Project_Career_Portfolio_11_2016

Project & Career

Portfolio

Name: Jordan Lopez Date: 11/04/16

Table of Contents

Cover Page Page 1

Thulium Laser Project: Technical Memorandum

Page 3

Junior Design: Biosensor Development (Excerpt)

Page 10

Senior Design Presentation: Reactor Simulation & Modeling (PowerPoint Excerpt)

Page 17

Senior Design Pitch: Process Summary (Excerpt)

Page 21

Thulium Laser Project:

Technical

Memorandum

Date Created: 08/10/2015

Technical Engineering Memorandum

Modeling/Testing of Thulium Dopants Within Liquid Media Jordan Lopez, Mentor David Hostutler

New Mexico Institute of Mining and Technology Air Force Research Laboratory/RDLT

Abstract: Testing lasing systems in glass media is a great expense in development of fiber

lasers. To replicate such action in a liquid solution would significantly reduce costs in fiber laser

development. Thus, an effort was undertaken to determine the similarity of absorbance spectra

between Nd3+ (a lanthanide (III) ion) in liquid solvents and Nd3+ in glass host material.

Absorbance spectra for Nd3+ were taken in water and methanol, and comparisons of spectra

were made between the spectra and an absorbance measurement of Nd3+ in Ca3Ga2Ge3O12, a

glass host material. 4 peaks between 530-1000nm were found to be similar among all media

tested. However, a common peak offset was found between the solvent media and the glass

host material. Using the absorbance spectra in Ca3Ga2Ge3O12 as reference, peak shifts were

determined to be +200±70cm-1 for water and +74±70cm-1 for methanol. By determining the

parameters that have changed between light absorbance in liquid solvents and glass for

lanthanide (III) ions, modeling Tm3+ glass doping in liquid solutions may be feasible.

Introduction:

AFRL has been doing extensive development of fiber lasers, using the final products in

integrated laser systems for various mission objectives. During development of fiber lasers, the

testing of different mixtures requires creating a fully-functional glass fiber for each mixture. The

processes are expensive, and the materials are very difficult to recover in the event of fiber

failure. Thus, by determining if lanthanide (III) ions have similar absorbance and emission

characteristics in liquid and glass media, the testing of different dopants in fiber can be done at

a lower cost via liquid media. The end goal of this project is to verify a Tm3+ - dopant system

that has its lasing transition at 852nm. This can be developed into a Tm3+ glass fiber laser pump

for a Cs laser. Glass fiber lasing systems generally have a narrower emission bandwidth (and

therefore higher pump efficiency) than diode lasers, thus making the final system more energy

efficient

Secondary goals are determining properties of the spectroscopic chemical shift for trivalent

lanthanide ions, and to develop a Tm3+ - dopant lasing system for the 2µm region.

If absorbance/emission characteristics are favorable, Nd3+ and Tm3+ solutions can be tested as a

gain medium in a new class of fiber laser that has a liquid core. Concentrations of lanthanide

ions can easily be changed, and heat dissipation issues can be mitigated by circulation of the

liquid within the hollow fiber.

Experiment:

Experimental Setup: 1. ThorLABS SLS201 halogen light source 2. CV10Q3500FS Quartz Cuvette 3. ThorLABS CVH100 Cuvette Holder 4. ThorLABS SP2 500-1000nm Spectrometer 5. AMD Athlon II 3.4GHz w/ SPLICCO Software 6. FB2000-500 Bandpass Filter 7. PDA10D InGaAs detector 8. Fluke Multimeter

A simple absorbance and emission detection setup was created for use with quartz cuvettes.

Light from (1) passes through a fiber optic to enter the cuvette holder (3), where it then passes

through the quartz cuvette (2) containing the solution to be examined. from there, light exits

along two different paths. The first path (6-7-8) is the emission detection path. A bandpass filter

(6) allows only relevant wavelengths of ~2µm through to the photodetector (7), which has a

changing voltage in response to light. This voltage signal is detected by the voltmeter (8), where

the resultant value is hand-recorded. The second path (4-5) is the absorbance detection path. A

550-1000nm spectrometer (4) captures the light from (1) that entered and exited the cuvette.

The data that (4) takes is recorded by a computer (5), for later use in data analysis.

Emissions Detection

The emission setup was tested for sensitivity by using white printer paper placed in the cuvette

holder to simulate a highly fluorescent compound at the ~2µm wavelength. The range of

operation for the photodetector was 0-10 V. A consistent change of ~0.12V was found between

having white paper inside of the holder, and having nothing there. As this was considered

relatively insensitive, the next experiment undertaken was to test the photodetector with

direct light. A change of ~.44V was detected. Upon further examination, the fiber optic coupling

the light source to the cuvette holder was found to severely absorb light in the mid-infrared

range (>1µm). This means that testing of the emission detector will be impossible until a strong

mid-infrared source is found, or fluorescence in the mid-infrared range is found. In future

phases of the project, stronger light sources will be used (such as a 785nm 60W diode laser),

and various Tm3+ compounds will be tested, with possible fluorescence in the ~2µm range.

However, the emission detector setup cannot be guaranteed to be sensitive enough to detect

relevant emissions that could be generated.

Absorbance Detection

For the absorbance setup, sensitivity and accuracy of the spectrometer was examined.

A graph of absolute error vs wavelength for the spectrometer used in absorbance measurements.

For accuracy determination, a series of atomic discharge tubes filled with various noble gases

(Ne, Kr, Xe) was examined using the spectrometer for the absorbance setup. The detected

peaks were then compared to accepted emission lines for the same elements. These

comparisons were then used to determine the mean offset and spread of the differences

0.0

0.2

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0.6

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1.0

1.2

550 600 650 700 750 800 850 900 950 1000

De

viat

ion

fro

m N

IST

line

(n

m)

Wavelength (nm)

Spectrometer Error

Error

between the peaks. An offset of +0.5nm and a standard deviation of 0.3nm was found for the

data set. This lead to a 99% confidence interval of ±0.8nm for the spectrometer.

For sensitivity measurements, an absorbance spectra for Nd3+ in methanol (MeOH) was taken

to see if hallmark absorbance peaks could be seen in the resulting spectra. The baseline would

be detection of the 808nm peak; if this peak could be detected, then the spectrometer was

considered sensitive enough to be used for absorbance series.

The absorbance spectra for Tris-Nd3+ in methanol is shown above. The absorbance peaks

corresponds to expected peaks, with a small absorbance peak around 808nm being the desired

outcome.

Software Development

Software was written in C++ to read in spectroscopy files and convert to intensity vs.

wavelength tables. In addition, functions were written to enable easy statistical manipulation of

spectroscopy data, such as subtraction of background, determination of standard deviation, T-

test between spectroscopic groups, and accurate smoothing of spectroscopic data. Algorithms

for the student T-test probability function, as well as an adapted Fast Fourier Transform (FFT)

algorithm was used in creation of these functions. All of the functions were encapsulated in

class structures, for ease of use in future written programs. Differences in processing time

between using standard applications and written software were substantial. Time taken to

create processed data sets ready to be graphed using the software took between 1/10 to 1/8 of

the time it would take to complete the same task using notepad++ and Excel. Source code of

software and executables will be left to the successor of the thulium project to assist in analysis

of spectroscopy data.

804 nm ±1nm

748 nm ±1nm

737 nm ±1nm

581 nm ±1nm

572 nm ±1nm

Data/Results:

In black is the absorbance spectra of Nd3+ gathered from Ca3Ga2Ge3O12, and in blue is the

absorbance spectra gathered from Nd3+ in H2O. The absorbance spectra from water is amplified

by a factor of 5 in order to facilitate comparison between spectra.

Above is a comparison of peak shifts for Nd3+ in H2O and MeOH, with the reference peaks being

the absorbance peaks from Ca3Ga2Ge3O12.

Discussion:

There appears to be a good match between absorbance profiles of Nd3+ in Ca3Ga2Ge3O12 and in

H2O. Relative intensities of the peaks, as well as relative spacing between the peaks, suggests

that absorption processes for Nd3+ in water and glass is very similar. However, a blue shift of

peaks was noted in the H2O solvent, thus potentially throwing off absorbance comparisons

between the H2O medium and the glass medium.

The peak shift chart shows the shifts of the readily-identifiable peaks of Nd3+ as the ion is

subjected to various chemical environments. Nd3+ in Ca3Ga2Ge3O12 glass is considered to have

zero shift. The standard deviation is large for both measured shifts, but it can be seen that there

is a greater blue shift of the absorbance peaks for the water. This shift can potentially be

explained by a common trend that occurs with visible absorbance spectroscopy. As a solvent

becomes more polar, the absorbance spectra of the dissolved compound becomes blue shifted.

This is due to the dipole moment of the solvent lowering the ground state energy of the

absorbing molecule. Anisotropic effects, such as the shape of the Nd3+ solvation sphere and the

shape of the orbital for the transition considered, may be responsible for the large spread of

peak shifts among the transitions considered.

Conclusion

Software was developed and hardware was analyzed in preparation of taking absorbance and

emission readings of lanthanide (III) ions, specifically Neodymium (Nd) and Thulium (Tm).

Absorption spectra for Nd3+ in water and methanol was taken, and compared to literature data

of Nd3+ in Ca3Ga2Ge3O12 glass. 4 peaks were found to match based upon relative spacing and

relative intensity of the peaks. However, an average positive shift in wavenumber of the peaks

was found for the solvents when compared to the glass absorbance. This shift was found to be

greater for water than for methanol. A large spread was found between the peak shifts for

individual peaks.

Much remains to be done to verify and develop a dopant system for a 852nm Tm3+ fiber laser.

Absorbance series for Tm3+ in various chemical environments (different ligands, different

solvents, different solvent modifiers, etc.) must be taken. Current instruments to read

emissions are relatively insensitive, and must either be used with a high power light source

(diode laser) or be modified to increase sensitivity.

In the future, it would be beneficial to look at other heavy aqueous ions that are chemically

similar to lanthanide ions, such as Bismuth (III) ions. These ions could serve as an additive that

reduces solvent vibrational modes associated with absorbance of desired emissions. Also,

multi-dentate ligands such as EDTA and citric acid would provide possible

suppression/enhancement of desired absorbance or emittance transitions for lanthanide ions.

Junior Design: Biosensor Development

(Excerpt)


3.5 Sensors and Data Acquisition

3.5a Purpose of Sensors

The purpose of our bioreactor test bed is to gain new information on the problem of growing

algae under industrial conditions that would be encountered within a typical algae bioreactor. A

bioreactor is a dynamic system that produces biological chemicals or organisms as the product.

They are fairly sensitive to input conditions. Without detailed and precise information on the

inputs and outputs of the photo-bioreactor, it becomes difficult to design an optimal environment

for maximum production of algae. Simple data on the end result of algae production does not

provide enough information for improvement.

3.5b Overview of Sensors Used

Throughout the project, there were a few main devices that we monitored to assess the state and

health of our algae photo-bioreactor. Algae must remain within a specific pH range, temperature

range, and cell concentration range or adverse effects may occur. These metrics were selected

based upon how useful they were in determining algae health. There was a pH meter (to monitor

pH within the system), a thermometer (to monitor temperature within the system), and a

photometric sensor (to monitor algal density within the system). The thermometer and pH meter

were easy to find and utilize for our system. However, the photometric sensor had a few initial

problems.

3.5c Photometric Sensors: Initial Design

Many commercial versions of the photometric sensor were prohibitively expensive units well

outside of the range of the junior design budget. We chose to create a photometric sensor with

easily available electronic components. This involved three different design considerations: one,

choosing a light source of an appropriate wavelength that will be absorbed by living algae; two,

choosing a sensor capable of detecting and measuring intensity of the chosen light source; and

three, designing circuits capable of a) keeping the light source at a steady intensity, and b)

amplifying the sensor signal to a sufficient level for detection by a voltmeter.

The first design consideration was dealt with by looking at the absorption spectra of chlorophyll

a and b for the visible range; this was done as these pigments are most common in C. Vulgaris12

.

The absorption peaks tended to hover around a wavelength of 450 nm or 650 nm, therefore both

450 and 650 nm wavelengths were considered as potential light sources. We decided to use a

light-emitting-diode (or LED for short) to generate 450 or 650 nm wavelength light, as these

light sources are mostly monochromatic, energy efficient, and provide reliable performance22

.

When it came to the second design consideration, we found that there were few sensors that

could reliably detect light within the visible range. Of those sensors that could detect visible

light, we found that they were most sensitive to intensity changes along the upper half of the

visible spectrum22

. Thus, we selected a light source with a wavelength of 650 nm, as well as the

sensor type most sensitive to this wavelength range: a PiN photodiode.

For the third design consideration, multiple circuits were used to improve performance of the

photometric sensor to the point where it could be used to detect algal concentrations. The

intensity of the light source was kept constant using an operational amplifier running in a

negative-feedback mode. This allowed the operational amplifier to act as a constant current

source, thus providing the LED with constant power, which generates constant light23, 24

. For the

sensor, a packaged PiN photodiode with a Darlington pair amplification stage provided enough

amplification of the signal to determine slight changes in light intensity with a voltmeter24

. A

simple diagram of the sensor system is shown in Figure 9:

Figure 9: a simple diagram of the photometric sensor system

For extra usability, an Arduino microcontroller was used to sample the voltage difference from

the sensor circuit, and send the data to a computer at regular intervals of 1 – 5 seconds. This data

was then used for the next stage of photometric sensor development, the calibration of the

sensor.

3.5d Calibration of the Photometric Sensor

It was decided that the most reliable way to calibrate the photometric sensor was to couple

readings from the sensor to direct cell counts from an optical microscope. There were a couple of

hurdles to overcome to make this method viable. First, a method to obtain algal concentration

from counting algae under the microscope had to be determined. Second, the signal from the

photometric sensor had to be stabilized against external factors to make the calibration as

accurate as possible.

The first hurdle was overcome by looking over basic biology sources and determining what they

use to measure lengths, areas, and volumes underneath the microscope25,26

. Equation 11 shows

how a length seen in the field of view corresponds to the real length under the microscope:

Real Length = Length seen under microscope * Magnification power Equation 11

This is commonly used to calibrate microscopes and determine the diameter of the field of view

under the microscope. However, for this equation to work well, we would have to attain a

microscope slide with an accurate ruler etched into it. Therefore, for our measurements, a second

equation was used along with equation 11 to determine lengths and areas underneath the

microscope (Equation 12):

Field Radius = tan(𝑎𝑛𝑔𝑙𝑒 𝑜𝑓 𝑓𝑖𝑒𝑙𝑑) ∗ 𝑓𝑜𝑐𝑎𝑙 𝑙𝑒𝑛𝑔𝑡ℎ Equation 12

Once the field radius was known, the area of the field of view could be determined. To

extrapolate a volume of water under observation from this, the initial sample from the reactor is

weighted with an analytical balance, and the sample water is assumed to distribute itself evenly

over the area of the cover slip. From that and the density of water, a thickness of water is

calculated. This is multiplied by the area of the field of view to end up with the volume of water

under observation.

For the actual microscope counts, the microscope was brought into focus using the 300x primary

stage onto the sample. Then, all objects that appeared to be spherical and green, with an

appearance similar to a C. Vulgaris sample observed previously, were counted in the field of

view. This number was noted in a log. Then the slide was shifted to a new section, and the count

repeated. This routine was repeated 20 times with every sample examined; the numbers were

combined into an average microscope count, and were converted into an algal concentration

using data previously gathered about the sample.

For the first attempt of calibration, the photometric sensor and light source were arranged

opposite to each other in a tube assembly that surrounded a section of the photo-bioreactor

tubing. This assembly was covered in denim and taped on both sides using white gaffer’s tape.

Figure 10 displays the calibration curve that resulted from this arrangement:

Figure 10: the Sensor Calibration curve from the first sensor arrangement

It is seen that this calibration curve is very noisy and has no predictive power for cell

concentrations from sensor values. Thus, work was done to identify and remove the source of

noise within the sensor system. It was discovered through regular monitoring of the sensor that

ambient light conditions affected the sensor value greatly, by upwards of 50 units. To remedy

this source of noise, a tarp was placed over the photo-bioreactor; this remedied the sensor

fluctuations immediately. Figure 11 displays the calibration curve with the tarp:

y = 3163.4x - 47085 R² = 0.0629

0.0E+00

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100 110 120 130

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cells

/mL)

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Sensor Calibration

Sensor Avg

Linear (Sensor Avg)

Figure 11: the Sensor Calibration curve after a tarp was added to the system.

3.5e Photometric Sensor Use and Problems

Now, with a useable calibration curve, the photometric sensor could be used for determining

growth rates from the photo-bioreactor. Figure 12 displays a graph of the natural log of cell

concentration for a 3 hour run, along with the linear relationship for the data set:

Figure 12. a graph of cell concentration over time, with a linear fit of the data, that is

extrapolated from the photometric sensor.

From this data, we could determine that the growth rate for the reactor during that period of time

was roughly 1.4% per hour. This fits in with analysis from previous sections about the kinetics of

y = -28877x + 3E+06 R² = 0.9973

0.0E+00

1.0E+05

2.0E+05

3.0E+05

4.0E+05

5.0E+05

6.0E+05

7.0E+05

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9.0E+05

60 65 70 75 80 85 90

Ce

ll C

on

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trat

ion

(ce

lls/m

L)

Sensor Value

Sensor Calibration: With Tarp

Sensor Avg

Linear (Sensor Avg)

y = 0.0139x + 14.147 R² = 0.9413

14.13

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Current Growth Rate

Trial 1

Linear (Trial 1)

algae growth within a limited system; previously measured growth rates were higher at about

3.5%, and the slowing growth rate measured here tells us that the algae were beginning to reach

levels where photo-inhibitance was predominant.

The photometric sensor wasn’t perfect, however. Bubbles and foam occurred throughout our

system and tended to reflect light. When these bubbles and foam reached our photometric sensor,

they disrupted the sensor and resulted in extremely low algae counts from the sensor. This is

exemplified in figure 13 by the incredible sudden dips in algae density observed over a 9 hour

stretch:

Figure 13: A graph of algal density over 9 hours of measurement

In future designs, the location and method of sampling for the photometric sensor will be refined

to prevent errors such as the one above, and will be improved so that outside noise is kept to a

minimum.

-1.00E+06

-5.00E+05

0.00E+00

5.00E+05

1.00E+06

1.50E+06

2.00E+06

0 2 4 6 8

Ap

par

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Time (hrs)

Sensor: Problems

Series1

Sources for Excerpt:

[12] G. Hill, P. Mitra, D. Sasi, A. Vigueras, Growth kinetics and lipid production using chlorella

Vulgaris in a circulating loop photo-bioreactor, Journal of Chemical Technology and Biotechnology,

2011

[22] Dakin, John P., and Robert G. W. Brown. Handbook of Optoelectronics Volume I. Boca Raton, FL:

CRC Press, 2006.

[23] El-Osery, Dr. Ali. 2011.

[24] Malvino, Albert Paul Ph. D. Transistor Circuit Approximations. United States of America: McGraw-

Hill, 1973.

[25] Caprette, David R. "Measuring with the Microscope." Rice University Website.

[26] Schell, Wendy, Kevin Schmidt, and Michelle Strube. "The Microscope: Estimating the Size of an

Object and Preparing Biological Drawings."

Senior Design

Presentation:

Reactor Simulation and

Modeling

(Excerpt)


Senior Design Pitch: Process Summary

(Excerpt)


Process Flow Diagram

There are two process flow diagrams made for comparison of two different cases of

Metformin manufacturing. However, both process flow diagrams share the same common stages;

these stages will be discussed in this section. There are 4 main stages: the reactor, impurity

removal, refinement of Metformin, and tablet creation. Both of our process flow diagrams can be

seen in appendix 1.

The first stage we will examine is the reactor. This is where Metformin is synthesized.

All other stages are used to purify/modify the raw metformin coming out of this stage. There are

supply lines/storage tanks that contain the materials necessary to manufacture Metformin: 2-

cyanoguanidine (dicyandiamide) and dimethylamine HCl. In addition, there is a supply

line/storage tank for the solvent for the reaction. Currently, this solvent is isoamyl alcohol as its

use eliminates the need of a liquid-liquid extractor. These three compounds are added in series

into the reactor, with isoamyl alcohol being the first compound to be added and 2-

cyanoguanidine being the last compound to be added to the reactor. The reactor is then heated to

the boiling point of the isoamyl alcohol and stirred at 120 rpm. The reaction mixture is kept

under constant reflux with stirring for a period of 12 hours. The reactor continues to mix as the

mixture is cooled and the metformin comes out of solution. At this point, the metformin has been

synthesized with over 95% yield. When the mixture has cooled to room temperature, the

metformin-solvent slurry is pumped to a filter. The filter separates the metformin from the

solvent, from which the metformin is sent off to the other stages. The solvent is recovered and

either dumped as waste or is recycled back into the reactor for another batch.

The second stage examined is the impurity removal stage. First, the metformin is dumped

into a mixer, where it is dissolved in 3 times its own weight of methanol at 50°C. Then this

solution of crude metformin is run through two adsorption towers, which adsorb most of the

impurities found in metformin. Adsorption is used, as the impurities are relatively dilute (~ 0.01

mol fraction), and there is evidence that this form of impurity removal is used in metformin

manufacturing. The first tower uses small pore-sized activated carbon to absorb small non-polar

impurities such as melamine from the raw metformin stream. The second tower uses activated

diatomaceous earth to remove polar impurities, such as dimethylamine, from the stream. After

this stage most impurities are present as 0.1 parts per thousand or less.

The third stage, Metformin refinement, takes the impurities present and brings them

down into the 10-100 ppm range. In this stage, the metformin solution is brought into a

recrystallizer. The solution is cooled while being gently stirred, and crystals of metformin are

formed while this cooling process takes place. After reaching temperatures of 0°C, the slurry of

metformin crystals and chilled methanol is filtered, with the chilled methanol being recycled and

the metformin continuing on to the grinding step.

In grinding, the recrystallized metformin is ground into a fine powder that is later shipped

in the API process but moves on to tablet making in the integrated process. This pulverizing

allows impurity inclusions within the crystallized metformin to be exposed, so that subsequent

rinsing steps can dissolve the impurities. Thus, the next step is to place that powder into a

washing tank, where the powder is washed with chilled methanol three times. The now pure

metformin is then transferred into the next step of the process, tablet formation.

The fourth stage comprises the set of operations required to transform a wet filter cake of

Metformin crystals into the formulated tablet form. Slightly wet filter cake from the final

filtration step undergoes size reduction by wet grinding to a size suitable for tabulation. The

ground product is then transferred to a tray type dryer that uses warm air to remove the

remaining methanol solvent. The dryer conveys product to the wet granulator at which point the

excipient materials microcrystalline cellulose, and Polyethylene Glycol are added with a

controlled amount of water to form granules of sufficient size for compaction during the

granulating process. The final step of the process is tableting of the moistened granules. A rotary

type tablet press was selected because of the excellent compression control and speed of

operation. For this process a small tablet press has the required capacity for handling the entire

batch size quickly leaving the option of having multiple production lines feeding through one

tablet press.

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