateau. Since it has been shown that J/Jo Plateau is essentially a linear function of K, it is anticipated that all of the membrane properties identified by MLR analysis as predictive of K will also predict J/Jo Plateau.

A similar MLR approach as was used to determine the relationship between membrane properties and K was also performed with the J/Jo Plateau values identified in the nonlinear regression membrane models. Since the fouling tests were done using similar test conditions (cross-flow velocity, electrolyte concentrations/types, and solution pH) the principal differences observed in J/Jo Plateau were presumed to likely be due to membrane properties alone (the structure of the resulting cake influenced by the deposition of the first layer on the membrane surface). As before, preliminary simple regression was used to screen the potential input variables, resulting in those shown in Table 3.16. Following this, construction of MLR models using these input variables revealed the best to be as follows:

( ) ( ) ( )θ*0.0212567 -J*0.0141576-R*0.0150848 - 2.0847 JJ

oqPlateau,o

= 3.5

The R-squared value for Equation 3.5. was 97.83%, the adjusted R-squared was 91.33% and the p-value was 0.1843 (the model was significant only at an 82% confidence level). The observed and predicted J/Jo Plateau values for each of the different membranes that were fouled using the bentonite clay are shown Figure 3.31. As was anticipated since it was shown that K and J/Jo Plateau appear to be mathematically linked, the same three membrane properties (Rq/nm surface roughness, contact angle with water, and Jo) that best explained the variance in the K value also best explained the variance in the observed J/Jo Plateau value. Zeta potential was again not required for a good model fit, likely for the same reason that it did not vary enough to play a role in clay interaction with the chosen test membranes.


86

Figure 3.36. Plot of the observed and predicted J/Jo Plateau values for each of the different membranes fouled by bentonite clay. R-squared = 97.83%, Adjusted R-squared = 91.33%, p-value = 0.1843. The MLR model was able to describe the majority of the variance in J/Jo Plateau using roughness, contact angle and initial water flux. As with K, zeta potential data were not required to explain the variance observed in J/Jo Plateau.

As with K, component effect analysis revealed membrane surface roughness to be the most influential membrane parameter related to J/Jo Plateau [-0.671 (SW30HR) to +0.545 (NF-270)]. Surface roughness was negatively related to J/Jo Plateau (the rougher the membrane, the less the J/Jo Plateau). Contact angle was the second most influential parameter [-0.376 (NF-270) to +0.260 (ESNA-1LF)]. Contact angle with water was also negatively related to J/Jo Plateau, suggesting that more hydrophilic membranes tended to favor higher water flux at clay loading equilibrium than more hydrophobic ones. The initial permeate water flux (Jo) was observed to be the least influential component [-0.175 (MUNIRO-400) to +0.177 (SW30HR)] and was also negatively related to J/Jo Plateau, suggesting that membranes exhibiting higher initial water fluxes tended to lose intrinsic flux the most when clay loading reached equilibrium, even though they tended to lose permeate water flux more slowly (lower K values) when initially loaded.

The relationships observed here tend to support the previously discussed hypothesis that membranes that were observed to lose water flux most rapidly were quickly accumulating a more porous bentonite clay cake (due to a combination of lesser surface roughness, greater surface hydrophilicity and lower initial water flux), and with continued clay load these membranes formed more water porous clay cakes that, when equilibrated with membrane hydrodynamic factors (cross flow shear) tended to exhibit greater water production (less fouling). On the other hand, membranes that fouled more slowly with bentonite clay did so by more slowly accumulating a less water permeable cake, so that when equilibrium was reached, the membrane water flux was significantly reduced (to near zero in some cases). The hypothesized fouling mechanisms are shown in Figure 3.36. The edges of the platelets (positively charged), are attracted to the negatively-charged membrane surface. The “flats” of the platelets are negatively charged and are repelled by the membrane surface. With less featured surfaces, the edges are

predicted

observ

ed

-0.02 0.18 0.38 0.58 0.78

-0.02

0.18

0.38

0.58

0.78ESNA-1LF

NF-270

ESPA-2

MUNIRO-400

SW30HR


87

more likely in contact with the membrane surface, and the slower flux allows the platelets to remain upright. The upright platelets result in a more open cake layer with greater water permeability. With rougher surfaces, the likelihood of the edges remaining in contact with the membrane surface decreases; in the case of platelets that do maintain edge contact, the higher water flux likely pushes them over. When the flats of the platelet are in contact with the membrane surface, a more planar cake layer accumulates and has less permeability. It's simplistic, but it does fit the fouling kinetic observations.

Figure 3.37. Mechanistic explanation of fouling by bentonite clay.

As with the case in which a constant bentonite clay load was investigated, the kinetics describing initial loss of membrane permeate water flux upon addition of a foulant material (K) and equilibrium water flux J/Jo Plateau could be nearly completely explained in terms of the membrane properties, again highlighting the importance of these parameters as predictors of membrane performance.

Modeling Conclusions

Water treatment membranes can be fouled with a fairly wide variety of materials, including biological substances (biofilms consisting of whole bacteria and bacterial extracellular polysaccharides and nanodebris), dissolved and colloidal organic substances (e.g., humic and fulvic acids) and mineral materials (colloidal clay nanoparticles and mineral precipitates). These foulant materials associate with membrane surfaces by a variety of mechanisms including hydrodynamic forces, friction, surface hydrophilic/hydrophobic interactions, surface ionic charge, and van der Walls interactions. Bentonite clay represents a surrogate for one class of membrane foulant. It is ideal for laboratory study as it is easy to handle and membrane exposure is easy to quantify, also it can be used to demonstrate in principal how accumulation of foulant, and the related loss of membrane performance, can be related to fundamental membrane surface properties. This study does successfully demonstrate how, under a defined set of matrix


88

conditions, membrane surface properties can be used to predict membrane performance in presence of a foulant material.


89

CHAPTER 4 CONCLUSIONS

DEVELOPMENT OF STANDARD TECHNIQUES FOR CHARACTERIZING MEMBRANE SURFACES

The following conclusions were reached regarding the development and application of standard techniques for characterizing membrane surfaces, and specifically for executing surface characterization using contact angle, surface roughness, and streaming potential measurements:

• Contact mode produces more consistent roughness statistics when imaging the outside of tubular structures. Therefore, when imaging hollow fiber membrane surfaces, whether the concave or convex surface, the AFM should be operated in contact rather than tapping mode.

• A linear relationship exists between the root mean square and average roughness statistics. Therefore, both parameters may be equally applied for drawing correlations between membrane surface morphology and membrane fouling.

• The inherent physical heterogeneity of membrane surfaces requires that greater than three sites be imaged (i.e., their roughness measured) in order to develop an accurate assessment of membrane surface roughness.

• Surface roughness measurements using the AFM technique is a precise measure of membrane surface morphology based on the reproducibility and repeatability of surface roughness statistics between two laboratories. The precision of the measurement is, however, dependent on the two laboratories using the standard technique.

• Using the standard technique, the captive bubble method produces precise contact angle results for membrane surfaces with a repeatability and reproducibility between any two laboratories of approximately 5°. This level of precision allows for equal comparison of relative assessments of membrane surfaces with regards to their hydrophobicity (i.e., is the membrane hydrophobic or hydrophilic). However, the current level of precision does not lend itself well to the comparison of surface energy parameters calculated between two laboratories using contact angles measured at each laboratory. Further study is needed to determine how variations in contact angle results affect the magnitude and sign of surface energy parameters determined through contact angle analysis to establish the broader significance of these values to the membrane community (i.e., are these surface energy parameters useful in membrane operations?).

• The proposed standard method for determining the zeta potential of membrane surfaces using streaming potential measurements produces relatively reproducible results independent of the type of electrokinetic analyzer used. However, minor deviations from the proposed standard method (e.g., differences in sample pretreatment) can result in substantial errors.


90

• The availability of characterization equipment and operator skill level requirements are the greatest impediment to membrane characterization tests being performed by utilities.

• From the perspective of utilities, characterization results, such as contact angle, surface roughness, and streaming potential, are only useful during membrane selection and lose their usefulness once membranes are put into service. A key challenge that was identified in this regard is how to determine membrane characteristics without damaging the membrane modules or elements (i.e., how to determine these characteristics without conducting a destructive autopsy).

CORRELATION BETWEEN MEMBRANE PROPERTIES AND MEMBRANE FOULING

• The loss of normalized permeate water flux (J/Jo) when the five test membranes were fouled by bentonite clay particles (representative of negatively charged plate-like colloidal particles) at constant pressure could be modeled statistically using linear regression methods; the resulting statistical models could then be used to express normalized permeate water flux as a function of constant clay exposure. The decline in normalized permeate flux as a function of clay loading followed a log-decay function. Furthermore, the normalized permeate flux declined to some minimum, but steady-state, value with respect to clay loading.

• It was possible to construct a multiple linear regression (MLR) model capable of describing >97% of the observed variance in normalized permeate water flux. Additionally, a nonlinear regression analysis using a log-decay function found that membrane performance could be successfully described in terms of a decay parameter (K) and a minimum normalized permeate flux (J/Jo,Plateau) parameter. It was hypothesized that the K value is related to the rate of accumulation of clay platelets at the membrane surface, while the J/Jo Plateau value represents a state of equilibrium between clay accumulation and removal by cross flow shearing. Because the K value describes the initial fouling of the membrane surface it was anticipated to be significantly influenced by membrane surface properties.

• Membrane properties (Rq and contact angle with water) and the initial permeate water flux alone were capable of describing the observed fouling behavior of the five different membranes by bentonite clay. Zeta potential was not required; however, the variance of zeta potential among the test membranes was the least of the membrane parameters, and it’s likely that its influence on clay interactions with the test membranes was thus not well represented in the study. A similar relationship was observed for the K value, which was shown by MLR analysis to be largely a function of membrane surface roughness, contact angle with water (hydrophobicity/hydrophilicity), and initial permeate water flux (in order of greatest influence to least influence). This implies that membrane surface roughness and hydrophobicity have a greater influence on bentonite clay cake density/water permeability than does membrane surface charge.

• It was not possible to eliminate significant inter-correlation of input variables and the resulting model is only significant at ~90% confidence interval; however, validation tests


91

performed by iteratively withholding data for one membrane suggest this MLR approach is capable of predicting membrane behavior.

• Overall, better performance (higher observed normalized flux after 5 g/m2 bentonite loading) was associated with greater RMS roughness and higher initial water flux and greater contact angle (greater membrane hydrophobicity).

• Although bentonite clay only represents one class of membrane foulant, the MLR modeling effort illustrates how measurements of fundamental membrane surface properties (roughness, hydrophobicity and surface charge) could be used to predict membrane performance in the presence of any type of foulant whose adhesion/accumulation depends on these membrane properties.


92

APPENDIX A

List of Utilities Using Membranes as a Treatment Technology (as of 2010)


93

MF Membrane Installations Pall Microza source: Pall Installation

Start-up List Jan2008.pdf

Owner & Location Project Water Being

Treated

Capacity (MGD)

Start-Up Date

Fridley Membrane Filtration Plant Minneapolis, MN

Municipal WTP Microza MF Surface Water 95 TBD

Johnson County, KS

Municipal WTP Microza MF Ground Water 31 Apr-09

City of North Bay, Ontario

Municipal WTP Microza MF Lake Water 21 Dec. 2007

California Water Service Company Bakersfield, CA

Municipal WTP Microza MF River Water 20 March 2003

Stones River WTP City of Murfreesboro, TN Municipal WTP Microza MF Lake Water 20 Apr-08

City Of Clovis, CA

Municipal WTP Microza MF River Water 15 Sept 2004

Saratoga County Saratoga, NY

Municipal WTP Microza MF River Water 14 Feb-08

City of Olathe, KS

Municipal WTP Microza MF

Ground Water under the influence 13 Dec 2005

City of Westminster Westminster, CO

Municipal WTP Microza MF Lake Water 12 (Peak) Oct. 2001

Tuscaloosa, AL

Municipal WTP Microza MF Lake Water 12 Est. 6/08


94

McCrosky Island WTP Sevierville,TN

Municipal WTP Microza MF River Water 12 Dec-06

Yucaipa VWD Yucaipa CA

Municipal WTP Microza MF Lake Water 12 Apr-07

Guadalupe-Blanco River Authority GBRA/Western Canyon Water, New Braunfels, TX

Municipal WTP Microza MF River Water 10 Dec 2005

City of Temple Temple, TX


Raw Lake Water 10 Oct. 2003

South Blount Utility District Maryville, TN


Raw Lake Water 10 June 2004

City Of Los Angeles Dept Water & Power Encino, CA

Municipal WTP Microza MF Reservoir Water 9.80 Sept 2005

City of Abilene Abilene, TX


Direct Coagulated Lake Water

8.0 June 2002

Travis County # 17 Austin, TX

Municipal WTP Microza MF Lake Water 8.0 June 2002

San Patricio Municipal W. D. Ingleside, TX


Coagulated, Clarified

Surface Water 7.8 Jan. 2000

Brushy Creek Municipal Utility District, Bushy Creek, TX

Municipal WTP Microza MF Lake Water 6.00 Oct 2005


95

City of St. Helens, OR


River/Creek Water 5.95 March 2006

City of Salmon ID


River/Creek Water 5.2 Feb 2006

Upper Eagle Regional Water Avon, CO

Municipal WTP Microza MF River / Stream 5.0 Nov. 2002

Town Of Orleans MA


Ground WaterIron & manganese Removal 4.5 April 2005

Alamitos Barrier, CA


Secondary Effluent Pre-ro

3.5 June 2001

City of Brady TX

Municipal WTP Microza MF Surface Water 3.30 Oct 2005

Town of Petrolia ONT


Raw Lake Water 3.17 Feb 2005

Sonoma County Water Agency Santa Rosa, CA


Secondary Effluent 3.0 May 2002

Travis County # 18 Austin, TX


Coagulated Settled Water 3.0 Nov. 2003

Washington City WTP Washington City, UT

Municipal WTP Microza MF Reservoir Water 3.0 July 2003

Amador Water Agency, Buckhorn WTP Pioneer, CA

Municipal WTP Microza MF Surface Water 3.0 April 2005


96

Holliday Water Co. Holladay, UT

Municipal WTP Microza MF Spring Water 2.5 March 2002

Town of Newton Newton, NJ

Municipal WTP Microza MF Lake Water 2.25 March 2005

Lost Creek UT

Municipal WTP Microza MF Ground Water 2.0 Jan 2005

Town of Byrdstown Byrdstown, TN


Flocculated Raw Surface

Water 2.0 December

2005

Garden City, UT Municipal WTP Microza MF GWUDI 2.0 November2006

Fountain Hills Sanitation District Fountain Hills, AZ


Tertiary Effluent for Reuse 2.0 March 2001

City of Chandler Chandler, AZ


Semiconductor Wastewater Reclamation

1.75 Nov. 97

Solano Irrig. Distr. Vacaville, CA


River Water 1.4 April 2001

City of Flowery Branch, GA

Municipal WWTP Microza MF

Secondary Effluent 1.3 Nov 2005

Town Of Whitecourt, AB


Coagulated Settled Surface Water 1.3 Jan 2005

Crested Butte WTP Crested Butte, CO

Municipal WTP Microza MF Creek Water 1.25 March 2002


97

Chalmette Refining LLC, Chalmette, LA

Industrial Microza MF River Water 1.15 Feb 2005

Mustang Utility District. CO


Direct Coagulated Raw

Water 1.15 Dec 2004

Lee County VA

Municipal WTP Microza MF Surface Water 1.04 Feb 2004

City of Parsons Parsons, KS

Municipal WTP Microza MF River Water 1.0 May 2003

Bedford Hills Correctional Facility Bedford Hills, NY

Municipal WTP Microza MF Wastewater 1.0 Sept. 2002

City of San Marcos San Marcos, TX

Municipal WTP Septra CB

Groundwater Under the Influence 1.0 July 2002

Lakeview Utility District, Rogersville, TN


Groundwater Under the Influence

1.0 December 2005

Sherkston Shores Resort Pt. Colborne, ONT

Municipal WTP Microza MF Surface Water 1.0 Apr. 2003

Rutherford County Murfreesboro, TN

Municipal WWTP Microza MF

Backwash Water 1.0 Dec 2005

City of Caney KS

Municipal WTP Microza MF Surface Water 0.72 Apr 2005

Hantsport Nova Scotia


Coagulated Water 0.72 March 2004

Upper Surface Creek, CO

Municipal WTP Microza MF 0.72 Jan 2005


98

Forestville County Sanitation District Forestville, CA


Secondary Effluent 0.58 Sept. 2003

Hoopa Valley, CA

Municipal WTP Microza MF Surface Water 0.57 May 2005

Bedford PSA, VA

Municipal WTP Microza MF Ground water 0.548 Aug 2005

Raccoon Creek, PA

Municipal WTP Microza MF Lake Water 0.5 Dec 2004

California Water Service Company Kernville, CA


Wheelabrator Pinellas, FL

Industrial Waste Microza MF

Secondary Effluent 0.5 June 2002

Young’s River /Lewis & Clark District Astoria, OR

Municipal WTP Microza MF River Water 0.5 Nov. 2001

Town of Basalt WTP Basalt, CO

Municipal WTP Microza MF Spring Water 0.5 May 2002

City of Red Boiling Springs TN


BoWater WTP Calhoun TN

Plant Drinkwater Microza MF

Surface 0.5 Dec 2004

Village of Alix Alix, AB

Municipal WTP Microza MF River Water 0.5 Oct. 2002

City of Meeteetse Meeteetse, WY

Municipal WTP Microza MF Reservoir Water 0.3 / 0.6 April 2001


99

Village of Hobart Hobart, NY

Municipal WTP Microza MF Wastewater 0.47 May 2002

Bruce Mines WTP Bruce Mines, ON

Municipal WTP Microza MF Lake Water 0.44 March 2003

Village of Groton NY


Stoney Creek Municipal Authority Basye, VA


Ground Water Under the Influence 0.36 Nov. 2002

Coast Springs CA

Municipal WTP Microza MF Ground Water 0.36 June 2004

Manitoba Hydro ONT Microza MF Surface Water 0.36 Feb 2004

No. Slope Borough Wainwright, AK


No. Slope Borough Nuiqsut, AK

Municipal WTP Microza MF Lake Water 0.35 Aug. 2001

No. Slope Borough Point Hope, AK


Westover Westover, PA


Mt. Rainier National Park Srvc. Ashford, WA


Questa La Honda, CA

Municipal WTP Microza MF Surface Water 0.14 Feb 2005

No. Slope Borough Kaktuvik, AK



100

No. Slope Borough Point Lay, AK


No. Slope Borough Atqasuk, AK


Hot Springs, AR


Spring Water for Bottling 0.05 Aug 2005

Masonville NY


Secondary Effluent 0.01 Aug 2004

Hite Marina Lake Powell, UT


Oregon Parks & Recreation Dept. Beverly Beach, OR

Municipal WTP Microza MF Creek Water 0.1 Oct. 1999

Oregon Parks Bullards Beach, OR

Municipal WTP Microza MF Creek Water 0.1 Jan. 2000

Russian River Util. Forestville, CA

Municipal WTP Microza MF Surface Water 0.1 Nov. 2000

Jim BridgerPoint of Rocks, WY Municipal WTP Microza MF River Water 0.1 Feb. 2002

Kennecott Greens Creek Mining Co. Anchorage, AK

Municipal WTP Microza MF Drinking Water 0.04 Nov. 2002

Burleigh Island Lodge Burleigh Falls, ON


Camp L’Man Achai WWTP NY


Secondary Effluent 0.00080 June 2004


101

SIEMENS Water Technologies Corp. GLOBAL MEMCOR® PRODUCTS INSTALLATION LIST

Project Name Country City State Feed Source

BlueScope Steel - No. 2 Blower Station AUS NSW Town Mains

BlueScope Steel - No. 2 Blower Station AUS NSW Town Mains

Cadbury Schweppes, Liverpool AUS NSW Town Mains

Cerestar FRA ROW Surface Water

Crackenback AUS NSW Surface Water

Dunedin NZL Dunedin Surface Water

Durango LaPlata Airport USA Durango CO Surface Water

Estero Mutual Water Co. (Dillon Beach) USA Dillon Beach, CA CA Surface Water

Green Spring Valley USA WI Surface Water

Kansas City Power & Light USA Clinton, MO MO Surface Water

Kwinana Water Reclamation Plant AUS Kwinana WA Secondary Effluent

LG Phillips P6 SKO Paju ROW Industrial Process

Metropolitan Water Dsitrict USA Yuma AZ Surface Water

Oklahoma Gas & Electric USA Konawa, OK OK Surface Water

Sears Point Raceway (Infineon Raceway) USA Sonoma , CA CA Surface Water


102

Severn Trent, Homesford WTW UK Derby ROW Ground Water

Southern Water, Arundel WTW UK Arundel ROW Ground Water Tarong PS AUS Kingaroy QLD Industrial Process Walden USA Walden CO Surface Water Xiang Yang Lu CHN Tianjin ROW Secondary Effluent Bolinas Community Public Utility District USA Bolinas, CA CA Surface Water Bundamba 1A AUS Brisbane QLD Secondary Effluent

Butano Canyon Water Company USA Pescadero, CA CA Surface Water

California Department of Parks (Gaviota State Park) USA Goleta, CA CA Surface Water

Carmichael Water District USA Carmichael CA Surface Water

Caseville WTP, Village of USA Caseville, MI MI Surface Water

Coca Cola - West Memphis USA AR AR

Cucamonga Water District USA Rancho Cucamonga, CA CA Surface Water

Duckmaloi AUS NSW Surface Water

Gore Valley (Eagle River) USA Vail, CO CO Surface Water

Hasting Council - Comboyne WTP AUS Comboyne NSW Surface Water

Hasting Council - Long FLat WTP AUS Long Flat NSW Surface Water

Hasting Council - Port Macquarie AUS Port Macqurie NSW Secondary Effluent

Hasting Council - Telegraph Point WTP AUS Telegraph Point NSW Surface Water


103

Hasting Council - Wauchope WTP AUS Wauchope NSW Surface Water

Idaho Springs, City of USA Idaho Springs, CO CO Surface Water

Isle Royale National Park USA Mott Island MI Surface Water

Linwood Metropolitan Water District USA Linwood, MI MI Surface Water

Los Alamos Radioactive Treatment Facility USA Los Alamos, NM NM Other

Lower Brule Sioux Tribe USA Lower Brule, SD SD Surface Water

Mackinac Island USA Mackinac Island, MI MI Surface Water

Marquette, City of USA Marquette, MI MI Surface Water

Metropolitan Water District LaVerne USA La Verne, CA CA Surface Water

Oamaru NZL Oamaru Surface Water

Portsmouth Water, Farlington WTW UK Portsmouth ROW Surface Water

Portsmouth Water, Farlington WTW UK Portsmouth ROW Surface Water

Portsmouth Water, Lovedean WTW UK Portsmouth ROW Ground Water

Portsmouth Water, Lovedean WTW UK Portsmouth ROW Ground Water

River Mountain WTF USA Henderson, NV NV Surface Water

Rojana Power THL ROW

Scotish Water, Invercannie WTW UK Aberdeen ROW Surface Water

Thames Water, Addington WTW UK Croydon ROW Ground Water Willaura AUS VIC Surface Water

Carbondale, Town of USA Carbondale CO Ground Water


104

County of Maui (Olinda WTF) USA Maui HI Surface Water

Algonquin, Village of USA Algonquin, IL IL Ground Water Carter Lake USA Berthoud, CO CO Surface Water

Erie, Town of USA Erie, CO CO Surface Water

Kennewick WTP USA Kennewick, WA WA Surface Water

Manitowoc Public Utilities USA Manitowoc, WI WI Surface Water

St. Johns - New Foundland CAN New Foundland NF

Carmichael Water District USA Carmichael CA Surface Water

Delta Centrifical USA Temple TX Surface Water

Indian Hills USA Indian Hills CO Surface Water

Kaneka BEL ROW Surface Water

Newell-Warnock, CO USA Loveland CO Surface Water

Point Cabrillio USA Ft Brag, CA CA Surface Water

Ravenna (Greenwood Village) USA Ravenna CO Surface Water

Colorado City USA Longmont, CO CO Surface Water

DeBeque, Town of USA DeBeque, CO CO Surface Water

Eatonville, Town of USA Eatonville WA Surface Water Homestead USA Hot Springs, VA VA Ground Water

Lake Santee WTP USA Lake Santee IN Surface Water

Oak Creek WTP USA Oak Creek CO Surface Water


105

Odana Hills USA Madison WI Surface Water Parachute USA Parachute, CO CO Surface Water

Pine Brook Water District USA Boulder, CO CO Surface Water

Silt, Town of USA Silt CO Surface Water

Albany-Millersburg USA Albany/ Millersburg, OR OR Surface Water Amcor PHL ROW Industrial Process

Amkor Phillipines PHL ROW Industrial Process

Andes WWTP USA Andes, NY NY

Appalachian State Univ. USA Boone NC Surface Water Beenliegh AUS QLD Surface Water Blackmores AUS NSW Boddington Gold AUS WA Ground Water Bogong AUS NSW Surface Water

Brewster (I-684 Restop) USA Brewster, NY NY

Brewster Heights USA Brewster, NY NY

Briarcliff, Village of USA Briarcliff, TX TX Surface Water

Bristol Water, Banwell UK Weston Super Mare ROW Surface Water Bristol Water, Banwell UK Weston Super Mare ROW Surface Water

Byesville WTP USA Byesville, OH OH Ground Water

Cadbury Schweppes, Brisbane AUS QLD Town Mains

Camp Fitch USA North Springfield PA Clear Spring WTP USA MD MD Ground Water Coca Cola - Brisbane AUS QLD Town Mains Columbia WTP USA Boise ID

Cookson WTP USA Cookson, OK OK Surface Water

Corrumbin Eco Village AUS Corrumbin QLD Secondary Effluent


106

Cortez, City of USA Cortez CO Surface Water Cottees AUS Liverpool NSW Town Mains

Crosshouse Hospital, Glasgow UK Glasgow ROW Potable Water

Crow Creek Casino WWTP USA Canyonville, OR OR Surface Water Crusta AUS NSW Industrial Process Detour, Village of USA DeTour, MI MI Surface Water Dunedin Airport NZL Dunedin Secondary Effluent

Environmental Management USA Lemont, IL IL Surface Water

Fort Bidwell USA Ft. Bidwell, CA CA Surface Water

Fort Ross USA Fort Ross, CA CA Surface Water Freshpac AUS Tatura VIC Surface Water Fuxin Jinshan CHN ROW Secondary Effluent Geochang SKO ROW Surface Water

Hallsdale-Powell Utility District USA Knoxville, TN TN Surface Water

Healdsburg USA Healdsburg CA Surface Water

Huron Regional Water Authority USA Port Austin, MI MI Surface Water

Indiana County USA Indiana, PA PA InVista - DuPont USA TX TX Industrial Process Joines Rd USA Brownsville, TX TX Surface Water

June Lake WTP USA June Lake CA Surface Water

Lake Alpine Water Company USA Lake Alpine CA Surface Water

Latrobe USA Latrobe PA

Laura WTP, Cook Shire AUS Laura QLD Surface Water

LeRoy WTP USA Leroy, IL IL Ground Water


107

Lexington, Village of USA Lexington, MI MI Surface Water

LG Micron P3 SKO ROW Industrial Process

Lochgilphead Hospital, Lochgilphead UK ROW Potable water

Logan Todd Regional Water Commission USA Russellville, KY KY Surface Water

London Power Company, Cottam Power Station UK Notts ROW Surface Water

Loxley TRN THL ROW Madisonburg USA Madisonburg PA

Marulan WTP AUS Marulan NSW Surface Water

McConnellsburg USA McConnellsburg PA Monroe WTP USA Monroe, OR OR Surface Water Mt Piper AUS NSW Industrial Process Neskowin USA Neskowin OR Surface Water Nestle Water USA Lee FL Surface Water

Nestle Water USA Red Boiling Springs TN Industrial Process

Noramco USA Ridley Creek PA

North Clackamas County (Sunrise) USA Sunrise, OR OR Surface Water

Nugans AUS NSW Surface Water

Possum Valley Municipal Water USA Bendersville PA

Pottawatomie, City of USA Pottawatomie OK Surface Water

Rix/Munemasa Shuzo JPN ROW Rix/Munemasa Shuzo JPN ROW Scottish Water, Invercannie WTW UK Aberdeen ROW Surface Water

Severn Trent, Homesford WTW UK Derby ROW Ground Water


108

Siemens THL ROW

Siemens THL ROW

Simms Mesa USA Navajo Dam, NM NM Surface Water

St. Johns - New Foundland CAN New Foundland NF

Standish, City of USA Standish, MI MI Surface Water

Tabulam 2nd chance facility AUS NSW

Taiyuan PS No. 1 CHN ROW Secondary Effluent Thames Water, Taplow WTW UK London ROW Ground Water Thames Water, Taplow WTW UK London ROW Ground Water

Three Valleys Water, Batchworth WTW UK London ROW Ground Water

Three Valleys Water, Batchworth WTW UK London ROW Ground Water

Three Valleys Water, Chertsey WTW UK London ROW Ground Water

Three Valleys Water, West Hyde/Mill End WTW UK London ROW Ground Water

Three Valleys Water, West Hyde/Mill End WTW UK London ROW Ground Water

Toshiba/Toyobo/Yamanaka JPN ROW

Tyers AUS VIC Surface Water Victor USA Victor CO Surface Water

Waurika, City of USA Waurika OK Surface Water

WEPCO - Point Beach USA Point Beach, WI WI Surface Water

Wessex Water, Tucking Mill UK Bath ROW Surface Water

Wolf Creek USA OR OR Surface Water


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Ameren Coffeen USA IL Surface Water Arnotts AUS Sydney NSW Industrial Process Barwon Heads AUS VIC Surface Water Barwon Water AUS VIC Secondary Effluent Blackberry WWTP USA Bootawa Dam AUS NSW Surface Water Bundamba 1B AUS Brisbane QLD Secondary Effluent Central Highlands Water AUS VIC Dagang Oil CHN Tianjin ROW Industrial Process DaHua CHN ROW Sea Water Dongshen CHN ROW Secondary Effluent Edinburg, Town of USA Edinburg, VA VA Ground Water Encampment USA Encampment, WY WY Surface Water Ennis Co Council, Ennis IRE Ennis ROW Surface Water Fairfield USA Fairfield, IL IL Surface Water Fargo USA Fargo, ND ND Surface Water Flying J Truck Stop USA Whiteford, MI MI Ground Water Gascoyne Junction AUS WA Ground Water Gibson Island AUS Brisbane QLD Secondary Effluent Guofeng North CHN Guofeng ROW Secondary Effluent Guofeng South CHN Guofeng ROW Secondary Effluent IBM Silicon CHN Shanghai ROW LPE THL ROW Milton NZL Milton Nice Mutual Water USA Nice, CA CA Surface Water Prairie Pride USA Deerfield, MO MO

Rend Lake Conservancy District USA Benton, IL IL Surface Water

Scottish Water, Badentinan UK Elgin ROW Ground Water Scottish Water, Fort William UK Fort William ROW Ground Water Scottish Water, Howden UK Melrose ROW Ground Water

Scottish Water, Kirkmichael UK Perth ROW Surface Water

Severn Trent, Washgreen WTW UK Derby ROW Ground Water


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Sime Oleander (Coca Cola) MLA ROW Surface Water Sourdough Home Owners USA Sandpoint, ID ID Stobhill Hospital, Glasgow UK Glasgow ROW Town Mains Thames Water, Deephams UK London ROW Secondary Effluent Ugum USA Surface Water Victoria Hospital, Glasgow UK Glasgow ROW Town Mains West Cumberland Utility USA Crossville, TN TN Yorktown Heights USA NY

Ajax Chemicals, Smithfield AUS NSW

Auspine AUS Industrial Process Autaugaville CAN

Cadbury Schweppes, Perth AUS WA Town Mains

Cadomin WTP (Yellow Head County) CAN Alberta AB

Carlton United Breweries AUS VIC

Chatham-Kent WTP (Ontario) CAN Erie Beach ON

Chrysler De Mexico MEX ROW Exxon, Olympia RUS ROW Surface Water Gordon, City of USA Gordon, TX TX Surface Water Hatillo-Camuy WTP PTR Hato Rey IDS SKO ROW In Ha University SKO ROW

La Laguna MEX La Laguna CA

Mt. Pleasant AUS VIC Surface Water


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Nestle Water CAN Gelph, Ontario ON

PetroBras- Reman BRZ Brazil ROW

Quatar Petroleum QTR ROW Sana Muerto PTR Sana Muerto ROW Surface Water

Seoul Metropolitan Government SKO ROW

Timbercorp AUS VIC Surface Water

Wagga Wagga Base Hospital AUS NSW NSW Town Mains

Wasagaming CAN Manitoba MB

Wyndham AUS WA Xiao Hong Men CHN ROW Secondary Effluent


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UF Membrane Installations Norit X-Flow

Facility Location Membrane Type Treatment type Flow Rate

(MGD) Commission

Date CHMFP Minneapolis, USA X-Flow S225 UFCM5 Surface Water to Drinking water 70 2005

- Sulaibiya, Kuwait X-Flow S225 UFCM5 Municipal Effluent to Reuse 100 2004

Aalsterweg Eindhoven, Netherland X-Flow S225 UFCM5 Conventional Backwash to Drinking water n/a 1996 - Macharen, Netherland Conventional Backwash to Drinking water n/a 2000 - Helmond, Netherland X-Flow S225 UFCM5 Conventional Backwash to Drinking water 0.36 2003 - Annen, Netherland X-Flow S225 UFCM5 Conventional Backwash to Drinking water 0.25 2001

Goreangab Windhoek, Namibia X-Flow S225 UFCM5 Municipal Effluent to reuse 5.6 1999


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NF Membrane Installations Dow Filmtec NF-270 Facility Membrane Type Treatment type Flow Rate (MGD) Commission Date

Overton Beach Marina, Nevada Filmtec NF 270-400 0.1

Lofsdalen, Sweden Filmtec NF 270-401 Treatment of lake water 0.19 1998

Irvine Ranch Water District Filmtec NF 270 Concentrate recovery 0.40 2007 Hydranautics ESNA1-LF Facility Membrane Type Treatment type Flow Rate (MGD) Commission Date

Hollywood, Florida ESNA1-LF Desalination of Brackish water 18 1996

Ft. Lauderdale, FL* ESPA4 /ESPA4 / ESNA1-LF2 Desalination of Brackish water 12 2006

Boca Raton, Florida ESNA Desalination of Brackish water 40

Deerfield Beach, Florida ESNA Desalination of Brackish water 120

Pompano Beach, Florida ESPA1/ESNA1 365 Desalination of Brackish water 10 2002

North Miami Beach Florida, FL ESNA1-FL Desalination of Brackish water 9 2005

Orange Tree Utilities, FL ESNA1-LF Desalination of Brackish water 2005

City of Boca Raton, FL ESNA1-LF2/ESNA1-LF3 Desalination of Brackish water 0.35 2004/2005

Deerfield Beach, FL ESNA1-LF Desalination of Brackish water 40.9

Irvine Ranch, CA HYDRACoRe Desalination of Brackish water 8 2002

Glendale, Maryland ESNA1 Desalination of Brackish water 7.35 2003

Moody Air force Base-Georgia ESNA1/ ESPA3 Desalination of Brackish water 0.86 2002

Collier County, FL ESNA Desalination of Brackish water 12 1993

Dunedin, FL ESNA Desalination of Brackish water 9 1992

Fort Myers, FL ESNA/ESPA Desalination of Brackish water 12 1990

St. Lucie West, FL ESNA Desalination of Brackish water 1 1980


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RO Membrane Installations Hydranautics ESPA Facility Membrane Type Treatment type Flow Rate (MGD) Commission Date Ulu Pandan, Singapore ESPA2+ 44 2006 Orange County Water District, CA ESPA2+ 70 2006 Shedgum/Saramco KSA ESPA2 3 2006 Libya ESPA2 3 2006 Wichita Falls, Texas ESPA2/ESPA4 12 2006 Tropical Farms WTP, Stuart FL ESPA2 8 2006 North Miami Beach, FL ESPA2/ ESPA1 6 2005 West Basin Municipal Water District, California ESPA2/ ESPA1 6 2005 Orange County Water District, CA ESPA2 5 2004 Southmost.Regional, Tx (Brownsville) ESPA2 6 2003 Alameda County Water District, CA ESPA1/ESPA2 8 2002 Tampa Bay Water, FL ESPA2/SWC4 5 2002 West Basin ESPA2 5 2001 Glouster County, VA CPA3/ESPA2 10 2001 Aledo, IL ESPA2/ESPA1 6 2001 Jupiter, Florida ESPA2 6 2000 Mexicana de Cobre, Mexico ESPA2 5 2000 Pt St. Lucie ESPA2 4 1999 Dow Filmtec SW30HR-380 Facility Membrane Type Treatment type Flow Rate (MGD) Commission Date

Weihai City, China SW30HR-380 Treats seawater for process

water and drinking water for plant

0.006

Canary Islands, Spain SW30HR-380 Desal 5 Mar-03 GE Water MUNI-RO-400 Facility Membrane Type Treatment type Flow Rate (MGD) Commission Date

Clearwater, Florida Muni-RO-400 Treatment for drinking water to reduce arsenic levels 3 2003

Gwinner, ND MUNI-RO-300 TDS, iron, hardness 0.32 1990

Toluca, IL MUNI-RO-350 Radium 0.29 1992


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Daupin, AL MUNI-RO-350 TDS, iron 0.24 1996

Pipeston, MN MUNI-LERO-400 Nitrates 2 1999

Pascagoula, MS MUNI-LERO-FF-365 Color, TDS, H2S 5.7 1999

Torrington, WY MUNI-LERO-FF-365 Nitrate removal 2.2 2000

Horizon City, TX MUNI-LERO-FF-365 TDS 2.25 2000

Ottawa, IL MUNI-LERO-FF-350 Radium 4 2001

Chelsea, MI MUNI-LERO-FF-365 Softening 1.2 2001

Wabaunsee, KS MUNI-LERO-FF-365 TDS 0.22 2001

Pottawatomie, Kansa Muni-LERO-FF-365 TDS reduction surface water. 0.22 2001

Lake Granbury, TX MUNI-RO-350 TDS and chlorides. 6 2002

Maud, OK MUNI-RO-350 TDS 0.07 2002

Clara City, MN Muni-LERO-350 Nitrate 0.43 2002

Inglis, FL Muni-RO-400 THM/HAA precursors 0.65 2002

Todd Creek, CO Muni-RO-400 TDS 0.43 2002

Great Rock, CO Muni-RO-400 TDS 0.14 2002

Village of Golf, FL Muni-RO-400 Softening / THM 0.87 2002

Hennessey, OK Muni-RO-400 TDS 0.22 2002

Garden City, KS MUNI-RO-2.5 TDS 5 2002

Gila Bend, AZ MUNI-RO-150 TDS 0.65 2002

Purrysburg, SC MUNI-RO-300 Arsenic 0.43 2003

Possum Kingdom, TX MUNI-RO-350 TDS 1 2003

Box Elder, CO MUNI-LERO-350 TDS/fluoride 0.14 2003

Milliken, CO MUNI-LERO-350 TDS 0.65 2003

Hudson, CO MUNI-LERO-350 TDS 0.22 2003

Windmere, TX MUNI-LERO-400 TDS 1 2003


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Bradford, IL MUNI-LERO-400 Radium 0.29 2003

Bonita Springs, FL MUNI-RO-400 TDS 6 2004

Grand Blanc, MI MUNI-LERO-350 TDS 2 2004

Macomb, IL MUNI-LERO-400 Radium 1 2004

Spring Valley, IL MUNI-LERO-400 Radium 1.4 2004

Bushmell, IL MUNI-LERO400- Radium 0.43 2004

El Paso, TX MUNI-LERO-400 TDS 4 2004

Meander River, OK MUNI-LERO-400 TDS 0.14 2004

Goodyear, AZ MUNI-LERO-400 TDS 3.5 2004

Saline, MI MUNI-LERO350- Softening 2 2004

Davison, MI MUNI-LERO-350 Arsenic 0.86 2004

San Juan Capistrano, CA MUNI-LERO-365 TDS 4 2005

Schuylerille, NY MUNI-RO-400 TDS 2005

Tama, IA MUNI-LERO-400 TDS, Fe, Mn 0.58 2006

St. John Parish, LA MUNI-LERO-400 TDS, color, TTHM 4 2007

Pilot Knob WD Farminton, MO MUNI-LERO-400 TDS, color, TTHM 0.29 2006

Le Center, MN MUNI-LERO-400 TDS, hardness 1.3 2007

Pryor Lake, MN MUNI-LERO-400 TDS, hardness 1.9 2008

Moss Point, MS MUNI-LERO-400 TDS, color, TTHM 5 2008


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APPENDIX B

Survey / Questionnaire on Membrane Characterization Techniques and Procedures


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Date: First and Last Name: Name of Organization: Role at Organization: Level of Expertise in Surface/Membrane Characterization (circle one): (Novice, Moderate, or Expert)

CONTACT ANGLE CHARACTERIZATION TECHNIQUE The contact angle test measures the angle formed by the edge of a bubble on a submerged membranes surface called the “dynamic” captive bubble technique. (See below).

Fig. 1 Captive bubble contact angle

In the “dynamic” captive bubble technique proposed, an air bubble is generated on the tip of a U-shaped needle. The needle is immersed in a quartz cuvette, which contains the probe liquid of interest (e.g., water). The air bubble is detached from the needle and it rises through the liquid until it comes into contact and subsequently attaches to the membrane surface. The contact angle would then be measured by a Gonimeter. The contact angle data gathered may be used to calculate specific surface energy properties (van der Waals, Lewis acid-base) for detailed interfacial analyses, as well as for qualitatively assessing the wettability, or hydrophobicity / hydrophilicity, of a membrane surface.

1. Given our description of the technique and the full test method, would you envision finding the results of the contact angle test useful?

2. If the previous answer is a yes, how would you foresee using the results and often do you think you would like to have the contact angle test conducted?

3. The contact angle test would include the following major pieces of equipment: a. Goniometer (example manufactures: Ramé-Hart or Krüss Scientific).

Given this list, would you have the capacity of conducting the test in-house on a regular basis?


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If not, what piece(s) of equipment would you require so that you could conduct the test?

4. The contact angle test would require the following chemicals a. ASTM Type 1 water, and b. Saturated NaOH in isopropanol (for hollow fiber membrane)

Do you currently have or could readily order these chemicals? If not, which chemical(s) would need to be specially ordered to conduct the test?

5. The contact angle test would require the following materials a. PTFE magnetic stirrer, b. Borosilicate glass vessel, c. Powder-free gloves, and d. Scissors.

Do you currently have or could readily order these materials? If not, which material(s) would need to be specially ordered to conduct the test?

6. Do you believe that your current laboratory and/or analytical staff would have the capability of conducting the contact angle experiment?

7. If the previous answer is a no, what aspect of the training would the staff require (such as equipment training or specific procedure training)?

8. Any new test requires the staff to overcome a learning curve before consistent, accurate results can be obtained. Given our description, how steep would you think this learning curve would be for your staff to learn the contact angle test?

9. We believe that the contact angle test would take 6 hours to complete (2 hours for each bubble measurement) once the process of understanding and conducting the test has been established. Do you believe our time estimate is accurate?


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10. Would the contact angle test have a significant impact on other lab activities by factors such as lead time or the complexity of the test?

11. Do you see this procedure as an accurate and precise method for measuring the contact angle?

12. Please provide any addition comments regarding the contact angle test.


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ZETA POTENTIAL CHARACTERIZATION TECHNIQUE Zeta potential (ζ) is the potential difference between the bulk of solution and the sheer (slipping) plane of the interfacial double layer along the membrane (A. V. Delgado, F. Gonzáles-Caballero, R.J. Hunter, L. K. Koopal and J. Lyklema, Measurement and Interpretation of Electrokinetic Phenomena (IUPAC technical Report) Pure and Applied Chemistry, 77 (2005) p. 1753-1805). Zeta potential, also referred to as streaming potential, is a function of surface and solution chemistry (pH, ionic composition, and ionic strength) at the solid-liquid interface. The zeta potential is measured by a streaming potential analyzer. The membrane is prepared depending on type (flat sheet or hollow fiber) and placed in the analyzer’s flow cell. Then the flow of electrolyte is started at constant pressure and changing directions until steaming potential is stabilized. The measurement is recorded after the zeta potential is stabilized. Zeta potential is an important membrane characteristic when assessing membrane fouling potential and in developing chemical cleaning protocols.

1. Given our description of the technique and the full test method, would you envision finding the results of the zeta potential test useful?

2. If the previous answer is a yes, how would you foresee using the results and often do you think you would like to have the zeta potential test conducted?

3. The zeta potential test would include the following major pieces of equipment: a. Streaming Potential Analyzer (by either CAD Instruments or Brookhaven

Instruments Corporation). Given this list, would you have the capacity of conducting the test in-house on a regular basis? If not, what piece(s) of equipment would you require so that you could conduct the test?

4. The zeta potential test would require the following chemicals a. ASTM Type 1 water, b. Potassium Chloride (KCl), c. Hydrochloric Acid (HCl), and d. Potassium Hydroxide (KOH).



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5. The zeta potential test would require the following materials a. PTFE magnetic stirrer, b. Borosilicate glass vessel, c. Polyethylene bottle, d. Scissors, e. Dessicator, f. Play-Doh or similar, g. Marine grade epoxy mix, and h. Tube cutter.


6. Do you believe that your current laboratory and/or analytical staff would have the capability of conducting the zeta potential experiment?


8. Any new test requires the staff to overcome a learning curve before consistent, accurate results can be obtained. Given our description, how steep would you think this learning curve would be for your staff to learn the zeta potential test?

9. We believe that the zeta potential test would take 12 hours for a single ionic strength at 3 different pH values to complete once the process of understanding and conducting the test has been established. Do you believe our time estimate is accurate?

10. Would the zeta potential test have a significant impact on other lab activities by factors such as lead time or the complexity of the test?


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11. Do you see this procedure as an accurate and precise method for measuring the zeta potential?

12. Please provide any addition comments regarding the zeta potential test.


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SURFACE ROUGHNESS CHARACTERIZATION TECHNIQUE Surface roughness is simply the measure of the texture on the membrane surface and the test would be with atomic level resolution using an atomic force microscope (AFM). The membrane is first prepared depending on type (flat sheet or hollow fiber) before the surface roughness is measured. The AFM uses a fine tip that exists on the end of the cantilever and is scanned over the membrane surface using a tapping method. The tapping mode uses a rapidly oscillating cantilever in the vicinity of the surface, and amplitude damping is used for imaging. Only short intermittent contact of AFM tip with the sample (tapping) occurs, which is especially suitable membrane surfaces. Direct measurement of surface features is by measuring the position and movement of the cantilever as it is scanned over a membrane surface. Surface roughness is an important property in determining membrane fouling propensity.

1. Given our description of the technique and the full test method, would you envision finding the results of the surface roughness test useful?

2. If the previous answer is a yes, how would you foresee using the results and often do you think you would like to have the surface roughness tested?

3. The surface roughness test would require the following major pieces of equipment: a. Atomic Force Microscope (AFM).

Given this list, would you have the capacity of conducting the test in-house on a regular basis? If not, what piece(s) of equipment would you require so that you could conduct the test?

4. The surface roughness test would require the following chemicals a. ASTM Type 1 water, and b. Potassium Chloride (KCl) optional,


5. The surface roughness test would require the following materials a. PTFE magnetic stirrer, c. Powder-free gloves, d. Scissors, e. Double stick tape,


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f. Knife cleaned with isopropanol, and g. Borosilicate glass vessel


6. Do you believe that your current laboratory and/or analytical staff would have the capability of conducting the surface roughness experiment?


8. Any new test requires the staff to overcome a learning curve before consistent, accurate results can be obtained. Given our description, how steep would you think this learning curve would be for your staff to learn the surface roughness test?

9. We believe that the surface roughness test would take 3 hours including sample preparation to image acquisition to complete once the process of understanding and conducting the test has been established. Do you believe our time estimate is accurate?

10. Would the surface roughness test have a significant impact on other lab activities by factors such as lead time or the complexity of the test?

11. Do you see this procedure as an accurate and precise method for measuring the surface roughness?

13. Please provide any addition comments regarding the surface roughness test.


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APPENDIX C

Instructions to Participating Laboratories for Conducting AFM, Contact Angle, and Streaming Potential Measurements


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Dear Participating Laboratory,

Thank you for agreeing to participate in the project titled “Evaluation of Membrane Characterization Methods” that is being sponsored by the Water Research Foundation (formerly AWWARF). At this time, we have begun Task C - Development of Membrane Characterization Standard Techniques and we are now requesting your assistance with this investigation. The goal of Task C is to develop standard techniques for characterizing the following membrane properties:

• Contact angle with water

• Streaming potential (used to calculate zeta potential)

• Surface roughness using atomic force microscopy (AFM) The three teams participating in the development of these techniques are the University

of Nevada, Reno (UNR; A. Childress, project PI), Duke University (DU; M. Wiesner) and Colorado School of Mines (CSM; T. Cath). Developing the standard methods for measuring the aforementioned characteristics will be carried out in three phases. During Phase 1, UNR is distributing both control and membrane samples to the partner laboratories (DU and CSM). UNR is also distributing general instructions for performing the characterization measurements; these instructions are not comprehensive and are intended as guidance only. Each laboratory will perform contact angle, streaming potential, and surface roughness measurements using the instructions. UNR will collect the data and analyze the local (within each institution) and global (among all institutions) precision of each of the methods. Additionally, each laboratory will record in detail the methodology that they used when measuring contact angle, streaming potential, and surface roughness. This information will then be submitted to UNR along with the characterization results for subsequent analysis. In Phase 2, UNR will further develop and refine the instructions for measuring contact angle, streaming potential, and surface roughness using AFM in order to develop a standard technique for measuring each characterization parameter. In Phase 3, UNR will again distribute the control surfaces, membrane samples, and draft standard techniques to the partner laboratories. They will repeat the same measurements as in Phase 1; however, they will carefully follow the standard technique. The data will again be collected by each lab and submitted to UNR for analysis. If necessary, the standard technique may be iterated on until a desired level of precision is achieved.

Choices of Control Materials for Method Development

Prior to performing characterization measurements on membrane samples, measurements will be performed on standard or control surfaces. The control surfaces for both flat sheet and hollow fiber configurations are given in Table 1. The ability to characterize the properties of the hollow fiber configuration (either concave or convex depending on outside-in or inside out flow) is an important aspect of this investigation because two of the seven membranes to be tested are of the hollow fiber configuration. Thus, in addition to the instructions for the flat surfaces, instructions are also given for the laboratories to perform contact angle measurements on one convex surface (glass capillary coated with PMMA, prepared by UNR) and AFM imaging on one concave surface (angle-cut Teflon tubing).


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Table 1.

Reference surfaces selected for method development.

Measurement\Sample A Flat

B Concave

C Convex

Streaming Potential

PMMA film cast on flexible support (polyethersulfone support from commercial membrane)

PMMA block with small diameter hole (or holes) drilled though it.

PMMA commercial rods or tubes, or glass rods covered with PMMA film

Contact Angle Teflon flat sheet from Ramé-Hart with contact angle range and material origin certificate.

Cleaned glass capillary painted black on the outside

Glass capillary covered with PMMA on the outside

AFM Roughness

HS-20MG nanostructure array (20 nm step height) for Z-axis calibration from budgetsensors.com, or similar grid.



AFM instructions for flat samples (membranes or control materials)

1) Rinse membrane (or control material, except for metrological nanostructure for Z- axis calibration) three times with saturated ultrapure water, store completely immersed in saturated ultrapure water at 5°C at least 48 hours prior to measurement. Ultrapure water has conductivity of 0.05 μS cm when not saturated with ambient CO2, and 0.8 μS cm when equilibrated with atmospheric CO2. Check pH of saturated ultrapure water (5.6).

2) Prepare liquid cell and tip for tapping mode measurement in liquid.

3) Calibrate instrument in air (especially Z-axis), if metrological nanostructure is available.

4) Cut coupon suitable for imaging and install in liquid cell, fill with saturated ultrapure water.

5) Follow instrument-specific instructions, set 10μm ×10μm scan area, 256 steps in X and Y direction; acquire date in tapping mode.

6) Record and describe any data processing procedures applied that may affect roughness, such as automatic leveling.

7) Save data in software specific binary format and export data into SDF, ASCII or Excel format. Free program Gwyddion may be useful in data conversion.

8) Calculate average roughness, RMS roughness, and skewness (if possible) with instrument-specific software.

9) Create surface image in jpeg, png or gif format.


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10) If direct conversion to SDF format was not possible, edit ASCII/Excel data to conform to SDF specifications as described in Internet-based Surface Metrology Algorithm Testing System (http://syseng.nist.gov/VSC/jsp/index.jsp).

11) Upload data in SDF format to Internet-based Surface Metrology Algorithm Testing System and perform average roughness, RMS roughness, and skewness calculations again and calculate respective uncertainties (all described functions are available in Internet-based Surface Metrology Algorithm Testing System).

12) Repeat measurements for three regions on three different coupons (for a total of nine measurements) for each membrane or control material.

13) Collect data in a table. Report tip characteristic (tip curvature, material, etc.), instrument make and type, operator name and date. Report sample name and origin, measurement temperature and results (roughness ,skewness, uncertainties, instrument specific software and Internet-based Surface Metrology Algorithm Testing System, include image) for each nine imaged spots.

14) Archive raw data files, create one compressed archive with meaningful name (related to sample name and laboratory name) per membrane/control material.

AFM instructions for hollow fiber membranes (inside-out) or control concave materials

1) Rinse sample three times with saturated ultrapure water, use syringe to rinse inside the fiber, store completely immersed in saturated ultrapure water at 5°C at least 48 hours prior to measurement.

2) Prepare instrument as for flat-sheet membranes.

3) Cut fiber with knife or blade at a sharp angle to expose inside surface.

4) Affix angle-cut hollow fiber material with two-sided tape to measurement cell, with exposed inside of the membrane upwards. Make sure angle-cut area does not interfere with tip movement.

5) Fill cell with ultrapure water.

6) Proceed as for flat-sheet membranes.

7) Report any baseline correction options different from flat sheet membranes (automatic leveling), e.g., if software integrated with the instrument allows for curved baseline correction.

8) Proceed with data recording and analysis as for flat sheet membranes.

9) Report the same data as for flat-sheet membranes.

For sample cutting technique see: M. Rafat, D. De, K.C. Khulbe, T. Nguyen, T. Matsuura, Surface characterization of hollow fiber membranes used in artificial kidney, J. of Appl. Polym. Sci. 101 (2006) 4386-4400.

Contact angle instructions for flat samples (membranes or control materials)


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1) Sample preparation: membranes or reference materials should be rinsed with saturated ultrapure water and stored fully immersed in saturated ultrapure water at 5°C for at least 48 hours prior to measurements.

2) Prepare goniometer for captive bubble measurement in saturated ultrapure water.

3) Cut membrane coupon using sharp scissors. Stretch coupon on steel support (depending on instrument), ensuring that there are no gaps between the support and the sample, and also ensuring that the edge of the membrane is straight and smooth.

4) Follow goniometer instructions for baseline and light optimization.

5) Use dynamic captive bubble technique with 10 μL bubble size: bubble should be detached from needle under membrane prior to touching it.

6) Measure 3 captive bubbles on 2 coupons (6 bubbles total), perform 5 automatic measurements per each bubble with 1 minute time delay, if instrument allows.

7) Save 1 representative bubble image per sample.

8) Collect data for all 6 bubbles in a single Excel 97 (xls) file.

9) Report instrument make and type and operator name and date. Report sample name and origin, measurement temperature, and results (contact angle averages for left and right side of the bubbles).

10) Save and archive one Excel file per sample (with bubble image pasted) with meaningful name (related to sample name and laboratory name). UNR will calculate statistics from raw data (average, standard deviation, confidence interval at 95% level).

Contact angle instructions for hollow fiber membranes (outside-in) or control convex materials

1) Sample preparation: hollow fiber membranes should be rinsed with saturated ultrapure water three times to remove preservatives; sample should then be dessicated over phosphorus pentoxide for at least 24 hours prior to measurement. If control surface is clean, water rinsing can be omitted or replaced with control-specific cleaning procedures.

2) Prepare goniometer stage: sample holder, transparent cover with paper pads soaked in water that do not obscure sample image (see Figure 1). If air around the sample is dry, any drop will evaporate quickly, and contact angle measurements will not be stable.

3) Mount hollow fiber over goniometer stage, so fiber axis is parallel to goniometer stage plane, and perpendicular to camera axis (axis normal to the lens surface at its center, see photograph). Inverted holder for captive bubble technique may work well. Stretch fiber gently, so it has appearance of a cylinder.

4) Follow the same baseline and light optimization procedures as for captive bubble technique.

5) Use 5-15 μL hanging sessile drop on horizontal cylinder (larger clam-shell drop might release from less hydrophilic membranes). Produce advancing sessile drop – drop pumped with syringe after it is created on the hollow fiber surface.

6) Look on the fiber directly from above – make sure drop is not leaning to the front or to the back, adjust by turning fiber in the holder.


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7) Run measurement as for captive bubble technique, set baseline at bottom of the capillary contour.

8) Make the same number of measurements as for flat-sheet membrane, report the same data and fiber (cylinder) outer diameter measured with a micrometer.

Figure 1. Capillary with hanging clam-shell drop photographed at UNR laboratory. Elements of sessile drop holder were used to position capillary on the goniometer stage. Note that a small reservoir with water and cover with moist paper pads were used to prevent drop evaporation.

Zeta potential instructions for flat samples (membranes or control materials)

1) Perform calibration of streaming potential instrument conductivity and temperature sensors.

2) Prepare electrolyte solutions needed using saturated ultrapure water and ACS reagents (see below).

1) Sample preparation: rinse 3 times with saturated ultrapure water, store completely immersed in 2.0 mM KCl solution (prepared from saturated ultrapure water and ACS certified KCl) at 5 °C for at least 48 hours prior to measurement,

2) Cut coupon to size, install coupon in flow cell just prior to measurement.

3) After cell is installed, start flow of electrolyte (2.00 mM KCl ACS certified in ultrapure saturated water) at constant pressure and changing directions.

4) Measure electrolyte conductivity and pH with external well-calibrated instruments and make sure that conductivity is in agreement with values reported by internal sensor.

5) When streaming potential is stabilized, collect data. When variable pH is desired, prepare electrolytes using KOH or HCl (add KCl to keep total monovalent ion concentration at 4.0 mM).

6) Follow the following pH cycle (5.7 corresponds to ambient air saturated ultrapure water) keeping the same sample and changing electrolytes: 5.7 → 4.0 → 3.0 → 5.7 →7.0 → 8.0.

9) Run triplicate measurements (3 different coupons) for each sample.


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10) Report instrument make and type and operator name and date. Report sample name and origin, electrolyte composition and pH, and results: streaming potential vs pressure slope, slope standard deviation, temperature, conductivity, calculated viscosity, calculated permittivity, and zeta potential. Use the following formulas for viscosity (η) and permittivity (ε) , where t is temperature in degrees Celsius:

11) Make one Excel file per electrolyte and sample coupon. Create one compressed directory

(containing excel files) with meaningful name (related to sample name and laboratory name) per membrane/control material. UNR team can calculate statistics from raw data (report streaming potential vs pressure, temperature and conductivity).


133

APPENDIX D

Standard Protocols for Characterizing Membrane Surfaces: Contact Angle, Zeta Potential, Surface Roughness


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Standard Protocols for Characterizing Membrane Surfaces: Contact Angle, Zeta Potential, Surface Roughness

WaterRF Project #4102:


Funding Agency Water Research Foundation

Project Number 4102 6666 W. Quincy Avenue Denver, CO 80235-3098

Principal Investigator Dr. Amy E. Childress

University of Nevada, Reno Department of Civil and Environmental Engineering/258

1664 N. Virginia Street Reno, NV 89557-0258

Co-Principal Investigator Dr. Jonathan A. Brant HDR Engineering, Inc.

500 108th Avenue NE, Suite 1200 Bellevue, WA 98004-5549

September 30, 2010


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Contents

CHAPTER 1: SURFACE WETTABILITY - CONTACT ANGLE MEASUREMENTS 136

General Discussion 136 Contact Angle Measurement Apparatus and Software 137 Materials and Reagents 139 Contact Angle Measurement Procedures 139

Procedure for Flat-Sheet Membrane Samples 139 Procedure for Hollow Fiber Membrane Samples (External Surface) 140

Contact Angle Data Analysis 142 Additional Recommendations 142

CHAPTER 2: IONIZATION OF SURFACE FUNCTIONAL GROUPS AND ADSORBED IONS: SURFACE CHARGE - STREAMING POTENTIAL / ZETA POTENTIAL 143 General Discussion 143 Test Apparatus and Software 143 Materials and Reagents 144 Procedure 144

Procedure for Flat-Sheet Membranes 144 Procedure for Hollow Fiber Membranes 145

Streaming Potential Data Analysis 146 Additional Recommendations 146

CHAPTER 3: MEMBRANE SURFACE ROUGHNESS - AFM MEASUREMENTS 148 General Discussion 148 AFM Apparatus and Software 148 Materials and Reagents 149 Procedure 149

Procedure for Flat Membranes 149 Procedure for Hollow Fibers 149

AFM Roughness Data Analysis 151 Additional Recommendations 151


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CHAPTER 1 SURFACE WETTABILITY - CONTACT ANGLE MEASUREMENTS

GENERAL DISCUSSION

The contact angle (θ) that is formed at the three-phase interface between solid, liquid, and

gas / vapor phases may be used to elicit information regarding the surface energy properties of a material, such as membranes. The contact angle is the angle that is formed between a surface and the tangent line whose origin is at the three-phase junction and is measured through the liquid phase (Fig. 1). The information that is gathered from contact angle analysis may be used to calculate specific surface energy properties (van der Waals, Lewis acid-base) for detailed interfacial analyses, as well as for qualitatively assessing the wettability, or hydrophobicity / hydrophilicity, of a membrane surface. This latter application is perhaps the most common use for contact angle by utilities and other membrane users.

Figure 1. Sessile drop configuration (left) for measuring contact angle in which a liquid droplet is placed on a dry surface and captive bubble configuration (right) for measuring contact angle in which a surface is immersed in a probe liquid and a air bubble is placed on the wetted surface.

The two most commonly used methods for measuring contact angle on membrane surfaces

are the sessile drop and captive bubble techniques (Fig. 1). Of these techniques the captive bubble method is being used more and more frequently as it allows the membrane to be characterized in its native hydrated (wet) state. These two techniques are illustrative of the two general types of contact angles that may be measured for membranes: the advancing and receding contact angles. The advancing contact angle is that which is measured for a dry surface, where the liquid is “advancing” over the previously dry surface. Conversely, the receding contact angle is that which is measured for a previously wetted or hydrated surface. Contact angle hysteresis, which is the difference between the advancing and receding contact angle values, is one of the greatest challenges in contact angle analysis. Hysteresis is a challenge because it can produce two different contact angle values (receding and advancing angle), as evidenced by a drop on a tilted flat surface (Fig. 2). The challenge is to determine which value is the most relevant for a particular application. For membrane analysis the receding contact angle would seem to be more relevant as membranes are hydrated or wet in most practical applications. Membrane surface roughness and porosity may also affect the value of the measured contact angle values relative to those contact angles that are measured for a smooth non-porous surface


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of the same material. Contact angles may be corrected for surface roughness using the Wenzel Equation.

Figure 2. Contact angle formed between a water droplet on an inclined Teflon surface. This

image illustrates the difference between an advancing (angle formed on the lower left) and receding (angle formed on the upper right) contact angle.

In the “dynamic” captive bubble technique proposed here, an air bubble is generated on the

tip of a needle that is U-shaped. The needle is immersed in a quartz cuvette, which contains the probe liquid of interest (e.g., water). After detachment, the air bubble rises through the liquid and comes into contact and subsequently attaches to the membrane surface. The type of contact angle that is measured using this technique is referred to as the receding contact angle, because the water has receded from the previously wetted surface. The “dynamic” captive bubble technique as proposed here is recommended for measuring contact angles on membrane surfaces because it characterizes the membrane in its most relevant condition (i.e., hydrated). Measurement of the advancing contact angle using the captive bubble technique is not recommended because it would require air to be withdrawn from the bubble (after it is formed on the sample surface) until the contact angle reaches a stable value. This action would increases the risk of surface damage (proximity of tip to the membrane), would complicate manual control of bubble volume, and would distort the shape of the air bubble as a result of the needle penetrating into its volume. It would also limit choices of image analysis method (asymmetric drop shape analysis, ADSA, was modified to accommodate the needle only recently and may not be implemented in current software).

CONTACT ANGLE MEASUREMENT APPARATUS AND SOFTWARE

Contact angle is most commonly measured using a goniometer (Fig. 3). Goniometers are

instruments that consist of a sample stage, digital camera, and a light source. Other add-ons are also available that allow for digitally capturing and analyzing the bubble or droplet shape in order to calculate contact angle. The primary equipment vendors for goniometers are Ramé-Hart and Krüss Scientific, though others also exist. The principle components of a goniometer system that is capable of operating in the sessile and/or captive bubble configuration, include:

• Adjustable height sample stage,

• Adjustable light source,

• Computer interfaced digital camera,


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• Quartz cuvette (captive bubble),

• Environmental chamber (temperature and humidity control)

• Gas-tight micro syringe with U-shaped needle

• Sample holders

• Fixed drop standards – images of idealized drops on a slide with certified contact angle values (Figure 4, used for accuracy assessment)

• Two rectangular plates cut out of microscope slide to sandwich a hollow fiber membrane in sample holder (Figure 5

Figure 3. Example of a modern goniometer system (Ramé-Hart, Netcong, NJ) that includes a

computer for data acquisition and analysis, environmental chamber, sample stage, light source, automatic syringe, and digital camera for image acquisition.

Figure 4. Certified contact angle fixed-drop calibration reference tool (Ramé-Hart, Netcong, NJ)

for determining the accuracy of contact angle values measured in a respective laboratory.


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MATERIALS AND REAGENTS

To facilitate the comparison of results between labs and to allow for the calculation of surface energy parameters from contact angle values, it is highly recommended that all reagents be of the highest available quality. For measuring the contact angle that water forms on a surface it is recommended that ASTM Type I water be used (maximum conductivity = 0.055 µS cm-1).

Following production and subsequent exposure to the surrounding atmosphere the ASTM Type I water will be subject to carbon dioxide (CO2) dissolution until saturation occurs. This will affect the conductivity and pH of the water. It is therefore recommended that the water to be used for contact angle measurements be allowed to reach equilibrium with the atmosphere prior to performing the contact angle measurements. This may be accomplished by stirring the water sample (PTFE magnetic stirrer) in ambient air in properly cleaned borosilicate glass vessel (Standard Method 1070 B, Standard Methods for Examination of Water and Wastewater, 1995), until the conductivity reaches 0.7 µS cm-1.

Note that measuring the pH of ASTM Type I water requires special electrodes. As an alternative, we propose that ACS certified KCl can be dissolved in ultrapure water (0.50 g/L) and pH tested periodically with standard pH meter (pH = 5.6 for ambient CO2 saturation). The water sample that is being used to determine the pH of the ASTM Type I water should not

CONTACT ANGLE MEASUREMENT PROCEDURES

be used for contact angle measurements. As a precaution, the water sample for contact angle measurements should be stored in borosilicate glass for no longer than one week.

Procedure for Flat-Sheet Membrane Samples

1) Contamination of the sample surface must be avoided. Powder-free gloves must be worn at all times when handling the membrane samples and goniometer equipment that will come into contact with the probe liquid and membrane. Sample preparation: membranes should be rinsed with saturated ASTM Type I water and stored fully immersed in saturated ASTM Type I water at 5°C for at least 48 hrs prior to performing the contact angle measurements. When selecting samples for analysis choose sample areas that are free of visible defects, like scratches or stains.

2) Set-up and start the goniometer according to the vendor supplied instructions. Precaution should be taken to ensure that the instrument is leveled and protected from external vibrations. This is especially important when older instruments are used and can be major source of error.

3) Test the instrument using a fixed drop image standard: measure all four contact angles imaged on the standard and calculate deviations from certified values. Calculate the relative value (%) of the greatest deviation (absolute value) as an estimate of systematic error.

4) Prepare goniometer for captive bubble measurement in saturated ASTM Type I water. When goniometric method can be chosen, specify polynomial fit to bubble edge, not asymmetric drop/bubble shape analysis (ADSA). Although ADSA is accurate and uses


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information of the whole drop/bubble shape to calculate contact angle, it is not applicable to hollow fiber measurement as proposed below. Polynomial fitting is applicable to both flat-sheet and hollow fiber samples and will assure better method uniformity.

5) Cut membrane coupon using sharp scissors. Stretch coupon on steel support (depending on instrument), ensuring that there are no gaps between the support and the sample, and also ensuring that the edge of the membrane is straight and smooth. Immerse sample in wet cell.

6) Follow the instrument specific instructions for determining the sample baseline and for optimizing the light conditions. Run measurements in the dark, to minimize stray light.

7) Test samples at ambient temperature 23 ± 2 °C.

8) Use the dynamic captive bubble technique and an air bubble volume of 10 μL. The volume of the air bubble may be specified if using a computer controlled syringe or manually by the operator. The distance between the needle tip and the membrane sample surface must be such so as to allow the bubble to detach from the needle under the membrane prior to the two coming into contact. PTFE tubing can be put around the syringe tip, if bubble breaks down before reaching desired volume. Tap U-shaped needle at the bottom to detach bubble.

9) Measure at least 3 captive bubbles on 2 membrane coupons (6 bubbles total), perform 5 automatic measurements per each bubble with 1 minute time delay between measurements. Treat average for left and right contact angle from 5 measurements as a single data point. Report instrument make and type, report sample name and origin, cleaning procedures, if any, measurement temperature if outside specified range, number of bubbles and coupons, average, sample standard deviation, confidence interval at 95% level).

Procedure for Hollow Fiber Membrane Samples (External Surface) 1) Prior to conducting the contact angle measurement it is necessary that the hollow fiber

membranes be rinsed with saturated ASTM Type I water three times to remove preservatives and other contaminants that may be present on the membrane surface. Some hollow fiber membranes are shipped with glycerol and bisulfite as preservatives. Run DI water through plugged fiber, with pressure that allows water flow across the pores. Check effluent for TOC. Pores may become flooded when pressure is applied, even with hydrophobic material, this is usually irreversible process. Sample may appear more hydrophilic (smaller contact angle) as compared to a sample with dry pores. Describe cleaning procedure in the report.

2) After cleaning, the hollow fiber membranes should be stored fully immersed in saturated ASTM Type I water at 5°C for at least 48 hrs prior to measurements. When selecting samples for analysis choose sample areas that are free of visible defects, like scratches or stains. Use powder-free gloves to handle the membrane samples and associated equipment at all times.


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3) Set-up and start the goniometer according to the vendor supplied instructions. Precaution should be taken to ensure that the instrument is leveled and protected from external vibrations.

4) Test the instrument using a fixed drop image standard: measure all four contact angles on the standard and calculate deviations from certified values. Calculate the relative value (%) of the greatest deviation (absolute value) as an estimate of systematic error

5) Mount hollow fiber between two thoroughly cleaned (Saturated NaOH in isopropanol at room temperature followed by 5-fold water rinses; water drop spreads uniformly and quickly on clean surface) pieces of microscope slide (Fig. 5). Make sure that syringe needle is thinner than the hollow fiber tested.

Figure 5. PTFE tubing sandwiched between two glass slides. 4) Mount hollow fiber over goniometer stage, so fiber axis is parallel to goniometer stage

plane, and perpendicular to camera axis (axis normal to the lens surface at its center).

6) Follow the instrument specific instructions for determining the sample baseline and for optimizing the light conditions. Run measurements in the dark, to minimize stray light

7) Test samples at ambient temperature 23 ± 2 °C.

8) Use the dynamic captive bubble technique and an air bubble volume of 10 μL. The volume of the air bubble may be specified if using a computer controlled syringe or manually by the operator. The distance between the needle tip and the membrane sample surface must be such so as to allow the bubble to detach from the needle under the membrane prior to the two coming into contact. PTFE tubing can be put around the syringe tip, if bubble breaks down before reaching desired volume. Tap U-shaped needle at the bottom to detach bubble.

5) Measure at least 3 captive bubbles on 2 membrane coupons (6 bubbles total), perform 5 automatic measurements per each bubble with 1 min time delay between measurements. Treat average for left and right contact angle from 5 measurements as a single data point. Report instrument make and type, report sample name and origin, cleaning procedures, if any, measurement temperature if outside specified range, number of bubbles and coupons, average, sample standard deviation, confidence interval at 95% level).


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CONTACT ANGLE DATA ANALYSIS For determining the average, standard deviation, and 95% confidence level at least three

measurements should be per sample per pH and electrolyte concentration. The noted statistics should be reported for each test condition. The left and right contact angles should be used to calculate a single average contact angle value per air bubble or water droplet. If the deviation between the left and right angles exceeds 2°, then the bubble/droplet should be discarded and the measurement repeated.

ADDITIONAL RECOMMENDATIONS

The following recommendations are also suggested in addition to the standard method

proposed earlier for carrying out contact angle measurements on flat-sheet and hollow fiber membranes:

• Teflon standard surface flat surface can be used, but it will require re-certification from provider at additional cost (re-measured for receding contact angle measurement with captive bubble), as current certified value is advancing contact angle measured with sessile drop technique,

• A 3D fixed shape calibration standard (made from gauge ball and plate) should be used to establish that the sample stage is properly leveled and to establish the error that is associated with the goniometer being used,

• Front-back inclination of the goniometer stage with respect to the camera axis is the key geometrical parameter in goniometer setup affecting accuracy,

• Organic contaminants can have profound effect on contact angle measurements. TOC of ASTM water should be monitored and not to exceed 100 ppb. Use fresh HPLC-grade water, if TOC monitoring is not possible,

• Use “zero air” (from which traces of organics were removed) to equilibrate water, and

• Consider glove box filled with zero air, when VOC can be present in the laboratory air.


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CHAPTER 2 IONIZATION OF SURFACE FUNCTIONAL GROUPS AND ADSORBED

IONS: SURFACE CHARGE - STREAMING POTENTIAL / ZETA POTENTIAL

GENERAL DISCUSSION

Zeta potential (ζ) is the potential difference between the bulk of solution and the sheer (slipping) plane of the interfacial double layer (A. V. Delgado, F. Gonzáles-Caballero, R.J. Hunter, L. K. Koopal and J. Lyklema, Measurement and Interpretation of Electrokinetic Phenomena (IUPAC technical Report) Pure and Applied Chemistry, 77 (2005) p. 1753-1805). Zeta potential is a function of surface and solution chemistry (pH, ionic composition, and ionic strength) at the solid-liquid interface.

Zeta potential is an important membrane characteristic when assessing membrane fouling

potential and in developing chemical cleaning protocols. Currently, zeta potential is used as a substitute for surface charge, which so far is not accessible through direct measurement. Membrane zeta potential is determined from streaming potential measurements. Streaming potential is generated when electrolyte flows through a capillary channel, or a porous plug, and is related to zeta potential of the capillary (plug)/electrolyte interface by the Helmholtz-Smoluchowski equation:

0E

pεε ζλη

= Equation 1

where E is streaming potential due to electrolyte flow through a capillary channel, p is the applied pressure driving the flow, ζ is zeta potential, λ is electrolyte conductivity, η is the viscosity of the electrolyte solution, ε is the relative permittivity of the solution (dimensionless), and ε0 is the permittivity of a vacuum (fundamental constant). Values of E, p, and λ can be determined using a specialized instrument, while ε and η are calculated based on temperature measurement (empirical fit functions for pure water data are used). Instruments with pH control (titration) capabilities are available.

TEST APPARATUS AND SOFTWARE

Currently, there are two widely utilized vendors for streaming potential analyzers: CAD Instruments, France and Brookhaven Instruments Corporation, Holtsville, NY. Streaming potential analyzers consist of a sample cell whose design is tailored to the material being analyzed (flat sheet, fiber, granular material), a pumping system (typically pressurized nitrogen) to move an electrolyte solution through the sample cell, pressure/conductivity/pH/temperature probes, and electrodes to measure the voltage across the sample cell. Other add-ons may also be available to allow for titration of the test solution. The instrument is interfaced with a computer to allow for data acquisition and analysis in real-time. Temperature is used to calculate the viscosity and electric permittivity of the test solution, which are then used to calculate streaming potential. While sample cells are commercially available in a variety of geometries, it may be necessary to custom design some cells in order to meet system specific constraints as may be the


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case for some hollow fiber membranes. Most commercially available instruments are supplied with software packages for acquiring and analyzing the data.


To facilitate the comparison of results between labs it is necessary that all reagents be of the highest possible quality. Specifically, all electrolyte solutions should be made using ASTM Type I water (maximum conductivity = 0.055 µS cm-1, ASTM D1193-06) or its equivalent. Following production and subsequent exposure to the surrounding atmosphere the ASTM Type I water will be subject to carbon dioxide (CO2) dissolution until saturation occurs. This will affect the conductivity and pH of the water. It is therefore recommended that the water to be used in the streaming potential measurements be allowed to reach equilibrium with the atmosphere prior to performing the contact angle measurements. This may be accomplished by stirring the water sample (PTFE magnetic stirrer) in ambient air in properly cleaned borosilicate glass vessel (Standard Method 1070 B, Standard Methods for Examination of Water and Wastewater, 1995), until the conductivity reaches 0.7 µS cm-1. ACS certified reagents must be used when preparing all test solutions. The following reagents are required to prepare the test solutions as described in this overview: potassium chloride (KCl), hydrochloric acid (HCl), potassium hydroxide (KOH).

Prepare 0.1 M solutions of HCl and KOH using ASTM Type I water. The KOH solution should be stored in a polyethylene bottle, as it will leach silica from glass over time. Potassium hydroxide that contains carbonates may be used, as atmospheric CO2 will be absorbed from the air in absence of special precautions. A 2.0 mM KCl solution should be prepared as follows: place 0.2983 g of ACS grade KCl in a 2 L volumetric flask and add ASTM Type I water. Adjust the solution pH to the desired value using wither HCl or KOH. The volume of HCl or KOH solution that must be added to achieve a pH between 3 to 8 will have a negligible effect on the KCl concentration, but overall conductivity changes rapidly with acid addition as a result of the high mobility of H+ ions. Acidified solutions (pH 3 or 4) are quite stable, in contrast to neutral and basic solutions that absorb atmospheric CO2. Use basic solutions immediately after preparation, create pH by 0.3 unit greater than the target, as pH measured in the bulk of the solution after streaming potential experiment is usually decreasing.

PROCEDURE

The procedure used for preparing the membrane samples for streaming potential analysis will depend on the membrane configuration being characterized. Two separate procedures are outlined below for flat sheet membranes (i.e., samples from spiral wound elements) and for hollow fiber membranes.

Procedure for Flat-Sheet Membranes

1) Sample preparation: rinse 3 times with saturated ASTM Type I water, store completely immersed in 2.0 mM KCl solution (prepared from saturated ultrapure water and ACS certified KCl) at 5 °C for at least 48 hrs prior to measurement.

2) Set-up and start the streaming potential analyzer according to the vendor supplied instructions.


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3) Perform calibration of streaming potential instrument sensors: conductivity, temperature, and pH (if equipped). Note the location of the temperature sensor: if it is removed from the cell make sure that solution is thermally equilibrated with the ambient air. Avoid conditions that may lead to temperature gradients (heaters, air conditioning air currents).

4) Test samples at ambient temperature (23 ± 2 °C)

5) Prepare the necessary electrolyte solutions (2.00 mM KCl with pH adjusted with KOH or HCl) using saturated ASTM Type I water and ACS reagents (see above).

6) If an asymmetric cell is used (most frequently PMMA + sample) measure the zeta potential of the fixed material under the exact conditions (electrolyte composition) to be applied to the membrane sample.

7) Cut the membrane coupon to size, install coupon and spacers in flow cell just prior to measurement.

8) After the cell is installed purge the cell and start flow of electrolyte (2.00 mM KCl ACS certified in ultrapure saturated water) at constant pressure and changing directions. If streaming potential shows changes over time, wait until it is stabilized. If streaming potential is not stable, air bubbles in the cell may be present: purge and start again.

9) When streaming potential is stabilized, collect data. Run measurement three times to estimate repeatability.

10) After experiment measure electrolyte conductivity and pH with external well-calibrated instruments and make sure that conductivity is in agreement with values reported by the internal sensor during experiment.

11) When variable pH is desired, prepare electrolytes using KOH or HCl as described above. Follow the following pH cycle (5.7 corresponds to ambient air saturated ultrapure water) keeping the same sample and changing electrolytes: 5.7 → 4.0 → 3.0 → 5.7 →7.0 → 8.0

12) Run triplicate measurements (3 different coupons) for each sample.

13) Report instrument make and type, sample name and origin, electrolyte composition and pH, and results: streaming potential versus pressure slope, temperature, conductivity, calculated viscosity, calculated permittivity, and zeta potential.

Procedure for Hollow Fiber Membranes

1) Follow procedures for flat-sheet samples, except that a porous plug must be prepared first.

2) Arrange fittings compatible with the instrument that will allow for installation of easily available tubing like PVC (Figure 6).

3) Rinse hollow fibers as described in the contact angle procedure, then dry in dessicator.


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4) Place 4-5 dry, clean hollow fibers centrally inside piece of tubing mounted vertically in a holder, plug the bottom with Play-Doh or similar material.

5) Prepare marine grade epoxy mix. Start pouring epoxy inside tubing, when it has consistency of honey so it is too viscous to soak into the hollow fiber membrane pores.

6) Wait overnight for the epoxy to cure, cut central section (about 5 cm long) for measurement with tube cutter. Do not use saws, files or sandpaper – otherwise dust goes into the hollow fibers and is hard to remove. Flush the plug with running DI water, then condition in 2.0 mM KCl solution for 48 hours.

Figure 6. Hollow fiber porous plug compatible with ZETA-CAD instrument.

STREAMING POTENTIAL DATA ANALYSIS

Check that the internally calculated values for solution viscosity, η and permittivity, ε agree to within 0.5% with the values that are manually calculated using Eq.2:

7 4 -5 3 -3 2 -2

-6 3 -4 2

1.787 + 1.5615 10 t - 2.221 10 t + 1.4409 10 - 6.044 1087.9144 - 1.32802 10 + 9.58726 10 - 0.404399 t

= t t= t t

η

ε

−× × × ×

× × Equation 2

where t is the solution temperature (°C). If agreement is satisfactory, then the zeta potential that is automatically calculated by the streaming potential analyzer is acceptable. Otherwise, use the Helmholtz-Smoluchowski equation (Eq. 3) and the values for η and ε that were determined using Eq. 2 to manually calculate zeta potential:

0

( )( )

E tp t

ληζε ε

= Equation 3

For determining the average, standard deviation, and 95% confidence level at least three measurements should be per sample per pH and electrolyte concentration. The noted statistics should be reported for each test condition.



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The following recommendations are also suggested in addition to the standard method proposed earlier for carrying out streaming potential measurements on flat-sheet and hollow fiber membranes:

• Silver chloride electrodes are preferable over platinum electrodes,

• Sample conditioning (soaking time, solution chemistry) is of the utmost importance for obtaining reproducible results both within a single laboratory and between laboratories. When comparing zeta potential results for membranes it is highly recommended that the sample condition procedures used at each laboratory be published and compared,

• Design of a standardized streaming potential measuring cell that could be used with all electrokinetic analyzers is desirable for further improving the reproducibility in streaming potential results.


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CHAPTER 3 MEMBRANE SURFACE ROUGHNESS - AFM MEASUREMENTS

GENERAL DISCUSSION

Surface roughness is an important property in determining membrane fouling propensity.

Surface roughness may be measured with atomic level resolution using an atomic force microscope (AFM). AFM is widely used to characterize the physical and chemical properties of membrane surfaces. The procedure that is outlined here specifically pertains to the characterization of membrane surface roughness. An AFM operates using a light-lever technique, where a laser is reflected off the back of a cantilever and its position determined using a photo-diode. A fine tip exists on the end of the cantilever and is scanned over a surface using any number of operating modes (e.g., tapping, contact). Tapping mode is most commonly used to characterize membrane surfaces as contact mode may result in damage to the membrane surface. Tapping mode uses rapidly oscillating cantilever in the vicinity of the surface, and amplitude damping is used for imaging. Only short intermittent contact of AFM tip with sample (tapping) occurs, which is especially suitable membrane surfaces. Measuring the position and movement of the cantilever as it is scanned over a membrane surface allows for the direct measurement of surface features.

The data that are collected by the AFM during imaging can be subsequently analyzed and

used to produce 3-D rendered images of the membrane surface and for calculating a variety of surface roughness statistics, such as average roughness (Ra), root mean square (RMS) roughness (Rq), and surface area difference. Of these various parameters, Rq and Ra are the most widely used to describe the morphology of membrane surfaces. The RMS roughness, Rq, is a population standard deviation for a set of z values (depth coordinate) collected for a given area, where zi are heights (z-coordinates) from the AFM scan, N is number of data points (i = 1,2,…,N), and i is data index.

2( )i avgq

z zR

N−

= ∑ Equation 4

The average roughness uses averaged absolute values of deviations from average:

| |−

= ∑ i avga

z zR

N Equation 5

AFM APPARATUS AND SOFTWARE

AFM systems and supplies for imaging membrane samples may be purchased from a variety of vendors. A wet-cell accessory is recommended for imaging membrane samples and is required for imaging “wet” membranes. AFMs are typically supplied with vendor specific image analysis software, though there are numerous image analysis programs that may be purchased for analyzing AFM generated data. Due to the nature of the instrument specific site constraints and precautions must be utilized (e.g., vibration dampening, temperature control, etc.). The AFM vendor should be contacted to determine instrument specific requirements.


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Water described here is the same as proposed for contact angle measurements. Ultrapure

water, like ASTM type I, degrades very quickly when exposed to ambient air, if conductivity is considered as primary purity indicator. We propose that conductivity of water used here should be 0.055 µS cm-1 at the source, but it should absorb atmospheric CO2 in a controlled way. Otherwise absorption will occur at the time of measurement anyway (wet cell is not protected from atmospheric CO2), and characteristics of original water will not apply. ASTM Type I water shall be stirred (PTFE magnetic stirrer) in ambient air in thoroughly cleaned borosilicate glass vessel, until conductivity reaches 0.7 µS cm-1.

Calibration standards are recommended for determining the accuracy and error that is associated with surface roughness measurements in any given laboratory. These may be purchased from the AFM vendor.

PROCEDURE

Procedure for Flat Membranes

1) Acquire image of at least one z-calibration standard in air in tapping mode. Verify that measured depth is in agreement with certified value.

2) Rinse membrane three times with saturated ultrapure water, store completely immersed in saturated ultrapure water at 5°C at least 48 hours prior to measurement.

3) Prepare liquid cell and tip for tapping mode measurement in liquid.

4) Cut coupon suitable for imaging and install in liquid cell, fill with saturated ultrapure water.

5) Follow instrument-specific instructions, set 10 μm ×10 μm scan area, 256 steps in X and Y direction; acquire data in tapping mode.

6) Record and describe any data processing procedures applied that may affect roughness, such as automatic leveling.

7) Calculate RMS roughness with instrument-specific software. Save raw data and create surface image.

8) Repeat measurements for three regions on three different coupons (for a total of nine measurements).

9) Report tip characteristic (tip curvature, material, etc.), instrument make and type, sample name and origin, measurement temperature and RMS roughness.

Procedure for Hollow Fibers


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1) Rinse sample three times with saturated ASTM Type I water, use syringe to rinse inside the fiber, store completely immersed in saturated ultrapure water at 5°C at least 48 hrs prior to measurement. Refer to the cleaning procedure for hollow fibers treated with preservatives in Section 1.4.2.

2) Prepare instrument as for flat-sheet membranes.

3) For inner surface imaging cut fiber with knife or blade (previously cleaned with isopropanol to prevent oil contamination often present on new steel blades) at a sharp angle to expose inside surface (Fig. 8). One of the participating labs reported better results with simple cut along the fiber axis.

4) Affix angle-cut hollow fiber material with two-sided tape to measurement cell, with exposed inside of the membrane upwards. Make sure angle-cut area does not interfere with tip movement. Fill cell with ASTM Type I water or its equivalent.

5) For imaging the outer surface of the membrane it is critical that the sample be properly mounted. If fiber slides on two-sided tape, cut is in the middle along the axis and affix with external surface facing upwards.

6) Proceed according to the procedure that was outlined for flat-sheet membranes (Section 3.4.1 Step 5).

a. Note: Make sure to report any baseline correction options different from flat sheet membranes (automatic leveling), e.g., if software integrated with the instrument allows for curved baseline correction.


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Figure 8. Membrane angle cut for inner surface imaging. AFM ROUGHNESS DATA ANALYSIS

Calculate the average, standard deviation, and 95% confidence level for Rq and Ra. A

minimum of six separate areas must be imaged on the membrane surface in order to generate proper statistics.


The following recommendations are made in addition to the previously described standard method for measuring the surface roughness of membrane surfaces using AFM:

• The scan area for curved surfaces (i.e., hollow fiber membranes) should not be larger than 5 µm2,

• A specially designed AFM tip (e.g., mounted at an angle) that is visible from above cantilever could be considered for imaging curved surfaces, and

• The AFM tip should have enough resolving power to define in detail at least a 20-30 nm surface feature, without “sticking” to the membrane.

1cm

2/3 1/3

AFM tip

Cutting

2/3 part for outside

1/3 part for inside image


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ABBREVIATIONS AFM atomic force microscopy ANOVA analysis of variance AP Anton Parr CSM Colorado School of Mines DU Duke University EKA Electro Kinetic Analyzer FKKT University of Maribor MF microfiltration MLR Multiple Linear Regression NF nanofiltration OCWD Orange County Water District PA polyamide PES polyethersulfone PP polypropylene RO reverse osmosis SEM scanning electron microscopy TEM transmission electron microscopy TFC thin film composite UCR University of California, Riverside UF ultrafiltration UNR University of Nevada, Reno WaterRF Water Research Foundation


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