MS3000 PARTICLE SIZE DATA QUALITY - PANalytical · 2020-06-21 · Obscuration: The amount of laser...

© Malvern Panalytical

MS3000 – PARTICLE SIZE DATA QUALITY

Calvin Wong – Application Specialist

[email protected]


DATA QUALITY


DATA QUALITY - INTRODUCTION

• What do we mean by data?

• Data is the fundamental light scattering caused by your sample• It is not the particle size result• It is independent of the optical model.

• A stable result requires stable data


DATA QUALITY - INTRODUCTION

• What Constitutes Quality Data?

• A good background measurement, taking into account • cleanliness of cell windows and dispersant• alignment of the system• stability over time

• Sample data• gaining sufficient signal to noise• avoiding negative data• limiting the effect of multiple scattering• avoiding beam steering

In general this applies to all laser diffraction instruments


DATA COMPONENTS -TYPES OF DATA

Background data

Sample data


BACKGROUND DATA AND SYSTEM CLEANLINESS

• A good measurement requires a clean, stable background

• This should show progressive decrease across the detector range

Less than 20 scattering

units by detector 20

Less than 100 units

on detector 1


A CLEAN BACKGROUND – WET SYSTEM

• A good clean background on a wet system should look very similar to this...

• Less than 100 units on the 1st detector• Limited fluctuations over time


A CLEAN BACKGROUND - DRY SYSTEM

The flow of air through a dry cell will cause a slightly higher degree of

fluctuation over time than seen in a wet system

▪ The same decrease in the signal is observed across the detector

range.

▪ The same maximum acceptable value of 100 applies


POOR BACKGROUND - MATERIAL STUCK TO THE WINDOWS

• A ‘hump’ in the data is often an indication of material stuck to the cell

windows

Dirty Windows?


POOR BACKGROUND –CONTAMINATED DISPERSANT

• Particulate contamination causes fluctuations in the background over time

• Bubbles in the dispersant can also result in a similar background


POOR BACKGROUND – MISALIGNED SYSTEM.

• Castellation indicates a mis-aligned system.

• This may be caused by:• Dirt on the cell windows. Address these and then re-align.

• Changes in the temperature of the dispersant

Castellation


POOR BACKGROUNDS –THERMAL GRADIENTS IN ORGANIC DISPERSANTS

• The sample dispersion unit may be at 30°C+, while the solvent is

generally much cooler

• This gives rise to thermal gradients resulting in fluctuations in the

laser beam, alignment problems and poor backgrounds

Thermal gradient in dispersant


HOW WILL I RECOGNISE THERMAL GRADIENTS?

- By wild instability of the scattering on the first few detectors….

• After a few minutes the background should stabilise as the temperature

differences equilibrate.

• If it does not stabilise, fit the cover of the dispersion unit to reduce surface

evaporation.


POOR BACKGROUNDS – A FINAL CHECK...

• After background subtraction, the inspection window shows;• Random scattering data

• Positive and negative data

• Any ‘blocks’ of scattering will indicate dispersant contamination


SAMPLE DATA – ADDING THE RIGHT AMOUNT OF SAMPLE

Terms used in this section:

Obscuration: The amount of laser light blocked and /or

scattered by the sample: a guide to the sample concentration

Signal-to-noise ratio: The magnitude of the sample data in

relation to the background data

Multiple scattering: The phenomenon caused by light

scattered by a particle being scattered by other particles when

too much sample is added


SAMPLE ADDITION

How much sample should be added to the dispersion unit?

▪ Too little:

• Signal to noise ratio may be poor, or:

• Not enough sample may have been added to be representative of

the bulk - particularly if the sample is very polydisperse

▪ Too much:

• Multiple scattering may affect the reported particle size distribution -

particularly if the material is small (typically < 10 microns)

What is the ‘correct obscuration?’


SAMPLE ADDITION –GUIDE TO TARGET OBSCURATION VALUES

• Wet measurement

• If the sample is fine, a low obscuration is best, ~5-10% will improve the signal to noise ratio

• If the sample is coarser, obscuration can be increased to 5%~12%

• If the sample is very polydisperse, 15-20% may be needed to improve sampling

• Too high an obscuration will give multiple scattering

• Dry measurement

• Fine 0.5%~3%• Coarser 0.5%~6%• Keep the obscuration within

the required values by enabling the obscuration filter in the software.

• Too high an obscuration reduces dispersion efficiency and leads to false peaks


SIGNAL TO NOISE RATIOS – LARGE PARTICLES

(42.58 micron glass beads measured at an obscuration of only 7%)

Note that the signal to noise ratio is usually high for large particles

because these scatter light more strongly.

Consequently, signal to noise ratio is less of an issue for large particles.

High sample signal

to background

ratio

Maximum signal ~250units

High sample signal

to background

ratio


SIGNAL TO NOISE RATIOS - SMALL PARTICLES

(1 micron Latex measured at an obscuration of 5% )

Maximum signal ~8units

The signal to noise ratio is the amount of sample data relative to the

background data.

Because small particles scatter light weakly, it is important that the

background does not swamp the data signal.

However, in this graph, the data is good since it falls where there is little or

no overlap between the sample data and the background data.


Detector

High detector

number

Low detector

number

Wet cell

Increasing the obscuration (concentration) makes multiple scattering more likely.

This leads to higher angle scattering which corresponds to smaller particles.

High detector number

Scattering indicates

small particles

MULTIPLE SCATTERING


NEGATIVE DATA - INTRODUCTION

• If the background is measured before the dispersant stabilises then• The background is over-subtracted

• The inspection stage shows significant negative data

• If this occurs, skip back and re-measure the background


HOW TO IMPROVE DRY MEASUREMENT DATA QUALITY?

• Check:• Are the cell windows clean?• Is the air pressure correct?• Does the air filter need changing?

• Is there oil droplet contamination or moisture in the air supply?

• Is the feeder earthed against static electricity ?• Is the vacuum bag full?

• Is the sample flow even?• if the sample obscuration is high, try lowering the feed rate or adjusting the hopper height• try changing the height of the hopper, different basket, ball bearing• try a different feed tray: often one tray design will deliver a more even sample flow for a particular material

• Use Fine Powder Mode when material less than 10 microns is present


SUMMARY - DATA QUALITY

• Background data• Make sure that:

• Material is not stuck to the cell windows• There is no dispersant contamination• There are no thermal gradients• That the system has been properly aligned

• Sample data• Check that

• There are reasonable signal to noise levels• There is no multiple scattering• There is no negative data• There is no noisy data • The inner detector data is free from castellation• There is no beam steering


OPTICAL

PROPERTIES


SCATTERING MODELS - FRAUNHOFER VS MIE

• Laser diffraction requires a model that accurately defines the light scattering behaviour of all

particles

• There are currently two popular choices available, Mie Theory and the Fraunhofer Approximation

• Mie theory has been found to be more accurate over a wider range of sizes – particularly for

particles less than 50 microns in size.


SCATTERING MODELS - FRAUNHOFER V MIE

• Older instruments used the less accurate Fraunhofer Approximation. This was due to limited

computer processing power.

• Since 1986 the preferred model has been Mie Theory which correctly predicts the scattering at all

wavelengths of light at all angles.


SCATTERING MODELS - MIE THEORY

• Unlike Fraunhofer, Mie theory:• Accounts for the interaction of light with matter

Assumes that spherical particles are present

• Is valid for all wavelengths, scattering angles and sizes of particle

• Correctly predicts scattering intensities(Correctly predicts secondary scattering)

• “For particles smaller than about 50µm the Mie theory offers the best general solution.”-ISO 13320-1


PREDICTED LIGHT SCATTERING- MIE THEORY

Incident light

Scattered light

Scattered light

Absorption


PREDICTED LIGHT SCATTERING - MIE THEORY

• The Mie scattering model takes into account light passing through the particle.

Angle of scatter

Scattering

Intensity

Particle size

Angle of

scatter


SCATTERING MODELS– FRAUNHOFER APPROXIMATION

• Accounts only for classical diffraction around a disc

Angle of scatter

Scattering

Intensity

Particle size

Angle of

scatter


SCATTERING MODELS- FRAUNHOFER APPROXIMATION

• Disadvantages• Will produce incorrect answers when…

• Particles are <50µm

• The angle of scatter becomes large and secondary scattering occurs.

• The relative refractive index is small (<1.3) - this equates to a particle refractive index of 1.73 in water.

• Claimed Advantage• “No need to know the optical properties of your material.” Which implies that optical properties are

required for Mie theory.


OPTICAL PROPERTIES – WHAT ARE THEY?

• For the application of Mie theory, 3 properties need to be known. These are:

• The Refractive index of the dispersant.

• The Refractive index of the sample material

• The Imaginary Refractive index of the sample material (often referred to as the absorption)


ESTIMATING ABSORPTION FROM PARTICLE APPEARANCE

0

0.001

0.01

0.1

1.0+

Latices

Emulsions

Slightly colored powders

Crystalline milled powders

Highly colored

(complementary) and metal

powders

Appearance Imaginary RI Example


ASSESSING THE FIT

• Once you have chosen an optical model, assess the result and the data “fit” to determine its suitability

• The data fit should only be used as a guide to the correctness of the chosen RI. • The best data fit does not always give the right result

• Is the result sensible for what you know about the manufacturing/production route?• Milled powders - should show tail of fines• Emulsions - typically log-normal distribution• Classified materials - display a cut–off at either end of the distribution


USING THE DATA FIT

• Here we see an overlay of the measured scattering data and the data predicted based on the

optical properties.

• The precision with which these two curves overlay is known as the “data fit”

• The ‘goodness’ of this fit is known as the ‘Residual’


INSPECTING FIT DATA: REFRACTIVE INDEX

• A poor fit in the low detectors indicates an incorrect choice of refractive index

.

Poor fit indicates

incorrect choice of

refractive index


INSPECTING FIT DATA : ABSORPTION INDEX

• Misfits to the extinction detectors 51 and 63 indicate incorrect absorption index.

Poor data fit here

indicates poor choice of

absorption.

Poor data fit here

indicates poor choice of

absorption.


Example - weighted

Example - unweighted

WEIGHTED AND UN-WEIGHTED DATA FITS


RESIDUALS - GOOD “RULE OF THUMB”

• For a normal size distribution, the residual should typically be less than 1%

• For a good fit, the weighted and unweighted residuals should be of similar

orders of magnitude

• A low residual is good, but the distribution should be believable

Poor

agreement

Good

agreement


ASSESSING THE FIT

• The user is seeing an “unexpected” mode of small material.

• The optical properties used were:• RI:1.4, Absorption: 0.01


ASSESSING THE FIT USING 1.4/0.01

Un-weighted fit

Weighted fit

Poor fit

– incorrect RI


ASSESSING THE FIT USING 1.59/0.01

Weighted fit

Un-Weighted fitImproved fit


EXAMPLE 1 – LOOKING AT THE RESULTS

• Sample is calcium carbonate• Reference RI is between 1.53 and 1.63


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MS3000 PARTICLE SIZE DATA QUALITY - PANalytical · 2020-06-21 · Obscuration: The amount of laser...

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Transcript of MS3000 PARTICLE SIZE DATA QUALITY - PANalytical · 2020-06-21 · Obscuration: The amount of laser...