JUCER Volume VI

May 2017

JUCER (Journal of Undergraduate Chemical Engineering Research)

Stony Brook University

Engineering 208

Stony Brook, NY 11794

Phone: (631) 632-6269

miriam.rafailovich@stonybrook.edu

Cover Art: Clockwise from Top Right

Top Right: Surface of 60 nm SBS film on a glass disk after 48 hours of annealing (See Nicole

Passariello, Megan Lenore, Asim Rattu et al., The Effects of SBS Tri-Block Copolymer Surface

Roughness on Protein Adsorption)

Center Right: Graphical model of initial carbon dioxide and water system (oxygen atoms are

red, hydrogen are white, and carbon are cyan) (See Sandhiya Kannan, John Mikhail, Raphael

Prodromou et.al., Pressure-Time Dependence and Simulation of Carbon Dioxide Hydrate

Formation)

Lower Center: Schematic of micellization and gelation of Pluronic F127 in the presence of

Vancomycin (See Kim Nguyen, Jessica Kelly, Michael Callan, Eungyo Jang et. al.

Characterization of Pluronic F127 for the Controlled Drug Release Vancomycin in the Spinal

Column)

Center Left: Diagram of salting-out assisted liquid-liquid extraction of unreacted furfuryl

alcohol monomers from its oligomers and polymers (See Patrick Yang, Carmenn Ooi, Marolyn

Liang, Jeong Suk Buyn et. al., Effect of Anhydrous Sodium Sulfate Salt Loading on Aqueous

Furfuryl Alcohol Washes Extracted from Furfuryl Alcohol Oligomers using Deionized Water)

JUCER is an annual publication by the Chemical and Molecular Engineering program by State

University of New York at Stony Brook.

ISSN: 2373-4221

Author Inquiries:

For inquiries relating to the submission of articles please contact Miriam Rafailovich

[miriam.rafailovich@stonybrook.edu]

1

2

Staff

Editor-In-Chiefs Patrick Yang

Angell Chee

Shweta Iyer

John Mikhail

Webmasters Sandhiya Kannan

Calvin Cheng

Steven Zhu

Faculty Advisor Dr. Miriam Rafailovich

Graduate Advisor Clement Marmorat

Associate Editors Adam Bennett

Veronica Burnett

Jeong Suk Byun

Mike Callan

Calvin Cheng

Vivian Cheng

Jillian Gannon

Henry Ho

Thomas Hurson

Anna Jang

Yunxiao (Shawn) Jiang

Sandhiya Kannan

Jessica Kelly

Steven Krim

Wooyoung Kwon

Christopher Lafergola

Kevin Lee

Megan Lenore

Jian Lin

Jinying Lin

Helen Liu

Jesse Matsuda

Mike McCutcheon

Kim Nguyen

Melanie Noye

Michael Okunewitch

Carmenn Ooi

Nicole Passariello

Raphael Prodromou

Dongni Qiu

Asim Rattu

Peter Ryzyk

Gurkirat Singh

Allen Tran

Danielle Wahl

Joshua Weinstein

Mailun Yang

Joo Yong Yi

Steven Zhu

3

A Letter from the Editors

We are pleased to present Volume VI of the Journal of Undergraduate Chemical Engineering

Research (JUCER). This volume is the culmination of a year of research from the Chemical &

Molecular Engineering class of 2017 at Stony Brook University. We hope that, in writing this

journal, the students have gained valuable insight in how to collaborate with their peers to

achieve scientific progress and how to communicate that progress to others in the academic

community.

JUCER is a peer-reviewed publication; every article included here has been approved by experts

in their respective fields. Each submission was written entirely by a team of undergraduate

students based on their own, unique work. This work involved studying the background of their

topics, performing experiments, and analyzing the data obtained from these experiments using

rigorous scientific principles learned over the course of their undergraduate career.

We would like to thank all of the students for their remarkable effort and the faculty for their

mentorship and guidance throughout this process. We are all incredibly proud of the results of

this endeavor and are delighted to present them here to be read and disseminated publicly.

Sincerely,

John Mikhail Patrick Yang Shweta Iyer Angell Chee

Co-Editors in Chief

Journal of Undergraduate Chemical Engineering Research

JUCER

4

Editor-In-Chiefs Patrick Yang is a graduating senior in the Chemical &

Molecular Engineering program at Stony Brook University

with Business Management specialization and Chemistry

minor. He has co-authored one conference publication through

Materials Today: Proceedings published by Elsevier and

another through ACS Sustainable Chemistry and Engineering

Journal it was also featured on the cover story of the journal.

Some of his research interests include renewable energy like

biomass conversion, catalysis, and polymers nanocomposites.

Following graduation, he will be participating in the Science

Undergraduate Laboratory Internship (SULI) at Argonne

National Lab and is planning to go into industry, possibly

returning for graduate studies.

Angell Chee is a 4th year student at Stony Brook University.

He is majoring in Chemical Engineering, minoring and

specializing in Computer Science. Angell is passionate about

challenging himself with new technologies and hopes to create

a business where he can contribute to the health, fitness and

computer science industries. He will work for Tata

Consultancy Services (TCS) as a software engineering

consultant and is looking forward to this new chapter of his

life.

John Mikhail is a student of Chemical & Molecular

Engineering and Applied Mathematics & Statistics with a

specialization in Computer Science. He does molecular

dynamics research with Professor Gersappe on interfacial slip

in polymers, Professors Koga and Endoh on carbon dioxide

hydrate formation, and Professor Michaelson on protein-

ligand docking. After his Bachelor's, John is pursuing

graduate studies in Chemical Engineering at MIT.

Shweta Iyer is the President of the Stony Brook University

chapter of AIChE. She has interned with Honeywell UOP, GE

Power, and GE Industrial Solutions, serving as a radionuclide

researcher, engineer, and pricing analyst, respectively. She

will be joining BAE Systems post-graduation and will pursue

an MBA.

5

Webmasters

Sandhiya Kannan is a graduating senior in Chemical and

Molecular Engineering with dual minors in Nanotechnology

and Astronomy. Her research experiences include her work at

Princeton University on a fractional distillation apparatus for

the removal of Po-210 from groundwater, an R&D internship

at Chemtura Corp. developing and testing petroleum

additives, her past research at Stony Brook on the synthesis of

gold and palladium nanoparticles, and her thesis project on the

formation of carbon dioxide hydrates. Upon

graduation, Sandhiya will be pursuing her Master's degree in

Chemical Engineering at Columbia University.

Calvin Cheng is a senior majoring in Chemical Engineering

with a specializing in computer science. He works on

simulations of a transdermal drug patch in order to build an

accurate model of a transdermal drug delivery system. He has

also researched under Dr. Taejin Kim to find an efficient

catalyst to help the reduction of NOx from fuel combustion.

After graduation, he plans to pursue a job in simulations and

modeling.

Steven Zhu is a Chemical and Molecular Engineering Major

of the Class of 2017. The research he worked on for his thesis

involved the simulation of a transdermal patch. His

specialization is in Energy and he plans to work in industry

post-graduation.

6

In Memory of Miguel Roncal

We dedicate Volume VI of the Journal of Undergraduate Chemical Engineering Research

to the memory of our beloved friend and classmate, Miguel Roncal. He embodied what it means

to be a true engineer: his energetic nature, ardent attention to detail, unwavering teamwork, and

ingenuity serve as continuous inspiration for our future academic and professional endeavors.

A Chemical and Molecular Engineering major with a Computer Science minor, Miguel

was a dedicated and hardworking student. He consistently scored well on exams and helped anyone

who struggled to grasp the material. Miguel was an excellent scholar and an even better colleague.

The years we were fortunate enough to share with Miguel were filled with happiness and

familial bonding. His many sayings, surprises, hugs, and jokes are forever missed but, greater still,

forever remembered.

7

Do it Up

By Shweta Iyer

A collection of memories from CME

When your car was parked too far, and you’d decided to drive a friend home

You bent the wheel of a bicycle, and laughed giddily away

Despite not knowing how to ride or brake

You never left anyone alone, if even for a minute you could stay.

Broken spokes could be mended; you built unbreakable ties

If a group splintered while crossing a street

You’d turn around and wait;

Even if the first half continued on, you’d never allow a break.

Perhaps the bike was one wheel too many

So naturally only a unicycle would do.

You determinedly fell and rode and fell again

Eliciting laughter from whomever were watching you.

A unique energy and a force of the purest love

Adventures with you are plenty.

You’ll heist toothpicks from the diner to make a proper gift

And ensure no hearts were empty.

Words cannot hope to express what even pictures won’t fully portray

The light, radiance, and gentle humor you embody inscribed in morning rays.

Your cheery nature and infectious laughter unfailingly constant and free

We’ll push up the tears and do it up forever in your memory.

8

Table of Contents Page(s)

9-19

20-29

30-40

41-49

50-57

58-66

78-84

85-91

92-101

102-110

111-121

Title

Characterization of Pluronic F127 for the Controlled

Drug Release Vancomycin in the Spinal Column

Effect of Surface Properties on Pluronic Deposition

Optimization of Synthesis Gas to Methanol

Conversion

The Effects of SBS Tri-Block Copolymer Surface

Roughness on Protein Adsorption

Transdermal Patch Simulation Using the Lattice

Boltzmann Method with Active Diffusion in the Cell

and Lipid Pathways

Effect of Anhydrous Sodium Sulfate Salt Loading

on Aqueous Furfuryl Alcohol Washes Extracted

from Furfuryl Alcohol Oligomers using Deionized

Water

Thermal Conductivity, Electrical Conductivity and

Mechanical Properties of Polypropylene/Graphene

and Polystyrene/ Graphene Nanocomposites

Effects of Graphene Oxide on Proton Exchange

Membrane Fuel Cells

Synthesis and Characterization of Gold-Palladium

Nanoparticles Catalyst For Improved Hydrogen Fuel

Cell Performance

Pressure-Time Dependence and Simulation of

Carbon Dioxide Hydrate Formation

Process Optimization for a Wood Stove with a

Combustion Catalyst

In-situ Water Management for the Optimization of

Methanol Dehydration to Dimethyl Ether

Author(s)

Michael Callan, Eungyo Jang,

Jessica Kelly, Kim Nguyen

Vivian Cheng, Christopher

Lafergola, Melanie Noye

Veronica Burnett, Shweta Iyer,

Steven Krim, Mike

McCutcheon, Gurkirat Singh

Megan Lenore, Nicole

Passariello, Asim Rattu

Angell Chee, Calvin Cheng,

Steven Zhu

Patrick Yang, Carmenn Ooi,

Marolyn Liang, Jeong Suk Byun

Peter Ryzyk, Thomas Hurson,

Joo Yong Yi, Joshua Weinstein

Henry Ho, Jesse Matsuda,

Mailun Yang

Adam Bennett, Helen Liu, Allen

Tran

Sandhiya Kannan, John Mikhail,

Raphael Prodromou

Yunxiao (Shawn) Jiang, Kevin

J. Lee, DongNi Qiu

Jillian Gannon, JinYing Lin,

Danielle Wahl 122-134

Characterization of Pluronic F127 for the Controlled Drug Release Vancomycin in the Spinal Column

Michael Callan1, Eungyo Jang1, Jessica Kelly1, Kim Nguyen1, Clement Marmorat2, Miriam Rafailovich1,2

1Department of Chemical and Molecular Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

2Department of Materials Science and Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

Abstract

Pluronic F127 is a unique hydrogel due to its sol-gel transition properties determined by temperature and concentration. By investigating its rheological properties and drug release capability, F127 can be used as a drug delivery system for the spinal column. In this study, rheological measurements including temperature sweeps and frequency sweeps were performed on various concentrations of F127 to characterize its physical structure at different conditions. For temperature sweeps, higher concentrations of F127 showed abrupt increase of viscosity at lower temperature. For frequency sweeps, the fracture point of Pluronic F127 with and without Vancomycin was observed at 18.42 Hz. The calculated flow rate at 18.42 Hz is (0.53 0.11) 10 ± × -6 m3/s, which is much higher than the flow rate of human spinal fluid. In addition, Pluronic F127’s drug release capability was tested via a flow chamber system where DI water is flowed at a constant rate over the hydrogel with antibiotic additive. UV-Vis spectroscopy was used to characterize the collected flow samples. From the flow test, a slow flow rate of 0.1 mL/min yielded higher release of F127 and Vancomycin as compared to higher flow rates of 0.5 mL/min and 1 mL/min.

Keywords: Pluronic, Vancomycin, hydrogel, drug release, rheology

1. Introduction

Hydrogels are three-dimensional networks formed by polymers that exhibit visco-elastic characteristics widely used in the field of cosmetic, pharmaceutical, and biomedical industries.[1] Pluronic is a triblock copolymer made up of hydrophilic end units, polyethylene oxide (PEO), with a hydrophobic center unit, polypropylene oxide (PPO).[2] Pluronic exhibits unique thermoreversible gelling capability.[3] At low

temperature in solution, Pluronic exists as single chain polymers (unimers) (Figure 1a).[4] As temperature is increased, the solubility of the PPO unit is lowered giving rise to formation of micelles (Figure 1b).[3] The hydrophobic blocks form the inner core while the hydrophilic blocks form the exterior corona of the micelle.[5] As more micelles are formed, the coronas of the micelles overlap leading to gel formation (Figure 1c).[5] Pluronic micelles self-assemble into a face-centered cubic (fcc) lattice structure.[6]

9

Figure 1. (a) Pluronic unimers in solution, (b) micelle formation, (c) micelles arranged in cubic structure.

Pluronic is biocompatible and FDA approved which makes it a desirable material to have as a drug delivery system. Pluronic is a non-cell adhesive material that can be used as a physical barrier to prevent scar tissue formation. Oh et al. studied the drug delivery of ibuprofen in Pluronic F127/F68 crosslinked with alginate as a physical barrier for adhesion prevention using in vitro and in vivo studies.[7] Their results showed a stable gel at 30 °C with sustained release of ibuprofen up to 45% of total loading amount. The Pluronic F127/F68/alginate/Ibuprofen gel was shown to be an effective material to prevent tissue adhesion formation. Therefore, it has been proposed to inject Pluronic after spinal surgery to prevent adhesion while delivering antibiotics.

In the spinal column of adults, there is continuous perfusion of cerebrospinal fluid at a rate of 400 to 600 ml per day,[8] which is equivalent to 0.28 to 0.42 ml per minute. Pluronic’s sol-gel phase transition is dependent upon the concentration and temperature of the material where Pluronic is a solution under a critical gelation concentration and temperature. Therefore, the gel stability of Pluronic in the spinal column is noteworthy to investigate to determine Pluronic’s effectiveness as a physical barrier to prevent cell adhesion.

Vancomycin is an antibiotic used to treat infections such as osteomyelitis.[9] The optimal dosage of Vancomycin as an

injectable solution is 5 mg/mL for adults.[10] This dosage can vary by age and weight of patients. Vancomycin does not impede bone fracture healing.[9] Hence, administering Vancomycin into the spinal column after spinal surgery can prevent surgical site infections.

In this study, we investigated Pluronic F127’s gel stability under various shear flows simulating cerebrospinal fluid flow to determine the dissolution time of Pluronic. Rheological measurements of Pluronic F127 were conducted to obtain critical gelation temperature of Pluronic at various concentrations and to observe the gel structure at increasing frequency sweeps. We conducted a drug release study of antibiotic Vancomycin to determine the drug elution of Vancomycin and the effects of flow on its release. The drug release study was performed using a flow chamber where DI water was flowed through the Pluronic gel containing Vancomycin at constant flow rates of 0.1, 0.5, and 1 mL/min. Samples collected from the flow chamber were then characterized via UV-Vis spectroscopy.

2. Methods

2.1. Chemicals and Materials

Pluronic F127 (MW = 12600 g/mol, Sigma-Aldrich, Saint Louis, MO, USA) and Vancomycin hydrochloride hydrate (CAS-No. 123409-00-7, Sigma Aldrich, Saint Louis,

10

MO, USA) were used as received without any further purification.

2.2 Preparation of Pluronic F127 Hydrogel

Pluronic F127 hydrogels were prepared by mixing together Pluronic F127 in DI water by weight/volume % (w/v %). For preparation of samples, an analytical balance with a precision of 0.001 g was used to weigh the desired amount of Pluronic F127. Samples with varying concentrations of Pluronic F127 in deionized (DI) water were prepared ranging from 1 - 30 (w/v %). Samples were also prepared with the addition of 1% Vancomycin added to the Pluronic F127/DI mixture. All solutions were stirred and kept in a refrigerator for at least one day to achieve homogeneous mixture.

2.3. Rheology of the Pluronic F127 Hydrogel

Rheological characterization of samples were performed on a Bohlin Gemini 150 HR Nano rheometer. Temperature sweeps were conducted using samples of varying Pluronic F127 concentrations (20%, 22.5%, 25%, 27.5%, 30% w/v%) in DI using the Peltier cylinder system with the temperature being controlled by the rheometer’s cooling system with a water pump. Samples were cooled in a refrigerator and then loaded onto the Peltier cylinder surrounded by the temperature control system. The temperature sweeps were carried out from 17 °C to 60 °C at a constant shear stress of 10 Pa and a strain of 10%. The condition of a constant shear stress of 10 Pa was selected for the temperature sweep after a preliminary test showed a plateau of G’ at 10 Pa both at low and high frequencies indicating that the hydrogel maintained its stable network under these conditions. The viscosity (Pa s) was • plotted against temperature (°C) from the data collected in the temperature sweeps. Samples were prepared for frequency sweeps by

placing 3 mL of the hydrogels into polystyrene (PS) petri dishes and placed into a Fisher Scientific IsotempTM IncubatorTM set at 37 °C for a duration of over 10 minutes to allow the samples to gel. Frequency sweeps were performed using the 2 cm Peltier plate spindle under the oscillatory mode of the rheometer. The rotating spindle allowed measurements of elastic modulus (G’), and viscous modulus (G”) to be collected following a logarithmic sweep of frequencies. Frequency sweeps of each modulus were evaluated between 0.01 to 100 Hz at a constant temperature of 37 °C, under a constant shear stress of 100 Pa with a set strain of 10%. A constant shear stress of 100 Pa was selected to identify the fracture point of the gel. The moduli were plotted against frequency.

2.4. Flow Tests for UV-Vis Spectroscopy

The flow tests were conducted in the Fisher Scientific IsotempTM IncubatorTM set at 37 °C. Prepared samples were loaded into a chamber for hydrogel testing that was created by the Garcia Center for Polymers at Engineered Interfaces at Stony Brook University. The schematic of the chamber can be found below in Figure 2.

Figure 2. Schematics of version 1.1 of the Tubing Apparatus for Hydrogel Testing. All dimensions are in inches.

A NE-1000 Single Syringe Pump (NE, Ringoes, NJ) was used to conduct the flow test to collect samples for UV-Vis

11

spectroscopy. A BD 20 mL syringe with a Lver-LoxTM tip (BD, Franklin Lakes, NJ) was loaded with DI and secured on the syringe pump. The chamber was loaded with 13 mL of hydrogel and secured by nuts and bolts. The hydrogel in the chamber sat in the incubator at 37 °C to gel. The syringe pump was then connected to the loaded closed chamber by amber latex tubing with an ID size of 3/16” and a wall size of 1/16” (VWR Scientific, West Chester, PA). The syringe pump was set to mL/min and flow tests of 0.1, 0.5, and 1.0 mL/min were conducted in order to simulate the flow rate range of spinal fluid (0.28-0.42 mL/min). For each single test, three separate samples were collected after 5 mL was collected so that for one test, a sample of 0-5, 5-10, and 10-15 mL were collected separately for UV-vis spectroscopy. UV-Vis of each 5 mL were taken and compared in order to characterize the time dependence of the release. A picture of the running setup can be found in Figure 3.

2.5 UV-Vis Spectroscopy

The UV-Vis spectroscopy of samples were conducted using a Evolution 220 (Thermo Fisher Scientific, Shanghai, China)

Figure 3. The running setup for the flow test. This setup is conducted in a Fisher Scientific IsotempTM IncubatorTM set at 37 °C.

UV-Vis spectrometer. A blank sample of the DI used in the solutions was used to calibrate the spectrometer. Samples were loaded on optical cuvettes and placed into the spectrometer. The absorbance of the UV-Vis spectrometer was performed between 200-400 nm at room temperature.

3. Results and Discussion

3.1. Gelation Temperature of F127

Rheology measurements were performed for Pluronic F127 aqueous solution for various concentrations: 20, 22.5, 25, 27.5, and 30 w/v %. Figure 4 shows the viscosity as a function of temperature from 18 °C to 60 °C. At low temperature, viscosity slowly increases as the copolymer starts to form micelles.[11, 12] Viscosity increases steeply when sol-gel transition takes place due to associations of the hydrophobic cores.[11, 13, 14] Temperature dependence of gel stability on Pluronic concentration was noticeable. With an increasing concentration of F127, the phase transition temperature decreased. This phenomenon is possibly due to a change in aggregation number with temperature and/or change in the micelle formation process at

Figure 4. Viscosity as a function of temperature at various concentrations of F127 aqueous solution (w/v %)

12

different concentration.[15] From the temperature sweeps, we chose the concentration of 30 w/v % to use for following experiments based on gel stability with the lowest opportunity to be liquefied at 37 °C.

3.2. Frequency Sweep on F127 with and without Vancomycin

Frequency sweeps measurements were performed on Pluronic F127 with and without the additive of 1% Vancomycin, and the effects of Vancomycin on the micellar structure of F127 was investigated. Frequency sweeps were conducted within the frequency range of 0.01 - 100 Hz at constant shear (100 Pa) and temperature (37 °C). Figure 5 shows the comparison between the frequency sweeps on F127 with and without Vancomycin. Both F127 systems, with and without Vancomycin, exhibited similar rheological behaviors in the examined frequency range. Regardless of the presence of Vancomycin, elastic modulus (G’) was greater than viscous modulus (G”) throughout the frequency range. This is associated with the highly entangled F127 gel networks, and reflects the gel behavior of 127.[16, 17] At low frequency ranges (0.01 - 10 Hz), G’ for both samples showed low-frequency plateau, and no significant increase was observed with frequency. For the viscous modulus (G”), decrease of G” is observed in both F127 systems. However, decrease of G” for F127 with Vancomycin was observed at lower frequency, which was at 0.01 Hz. Moreover, at this frequency, G’ of F127 with Vancomycin was twice higher than that of F127 without Vancomycin. These indicate that the presence of Vancomycin stabilized the F127 at lower frequency and F127 with Vancomycin exists as a stable gel. At frequency of 18.42 Hz, distinctive dips of G’ were observed in both systems. The possible explanation is that the frequency of 18.42 Hz was the limit for the material to

maintain its stable elastic domain at 100 Pa in shear. Due to this reason, at frequency higher than 18.42 Hz, the material degraded via breakage of micelle entanglements within shear at 100 Pa. As the micelles were no longer physically bonded at frequency higher than the gel fracture point, different slope of G’ were observed due to different forms of micellar solutions.

Figure 5. G’ (open symbols) and G” (closed symbols) moduli versus frequency for 30 w/v % Pluronic F127 with and without Vancomycin.

The frequency of 18.42 Hz was converted into flow rate, and the theoretical fracture point of F127 in fluid flow was obtained. From the frequency of the rotation in the rheometer, angular velocity of the material was determined as follows:

(1)⍵ = 2 · π · f In which is the angular velocity and ⍵

is the frequency of the rotation. By knowingf the angular velocity of the material, theoretical linear velocity was obtained from Equation 2 below:

(2)v = r · ⍵

where is the linear velocity and r is the v radius of the plate in the rheometer (Figure 6). Equations 1 and 2 were combined and rewritten as Equation 3, which gives the linear velocity in terms of the frequency of the rotation:

13

(3)v = 2 · r · π · f

Figure 6. Schematic representation for the theoretical calculation of the flow rate at the fracture point of F127.

Using the Equation 3, the theoretical linear velocity of the material was calculated for the frequency of 18.42 Hz, which is the fracture point of F127 at 100 Pa in shear. According to the Equation 3, the linear velocity of 0.53 0.11 m/s was obtained that ± corresponds to 18.42 Hz. This velocity is equivalent to the speed at which the outer point on the plate was rotating where the highest amount of mechanical deformation occurs. Assuming that 1 10-6 m2 unit area × from the outer point of the plate is moving at the velocity of 0.53 0.11 m/s (Figure 6), ± approximate flow rate of (0.53 0.11) 10 -6 ± × m3/s (= 31.8 6.6 mL/min) was obtained as a ± corresponding speed for frequency of 18.42 Hz. It is important to remark that the flow rate for the hydrogel fracture point is much greater than that of flow test (0.1, 0.5, 1 mL/min) or spinal fluid flow (0.28-0.42 mL/min).[8] This indicates that Pluronic F127 with and without Vancomycin maintains a stable gel. 3.5. Flow Test of Pluronic F127

UV-Vis for dilutions of 1 - 30 w/v % of Pluronic F127 were taken in order to obtain a baseline for comparison (Fig 8.a).

UV-Vis of Pluronic indicates that a higher and sharper peak corresponds to a higher concentration of Pluronic. Additionally, a broader peak range indicates a less pure sample. Thus, samples with lower concentrations of Pluronic are less pure, this might explain the shift observed in Fig 8.a at concentrations of 15% and less. However, since all concentration samples exhibited a peak around 220 nm, the known peak of Pluronic, we were able to create the calibration curve. Subsequently, UV-Vis of the flow tests at 1 mL/min, 0.5 mL/min and 0.1 mL/min were obtained (Fig 8.c). The time-dependence UV-Vis test showed that for each flow rate, all 5 mL samples exhibited similar curves. Thus we conclude that the release of Pluronic is not time dependent.

A qualitative comparison of the UV-Vis graphs showed that a very minimum amount of Pluronic was released during the flow tests. Therefore, a calibration curve using Beer Lambert Law was created using dilutions of 1 - 15 w/v % Pluronic in order to obtain a more accurate curve fit.

a(λ) A = · b · c (4)

where A is the measured absorbance, ) is (λa a wavelength-dependent absorptivity coefficient, b is the path length and c is the analyte concentration.

Figure 7. Calibration curve of 1, 2.5, 5, 7.5, 10, 15 w/v % dilutions of Pluronic F127 in DI.

14

From Figure 7 we can conclude that Beer’s Law was obeyed in the specified concentration range. The regression equation for the calibration curve was

having a correlation0.087x 0.0582y = + coefficient (R2) value of 0.99, and the standard deviation of the y-intercept was found to be 0.0263. A calibration curve with a

R2 value of 0.99 is considered to be linear, therefore using the curve we could reliably find the concentration of pluronic eluted during the flow test experiment. From the UV-Vis of Pluronic F127 dilutions we found that the max absorption peak occurred at 220 nm. Therefore, the absorbance at 220 nm was plotted against each flow rate using the regression equation.

Figure 8. (a) UV-Vis spectrum of 1-30 (w/v%) Pluronic in DI water at 37°C (b) UV-Vis spectrum of 0.001 – 5 (v/v %) Vancomycin in DI water at 37°C (c) UV-vis spectrum of 30% Pluronic F127 in DI water at 37 °C with aliquots of 5%, 2.5%, and 1% Pluronic F127 in DI at 37 °C for reference (d) UV-Vis spectrum of 30% F127 + 1% Vancomycin in DI with flow of 1, 0.5, 0.1 mL/min at 37°C, and UV-Vis spectrum of aliquots of 0.01 %, 0.05%, 0.025% Vancomycin for reference.

15

Figure 9. Concentration of 30 w/v % Pluronic F127 released as a function of flow rate at 0.1, 0.5 and 1 ml/min.

When we quantitatively analyzed the data, the calculated concentration of Pluronic released during the flow test was negative due to the fact that the concentrations of Pluronic were very low so there were many impurities that could have interfered with the UV-Vis spectra. Qualitatively, however, we observed that a minimal amount of Pluronic with concentrations far less than 1%, were released under flow. The readable data point at 0.1 mL/min showed that 0.62% Pluronic had eluted. This is because the micelle structure of Pluronic has ample time to disentangle at lower flow rates which allows the solvent of DI Water to degrade the Pluronic gel. Since there was no significant release of Pluronic, we conclude that our hydrogel is stable under flow and a good carrier for Vancomycin at the investigated flow rate. 3.6 Flow Test of Pluronic F127 and Vancomycin

UV-Vis spectroscopy of 0.001 - 5 v/v % Vancomycin were taken as a baseline (Fig 8.b). The same method used to create the calibration curve for pure pluronic was used to create the calibration curve for pure vancomycin. UV-Vis spectroscopy of the

flow test (taken at 0.1 mL/min, 0.5 mL/min and 1 mL/min) for 30 w/v % Pluronic F127 with 1% Vancomycin in DI water were also taken (Fig 8.d). The time-dependence UV-Vis also showed that for each flow rate, all 5 mL sample exhibited a similar curve. Therefore, we conclude that the release of vancomycin is not time dependent. A qualitative comparison of the UV-Vis of both the aliquots and Vancomycin flow tests also revealed minimal amounts of Vancomycin elution. Therefore a calibration curve was calculated using 0.01 - 0.1 v/v % Vancomycin for a better curve fit.

Figure 10. Calibration curve of 0.01, 0.025, 0.05, 0.1 v/v % dilutions of Vancomycin in DI.

Figure 10 obeys Beer - Lambert’s Law for the specified range. The regression equation for the calibration was found to be

having a correlation8.4253x .0402y = 1 − 0 coefficient (R2) of 0.94, and the standard deviation of the y-intercept is 0.102. An R2 value of 0.94 can be considered linear, therefore the calibration curve was used to plot concentration of Vancomycin against the flow rate. Concentration of Vancomycin released under flow was calculated by finding the absorbance at the known absorbance peak of Vancomycin, 280 nm[18], for each flow test and the concentrations were calculated using the regression equation.

For the flow rate of 1 mL/min we calculated a negative concentration, which is

16

theoretically impossible. We attributed this result to the extremely low concentration of Vancomycin that was released at this flow rate. Therefore the concentration was too minimal for UV-Vis analysis. However, since we obtained good results for the flow rates at 0.1 ml/min and 0.5 mL/min we decided to move forward with the quantitative analysis. The slope of concentration vs. flow rate for Vancomycin was calculated to be -0.0213 ± 0.0029 with a correlation coefficient of 0.98.

Figure 11. Concentration of 1 % Vancomycin in 30% Pluronic F127 released as a function of flow rate at 0.1, 0.5 and 1 mL/min.

Figure 11 shows small amounts, less than 0.02% concentration of Vancomycin eluting into DI water. Since Vancomycin is hydrophilic, it should have an affinity to release into DI water. However, because there is such a small release we can assume that the Pluronic micelle structure is highly entangled such that Vancomycin is trapped within the micelle branches. As a result, we can confirm that Vancomycin interacts with the hydrophilic corona of the Pluronic micelle structure. Furthermore, we can conclude that there is a decrease in the amount of Vancomycin released from Pluronic solution with increasing flow rate. This phenomenon is likely because at higher flow rates, the flow is such that there is not enough time for the micelle structure to disentangle and thus less

Vancomycin is released. In contrast, at slower flow rates the micelles have enough time to disentangle therefore more Vancomycin can be released from the solution. Qualitatively comparing the rate at which release rate is affected by flow rate for both Vancomycin and Pluronic, we observed similar behaviors. For both Pluronic and Vancomycin, there was a decrease in the amount of material released as flow rate was increased. Additionally, there was a very low release of both Pluronic and Vancomycin at 1 mL/min, indicating that both Pluronic and Vancomycin are resistant to fluid flow and remain stable at this flow rate. Thus, the Pluronic gel with Vancomycin will be retained in the spinal column as the flow rate of spinal fluid is 0.28 - 0.42 mL/min.

4. Conclusion

Pluronic F127 hydrogels possess the ability to serve as a carrier for the drug delivery of Vancomycin to the spinal column area. This is due to Pluronic F127’s biocompatibility, FDA approval, and the ability to manipulate the properties so that the release and stability of the hydrogel matrix will be favorable in the spinal column area. The rheological properties of Pluronic F127 were investigated, and flow tests were conducted to study the release of Pluronic and Vancomycin under fluid flow through UV-Vis spectroscopy. From temperature sweeps, the gelation temperature dependence on the hydrogel concentration was observed. As the concentration of Pluronic increased, the temperature of gelation decreased. The temperature of gelation for 30% w/v Pluronic F127 in DI was at 23 °C, while 20% w/v Pluronic F127 in DI was at 34 °C. These values are both below the in vivo temperature of the human body which means that a range of 20% to 30% w/v Pluronic can be injected into a person as a solution at room temperature, and then gels at body temperature. Frequency sweeps were

17

conducted which showed a thixotropic behavior of the hydrogel as G’ and G’’ diverged under a constant high shear stress of 100 Pa. Under the influence of a constant shear stress of 100 Pa, an interesting phenomena was observed after 10 Hz which can be attributed to a restructuring of the crystalline structure of the hydrogel matrix. The addition of 1% Vancomycin to the 30% concentration of Pluronic in DI did not alter the rheological properties significantly. This is important because hydrogels are sensitive materials that are affected by both their components, and the environments that the hydrogels experiences. If a significant change existed by the addition of the Vancomycin to the hydrogel matrix, then a further study would need to be conducted to understand the properties of the hydrogel created, and how those properties can be exploited for the controlled release. The flow tests showed that the slower the flow, the higher the release of the hydrogel components, which can be attributed to the duration for the test to occur, and the swelling of the components out of the hydrogel matrix.

These results show the efficacy of a Pluronic F127 hydrogel as carrier to deliver Vancomycin. The Pluronic F127 hydrogel maintains its stable network under the flow conditions of the spinal column area, and elutes the antibiotic Vancomycin. Further studies need to be conducted to establish further parameters for release that mimic in vivo conditions such as drug delivery rate of the body, pH, and the effects of enzymatic activity.

5. Acknowledgements

We gratefully acknowledge the financial support of the Department of Materials Science & Engineering and the Program in Chemical and Molecular Engineering at Stony Brook University. We also thank the Garcia Center for Polymers at

Engineered Interfaces at Stony Brook University for providing the hydrogel flow test chamber.

6. References

[1] Tatini D, Tempesti P, Ridi F, Fratini E, Bonini M, Baglioni P. Pluronic/gelatin composites for controlled release of actives. Colloids and Surfaces B: Biointerfaces 2015: 135: 400-407.

[2] Singla P, Chabba S, Mahajan R-K. A systematic physicochemical investigation on solubilization and in vitro release of poorly water soluble oxcarbazepine drug in pluronic micelles. In Colloids and Surfaces A: Physicochemical and Engineering Aspects 2016: 504: 479-488.

[3] Unosson J, Montufar EB, Engqvist H, Ginebra M-P, Persson C. Brushite foams-the effect of Tween® 80 and Pluronic® F-127 on foam porosity and mechanical properties. Journal of Biomedical Material Research Part B 2016: 104B: 67-77.

[4] Barba A, d’Amore M, Grassi M, Chirico S, Lamberti G, Titomanlio G. Investigation of Pluronic® F127-Water Solutions Phase Transitions by DSC and Dielectric Spectroscopy. Journal of Applied Polymer Science 2009: 114: 688-695.

[5] Giovanni F, Giovanna M, Marco C, Giulia B, Gianfabio G. Thermosensitive Self-Assembling Block Copolymers as Drug Delivery Systems. Polymers 2011: 3: 779-811.

[6] Kell M, Walther B, Søren H. Effects of PEO−PPO Diblock Impurities on the Cubic Structure of Aqueous PEO−PPO−PEO Pluronics Micelles:  fcc and bcc Ordered Structures in F127. Macromolecules 2008: 41(5): 1720-1727.

[7] Oh SH, Kim JK, Song KS, Noh SM, Ghil SH, Yuk, SH, Lee JH. Prevention of postsurgical tissue adhesion by

18

anti-inflammatory drug-loaded Pluronic mixtures with sol-gel transition behavior. Journal of Biomedical Materials Research Part A 2005: 72(3): 306-316.

[8] Sakka L, Coll G, Chazal J. Anatomy and physiology of cerebrospinal fluid. European Annals of Otorhinolaryngology, Head and Neck Diseases 2011: 128(6): 309-316.

[9] Simoes S, Veiga F, Torres-Labandeira J, Ribeiro A, Sandez-Macho M, Concheiro A, Alvarez-Lorenzo C. Syringeable Pluronic-𝛼-cyclodextrin supramolecular gels for sustained delivery of vancomycin. European Journal of Pharmaceutics and Biopharmaceutics 2012: 80(1): 103-112.

[10] Wang Q, Shi Z, Wang J, Shi G, Wang S, Zhou J. Postoperatively administered vancomycin reaches therapeutic concentration in the cerebral spinal fluid of neurosurgical patients. Surgical Neurology 2008: 69(2): 126-129.

[11] Pham T, Liem C, Djabourov M, Ponton A. Mechanisms of micellization and rheology of PEO-PPO-PEO triblock copolymers with various architectures. Journal of Colloid And Interface Science 2008: 382(2): 278-287.

[12] Cohn D, Lando G, Sosnik A, Garty S, Levi A. PEO-PPO-PEO-based poly(ether ester urethane)s as degradable reverse thermo-responsive multiblock copolymers. In Biomaterials 2006: 27(9): 1718-1727.

[13] Jiang J, Li C, Lombardi J, Colby R, Rigas B, Rafailovich M, Sokolov J. In Polymer 2008: 49(16): 3561-3567.

[14] Escobar-Chavez J, López-Cervantes M, Nail A, Kalia Y, Quintanar-Guerrero D, Ganem-Quintanar A. Journal of Pharmacy & Pharmaceutical Sciences 2006: 9(3): 339-358.

[15] Bohorquez M, Koch C, Trygstad T, Pandit N. A Study of the

Temperature-Dependent Micellization of Pluronic F127. Journal Of Colloid And Interface Science 1999: 216(1): 34-40.

[16] Fernández V, Tepale N, Alvarez J, Perez-Lopez J, Macías E, Bautista F, Pignon F, Rharbi Y, Gamez-Corrales R, Manero O, Puig J, Soltero J. Rheology of the Pluronic P103/water system in a semidilute regime: Evidence of nonequilibrium critical behavior. Journal of Colloid And Interface Science 2009: 336(2): 842-849.

[17] Grassi G, Crevatin A, Farra R, Guarnieri G, Pascotto A, Rehimers B, Lapasin R, Grassi M. Journal of Colloid and Interface Science 2006: 301(1): 282-290.

[18] Tariq M, Naureen H, Abbas N, Akhlaq M. Development and Validation of a Simple, Accurate and Economical Method for the Analysis of Vancomycin in Human Serum Using Ultracentrifuge Protein Precipitation and UV Spectrophotometer. Journal of Analytical and Bioanalytical Techniques 2015: 6(2): 239.

19

20

Effect of Surface Properties on Pluronic Deposition Vivian Cheng1, Christopher Lafergola1, Melanie Noye1, Clement Marmorat1,

Miriam Rafailovich1 1Chemical and Molecular Engineering Department, Stony Brook, NY, 11794, USA

Abstract This paper investigates ways in which Pluronic F-127, a biocompatible triblock copolymer,

interacts with substrates based on their surface properties including surface tension, which can be

calculated from the resulting contact angle when F-127 is deposited on a given substrate. This

testing will aim to provide insight on the physics involved when F-127 dries on a given substrate,

since this is commonly seen in topical medications, drug delivery systems, and skin products. By

creating F-127 solutions of different weight percentages, and applying them to spin casted

polystyrene-silicon wafer substrates, the resulting contact angle can be measured in order to

determine surface tension. F-127 solutions were made in 1 weight percent, 5 weight percent, and

10 weight percent batches, and a 15 mg/mL solution of polystyrene was spin casted onto cut

silicon wafer squares. Each F-127 solution was dried onto this substrate, as well as DI water as a

control, and the contact angle was measured as each droplet dried. This is important because one

can determine how F-127 will deposit on a given surface depending on different surface

tensions, as well as with different concentrations of F-127 in solution. Optical microscopy and

atomic force microscopy (AFM) will also be utilized in order to obtain images of the F-127 that

is deposited on the substrate, so the pattern of the deposition can be observed. The coffee ring

effect is observed in the images because as a droplet dries, the height decreases as a function of

time, with the radius remaining the same, causing deposition to occur on the outer rim of the

drying droplet. We hypothesize that we can control the evaporation rate of water by using

varying concentrations of F-127, and that surface tension will affect how F-127 deposits on a

given surface. This research will ultimately serve the purpose of providing an in-depth analysis

of how F-127 deposits on given substrates, so that observed trends can then be applied to how

topical medications, cosmetics, and drug delivery systems will work when being deposited on

human skin.

Keywords

Pluronic, F-127, Droplet, Evaporation, Surface Pattern, Coffee Ring Effect.

1. Introduction Pluronic F-127 is a nonionic triblock

copolymer composed of a central

hydrophobic block of polypropylene oxide

surrounded by two hydrophilic blocks of

polyethylene oxide.

Figure 1. Chemical Structure of F-127 [1].

F-127 can be used as an antifoaming agent,

wetting agent, dispersant, thickener, or

emulsifier. By understanding how the

conditions of a given substrate affect F-127

deposition, it is easier for dermatologists as

well as the cosmetic industry to better

understand how people’s skin will be

affected. Currently, there are many different

products that use F-127, namely in drug

delivery, but in order to understand how

well it will be received by a given

individual, the physics involved with F-127

deposition needs to be researched. F-127

21

solutions were created for varying weight

percentages and spun cast onto PS-silicon

wafer substrates. Then contact angle

measurements were taken for the different

solutions. DI water was used as a control, so

that one could determine how F-127 will

deposit on the surface due to varying surface

tensions. Atomic force microscopy and

optical microscopy was conducted in order

to observe surface patterns of the F-127 that

was deposited on substrate. Temperature

was kept constant as well since this can also

affect the drying process, and adding this

variable into the experiment would only

serve to complicate results. This research

will help to explain how F-127 deposits on

given substrates, so that observed trends can

then be applied to a variety of application in

both the cosmetic and pharmaceutical

industry.

A common example of an

evaporating droplet is a coffee droplet on a

table. After evaporation, the droplet leaves a

ring-shaped stain often referred to as the

‘coffee ring effect’. Droplets placed on solid

surfaces can adopt different shapes ranging

from spherical to flat shaped based on

surface tension, which is excess surface

energy per unit area of liquid surfaces that

are in contact with another material. The

contact angle of the droplet on a solid can be

found by the equilibrium forces due to the

surface tensions at the liquid-gas, solid-gas

and solid-liquid interface.

Figure 2. Droplet Evaporating [2].

In figure 2, an evaporating droplet

which has a contact line pinned on the

surface is shown. The evaporation flux that

diverges at the contact line is represented by

the arrows above the droplet. It is important

to note that while the radius of the droplet

remains constant, the height, h(t), slowly

decrease as the water evaporate over time.

The arrows inside the droplet represent the

flow of the liquid in order to compensate for

the liquid that is evaporating from the edge.

The flow then brings dispersed particle to

the outer rim of the droplet until the droplet

is fully evaporated, leaving a ring shape

around the outer line of the droplet.

Interestingly, when the contact line is pinned

the evaporating is faster and when the

contact angle approaches zero it creates a

rush of particles that accumulates around the

outer rim which can help explain the coffee

ring effect. The top of the droplet is the

coldest due to longer conduction paths from

the substrate, so evaporation occurs faster

along the contact line [2]

Figure 3. Electron Micrograph of a sample of

10% F-127 solution [3].

Individual micelles can be visualized

on the micrograph of the F-127 sample in

figure 3, which shows that it is spherical in

shape and demonstrates that a core-shell

structure is present. This correlates to F-127

consisting of a hydrophobic poly (propylene

oxide) core and a shell comprising of

poly(ethylene oxide) and water. The core is

lighter due to the lower electron density

compared to the shell.

22

A previous study by Yeganehdoust

et al. looked at the kinetic energy and

average pressure of a droplet during the

evolution of the droplet. The study found

that the curvature can be calculated more

accurately when there are a larger number of

particles, which results in a higher surface

tension in that region. The higher surface

tension also correlates to a higher initial

kinetic energy and larger average pressures

[4].

Figure 4. Cross section and 3D projection of

single droplets [5].

The shape of the ink droplets at

different temperatures were observed in a

previous study by Soltman et al and can be

visualized in figure 4. The flux of a fluid to

the edge of the drop can lead to buildup of

solute as the drop evaporates. The geometric

nature of pinning and the curvature at the

drop’s edge, leads to increased evaporation

which can enhance the coffee ring effect.

The study explains how heat is easily

transferred from the substrate to the edge of

the drop which results in improved

evaporation near the edge of the droplet

compared to the center of the drop. In

addition, the study found that increasing the

temperature of the substrate also increases

the amount of solute transferred near the

contact line, while decreasing the substrate

temperature decreases rim evaporation. [5]

This removes the coffee ring formation since

the substrate slows edge evaporation more

than in the center.

Developing a better understanding of

how droplets dry and the coffee ring effect

can help us also better understand how F-

127 will deposit on different substrates as a

result of surface tension. We are specifically

interested in how F-127 will deposit since F-

127 can be used by many different

industries.

2. Materials and Methods

2.1 Creating Solutions for Pluronic Droplets

The solutions for our droplets were

created using Pluronic F-127 purchased

from Sigma-Aldrich and DI water. We

measured 1, 5, and 10 weight percent’s of F-

127 in relation to DI water and stored in test

tubes. A separate test tube was also prepared

for 100% DI water for the testing of a

control droplet.

2.2 Spin Caster

The Spin Caster was used to deposit

an even film over a silicon wafer purchased

from Sigma- Aldrich. The silicon wafers

were cut into 1x1 centimeter squares using

an X-Acto knife. Polystyrene (PS) was

purchased from Sigma-Aldrich. PS solutions

were dropped onto the surface of a wafer

immediately before spin casting. Tweezers

were used to handle wafers because it is

important to avoid creating defects on the

wafer. In total, thirty PS wafers were spin

casted.

2.3 Contact Angle Goniometer

The contact angle goniometer was

used to measure the contact angle and

volume of the droplets of F-127 on PS

wafers. A total of fifteen frames were taken

with 60 seconds in between frames, in order

to track and observe the evaporating droplet.

After the completion of all frames for the

droplet, each frame was individually

23

analyzed by using its contact angle and

volume in relation to the time the frame was

taken. The contact angle goniometer

acquired the core of our data by measuring

the contact angle of a droplet in relation to

decreasing time.

2.4 Atomic Force Microscopy

The surface patterns of each wafer

will be observed using AFM. AFM will

detect the patterns of the particles deposited

from F-127 solution onto the PS wafer.

Using data from AFM, we will be able to

compare the coffee ring effect amongst

different concentrations of F-127. The

coffee ring effect is an indication of particle

movement and deposition, thus advancing

our research on the varying effects of

surface tension on F-127.

2.5 Optical Microscopy

The surface patterns of each wafer

were also observed using optical

microscopy. Images will be taken using

different lenses. The microscopy images

allow for visualization of a dried droplet on

different surfaces. The surface patterns

detected using optical microscopy can help

to explain the coffee ring effect.

3. Results and Discussion

3.1 Contact Angle Goniometer

We started our data collection using

the PS wafers. We conducted trials to

calculate contact angles for 1 wt% F-127, 5

wt% F-127, 10 wt% F-127 and DI water.

For each concentration, we used droplet

sizes of 5 µL and 10 µL.

Figure 5 a. Volume vs. Time for 5 µL samples. The rate of evaporation can be calculated by finding the

slope of each sample.

Figure 5 b. Volume vs. Time for 10 µL samples. From this graph it is clear that the rate of evaporation is

greatest in the 1 wt% F-127, and slowest in 10 wt% F-127.

24

Table 1. The rate of evaporation is compared

between samples. For 5 µL samples, 5% wt

concentration evaporated the fastest and 10% wt

concentration evaporated the slowest. For 10 µL

samples, 1 wt% concentration evaporated fastest

and 10 wt% evaporated the slowest.

Table 1. indicates that for both

droplet sizes of 5 µL and 10 µL, the droplet

containing the highest concentration by

weight of F-127 took the longest to

evaporate. Compared to our control sample

of DI water, droplets with F-127 showed a

trend of evaporating slower. Therefore, a

higher concentration of F-127 hinders the

evaporation of water, thereby indicating that

F-127 delays the reduction of the height of

the droplet.

The 5 µL, 5% wt% F-127 data

deviated from the norm in regards to

evaporation rate due to some possible errors.

While gathering contact angle data, the

reflection from the light onto the droplet

could have skewed the contact angle

goniometer’s ability to calculate the volume

of the droplet. In addition, all 5 µL of the

solution may not have ejected from the pipet

when the droplet was released, thereby

altering from the desired results. The white

overcast in conjunction with an error with

the pipet could have caused the anomaly of

the 5 µL, 5 wt% data.

Time

(s)

Average of Right and Left

Contact Angles (°)

DI

Water

1%

Wt

5% Wt 10%

Wt

0 91.7

+/- 7.4

79.5

+/-

0.5

61.2

+/- 1.4

57.0

+/-

1.0

60 89.2

+/- 5.9

57.9

+/-

1.1

49.8

+/- 1.8

48.6

+/- 0.9

120 86.6

+/- 5.7

59.3

+/-

4.6

46.9

+/- 0.5

45.1

+/- 0.7

180 78.9

+/- 8.1

41.4

+/-

2.1

42.797

+/- 1.1

43.26

+/- 0.8

Table 2 a. Contact Angles for 5 µL samples for

the first 3 minutes. The contact angle was found

using the contact angle goniometer, and by

averaging the right and the left contact angles of

each individual droplet.

The errors found in tables 2 a. and b.

was the standard deviation of a single

sample’s left and right contact angle. The

right and left contact angles should similar,

if not exact since the liquid from the droplet

disperses outward from the center

symmetrically. The reflection from the light

blurred the edges of our droplet in certain

Concentration of F-

127

Rate of

Evaporation

( µL/s)

5 µL

DI Water 0.094

1% wt 0.062

5% wt 0.16

10%wt 0.048

10 µL

DI Water 0.12

1% wt 0.10

5% wt 0.048

10% wt 0.044

25

Time

(s)

Average of Right and Left Contact

Angles (°)

DI

Water

1%

Wt

5%

Wt

10% Wt

0 68.4

+/- 0.9

68.1

+/-

1.3

55.2

+/-

1.5

68.2 +/-

0.3

60 58.7

+/-

16.4

57.6

+/-

1.6

52.4

+/-

1.5

58.2 +/-

0.7

120 54.1

+/- 1.5

53.0

+/-

1.3

49.2

+/-

1.7

53.525

+/- 0.8

180 53.1

+/- 0.1

52.4

+/-

1.0

47.0

+/-

1.4

52.73

+/- 0.5

Table 2 b. Contact Angles for 10 µL samples

for the first 3 minutes.

frames, making it difficult for the

goniometer to exact the contact angles. A

discrepancy between the right and left

contact angle measurements could also have

resulted from existing scratches and

particles on the substrate. The standard

deviation was found by averaging the right

and left angles, subtracting the average from

each number, squaring the results,

calculating the mean of those squared

differences, and lastly square rooting the

mean.

A high contact angle indicates that

the liquid is hydrophobic and displays a low

wettability. A lower contact angle has higher

wetting which means the liquid spreads over

the surface compared to a higher contact

angle. The surface energy is the amount of

intermolecular force created at the surface of

a substrate. [6] For a low wetting substrate,

the surface tension of the liquid is stronger

than the surface energy. Thus, the higher the

contact angle, the lower the surface tension.

Figure 5 c. The volume of four samples of a 5

µL droplet of 5 wt% F-127 compared to its

height.

Figure 5 d. The volume of four samples of a 10

µL droplet of 5 wt% F-127 compared to its

height.

In Figure 5 c. for the 5 µL sample of

5 wt% F-127, it is apparent that the height

remains relatively constant as the volume

decreases. The drying of a droplet begins

when its height begins to decrease and its

liquid begins to flow out in opposite

directions. Since the height remains drops

slower than the control, the droplet can be

categorized as frustrated because the surface

tensions of the substrate hold on to the liquid

at the edges of the droplet. The other

samples of a 10 µL droplet and 5 wt% F-127

shown in figure 5 d. showcase normal

drying conditions.

26

Our contact angle results show that

the droplets with F-127 concentrations have

a smaller contact angle compared to DI

water. Therefore, according to our results,

droplets with F-127 have a higher surface

tension with the polystyrene substrate than

DI water alone does with the same substrate.

3.2 Atomic Force Microscopy (AFM) and

Optical Microscopy

Atomic Force Microscopy was used

to observe how the F-127 deposited on the

PS substrate for each concentration. This

reveals a lot about how the F-127 deposits

on a nanoscale. Optical microscopy is also

an important tool since surface patterns can

be observed, which provides insight as to

how different concentrations of F-127 will

deposit. From the images we obtained it was

clear that concentrations of F-127 that are 5

wt% or higher do not produce clear ridges,

rather, the surface is much more bulky and

rough. This is most likely due to the fact that

concentration was simply too high, hence

why the focus was on concentrations of 1%

and 0.1%, since these concentrations

produced images that were much more

telling about how F-127 will deposit on a

given surface.

Figure 6 a. 1 wt% microscopy image at 10X.

Figures 6 a,b are images found using

1WT% 5 µL F127 on PS substrate. The

Figure 6 b. 1 wt% AFM image, stripes region.

stripes in figure 6 a show that there is some

indication of a surface pattern from F-127

deposition, but there is too much F-127 to be

able to analyze the surface structure in more

detail. The AFM image in figure 6 b shows

one of the stripes in detail, but it is hard to

make out any details because of this bulk

amount of F-127.

Figure 7 a. 5 wt% microscopy image at 10X

Figure 7 b. 5 wt% AFM image, center region.

27

Figures 7 a,b correspond to images of the

5WT% 5 µL F127 on PS substrate. These

images were even more difficult to interpret

due to the higher concentration of F-127.

For this reason, we chose to create samples

for 0.1 wt%. Since this concentration

produced samples that were much easier to

analyze, since they had clearer surface

patterns.

Figure 8 a. 0.1 wt% microscopy image at 5X.

Figure 8 b. 0.1 wt% microscopy image at 50X.

Figures 8 a,b show optical

microscopy images for a 5 uL drop of 0.1

wt% F-127 on a PS substrate. From these

images, it is clear that the lower

concentration of F-127 results in a smoother

surface with more distinct surface patterns.

Interestingly, defects present on the surface

cause the F-127 to wrap around the defects

as it deposits, as a result of increased surface

tension in the area of the defect. This sheds

light on the how surface defects will affect

F-127 deposition which is important in

medical applications that utilize F-127 in

topical medications, or in the cosmetic

industry that use F-127 in topical cosmetic

products. A perfectly smooth silicon wafer

substrate is great in a lab setting, however,

in real-life applications the surface will

rarely be smooth, especially when

considering how unique each individual’s

skin is.

Figure 9 a. 0.1 wt% microscopy image at 5X.

Figure 9 b. 0.1 wt% microscopy image at 50X

(left) and an AFM image of the area (right).

Figures 9 a.b show microscopy and

AFM images of a 5 µL drop of 0.1 wt% F-

127 on a PS substrate. The dark spots seen

on the surface are most likely the substrate

showing through a very thin layer of F-127

deposited in the area. This is most likely due

to how F-127 wets a surface selectively,

since it will not wet a surface uniformly.

When water wets a surface, there is a

uniform coating that occurs, however, with

F-127 it will wet selectively because of the

nature of its amphiphilic structure. This

duality results in this unique surface pattern

when F-127 wets a surface.

4. Conclusions

28

In conclusion, the surface properties

of Pluronic F-127 were investigated in order

to better understand how it deposits on a

given substrate. The contact angles were

determined for different solutions of varying

F-127 concentration in order to observe the

effect of F-127 on the evaporation rate of the

droplet. A larger contact angle represents a

more hydrophobic substance and

demonstrates low wettability. The results

indicate that droplets of the F-127 have a

higher surface tension with respect to the

polystyrene substrate compared to the

control, which is DI water. The surface

properties of F-127 were observed using

AFM and optical microscopy at varying F-

127 concentrations, which showed many

stripes originating from the center of the

droplet for the 1WT% 5 µL F-127 on PS

substrate indicating a change in structure to

a hexagonal shape. The coffee ring effect

can be observed in the microscopy images

since the height of the droplet decreases

with respect to time while the radius remains

constant, which causes the solution to

deposit on the outer rim of droplet. From

figure 9 b, we also learn some interesting

things about how F-127 will wet a surface

since it is not uniformly distributed like

water is. Rather, because of F-127’s

amphiphilic nature small gaps form where

F-127 does not deposit as heavily, most

likely due to its selectivity when it comes to

the surface it is depositing on.

It is difficult to observe these trends

in concentrations of F-127 higher than 1

wt%, which is made clear by figure 7 b,

which shows bulk amounts of F-127,

without any clear surface patterns.

Interestingly, the 0.1 wt% AFM images

provide insight on how surface defects affect

the deposition of F-127. The F-127

deposited in a ring around the defects, in

figure 8 b, shows that increased surface

tension from the defect results in F-127

deposition that is not uniform. Thus, areas of

greater surface tension will have a greater

buildup of F-127 compared to the surface of

the rest of the sample which is shown to be

very smooth and uniform. We had

hypothesized that surface tension would

affect F-127 deposition, and this has been

proven from our research. We also have

proved that we can control the evaporation

rate of water by increasing the concentration

of F-127, since increased concentrations of

F-127 results in a slower evaporation rate of

water. When it comes to applications to the

cosmetic and medical industries, this plays a

vital role in how topical products should be

designed. Everyone’s skin is unique with a

variable number of defects, which is why

this research can lead to further study of

how products can be designed to take

advantage of how surface tension affects

Pluronic F-127 deposition.

Acknowledgements

We are grateful for the financial

support from the Department of Materials

Science & Engineering and the Program in

Chemical and Molecular Engineering at

Stony Brook University through research

funding.

References

[1] "Applications of Thermo-Reversible

Pluronic F-127 Gels in Pharmaceutical

Formulations." J Pharmaceut Science.

Canadian, 27 Nov. 2006. Web. 09 Apr.

2017.

[2] Mampallil, Dileep. "Some Physics Inside

Drying Droplets." Resonance: Journal Of

Science Education 19.2 (2014): 123-134.

Education Source. Web. 12 Oct. 2016.

[3] Lam, Yeng-Ming, Nikolaus Grigorie,

and Gerhard Goldbeck-Wooda. "Direct

Visualisation of Micelles of Pluronic Block

Copolymers in Aqueous Solution by Cryo-

TEM." The Royal Society of Chemistry, 28

May 1999. Web. 22 Feb. 2017

29

[4] F. Yeganehdoust, M. Yaghoubi. H.

Emdad, M. Ordoubadi. “Numerical Study of

Multiphase Droplet Dynamics and Contact

Angles By Smoothed Particle

Hydrodynamics.” Journal of Applied

Mathematical Modelling Vol. 40 (2016):

8493-8512. Academic Search Complete.

Web. 26 Oct. 2016.

[5] Soltman, Dan, Subramanian, Vivek.

"Inkjet-printed Line Morphologies and

Temperature Control of the Coffee Ring

Effect." Engineering Village. Elsevier, 4

Mar. 2008. Web. 16 Nov. 2016.

[6] Yuan, Yuehua, and Randall Lee.

"Contact Angle and Wetting Properties."

Springer-Verlag Berlin Heidelberg, 2013.

Web. 7 Dec. 2016.

30

Optimization of Synthesis Gas to Methanol Conversion

Veronica Burnett1, Shweta Iyer1, Steven Krim1, Mike McCutcheon1, Gurkirat

Singh1

1Chemical and Molecular Engineering Program, Materials Science and Engineering Department,

Stony Brook, NY, 11794, USA,

Abstract

As global markets shift from petroleum usage to other fuel sources, methanol presents itself

as a viable alternative. Methanol is a cleaner burning fuel than traditional hydrocarbons because it

produces no particulates and significantly less carbon dioxide. A common way to make methanol

is by the conversion of synthesis gas over a catalyst. Synthesis gas is a mixture of carbon monoxide

and hydrogen commonly derived from anaerobic digestion of biogas from biomass or from dry-

reforming of methane. This research utilizes the cleaner energy source of syngas to produce

methanol in an optimized conversion process by varying parameters of reactor temperature

conditions and catalyst particle size. Previous research has focused on surface area effects of the

catalysts but fails to thoroughly study reactor conditions in conjunction with the catalyst. In this

experimental design, a pressurized batch reactor was used to flow the syngas mixture over various

copper catalysts supported on zinc oxide and gamma alumina at different operating temperatures.

Two types of catalysts were synthesized by sonication: 1) a nanocatalyst and 2) a mixed oxide,

and compared these against a commercial catalyst. These catalysts were characterized by X-ray

diffraction and their particle size and surface morphology were imaged and measured by

transmission electron microscopy and scanning electron microscopy respectively. The

experimental runs in the batch reactor operated at three different temperatures of 220˚C, 230˚C,

and 240˚C for each catalyst totaling in nine experimental batch runs. Liquid samples were taken

after each run and analyzed by gas chromatography. Based on these analyses, we calculated for

each run the space time yield, the activation energy, and the percent conversion. Our results

showed that the mixed oxide catalyst achieved the highest overall percent conversion of 36.3% at

a reaction temperature of 240˚C and required the lowest activation energy of 138.2 J/mol. The

nanocatalyst and commercial catalyst achieved the next highest conversions of 22.6% and 18.9%

respectively when reacting at 220˚C but had higher respective activation energies of 197.1 J/mol

and 158.0 J/mol. Overall, the mixed oxide catalyst exhibited a trend of higher percent conversion

at higher reacting temperatures, while the nanocatalyst and commercial catalyst exhibited converse

trends of higher percent conversions at lower reacting temperatures.

Keywords: Catalysis, Methanol Production, Metal Oxides

31

1. Introduction

With the rapidly declining reserves of

remaining petroleum oil, there is a high

demand for alternative fuel sources to satisfy

increasing needs for the world’s expanding

population. China and India have recently

decided to shift to “Methanol Economy” and

this is helping renewed interest in methanol

research to help switch the fuel source to

methanol [1] [2].

One of the reasons these countries

and many researchers are studying methanol

as a replacement for traditional fuels is

because methanol is a more environmentally

friendly fuel.

Eq.1 2CH3OH + 3O2 → 2CO2 + 4H2O

In the combustion reaction 1, for

every methanol molecule burned one CO2

molecule is formed [3]. In comparison,

octane is a popular fuel and when burned, as

seen in Equation 2 below, 8 CO2 molecules

are produced when it is fully combusted.

Eq.2 2C8H18 + 25O2 → 16CO2 + 18H2O

Methanol creates less greenhouse

gases when burned, and since there are no

carbon to carbon bonds, there is no

possibility for particulates to be formed [3].

Additionally, the equipment or vehicles that

run on traditional fuels can be easily

converted to run on methanol [4]. For these

reasons, methanol is a great alternative to

traditional fuels.

Unlike fuels such as octane, methanol

is a light compound that can be easily

produced from a mixture of CO and H2, also

known as synthesis gas, instead of being

produced by the petroleum refining process.

Producing synthesis gas is a more

environmentally friendly process than

producing most of the common fuel types.

Traditionally, synthesis gas is synthesized by

dry reforming of methane. One group of

researchers studied the formation of synthesis

gas from the anaerobic digestion of biomass

as an alternative process [5].

The process of forming methanol

from synthesis gas has not been drastically

altered since the 1960’s. The commercial

catalyst in use now is copper on a zinc oxide

support (Cu/ZnO) that operates under

reaction temperatures ranging from 240˚C to

280˚C [6]. There is a search for a better

catalyst for a cheaper operation of the

conversion of synthesis gas to methanol.

Smaller catalyst particle sizes tend to

have the more surface area per unit weight,

which is directly related to an increase in

active sites. There is much research to be

done for the synthesis gas to methanol

conversion process using nanocatalysts at

different operating temperatures. For this

process many different catalysts have been

studied for the effect of their size on activity

or the effect of changing the temperature but

these parameters have not been studied

simultaneously for a single catalyst. We

propose utilizing the most common catalyst,

Cu/ZnO, for this reaction and altering both of

these parameters for true optimization of the

process.

As mentioned before, the process of

methanol synthesis from syngas is not new.

Developed in the 1920’s the process has not

changed much, but with a more active

catalyst that has the ability to reduce the time

and energy input to drive the reaction forward

to increase the per pass conversion to more

than 20%. The specific reaction mechanism

and how the carbon monoxide and hydrogen

interact on different catalytic surfaces are

only being able to be understood in recent

years due to advances in spectroscopic

techniques. Many recent papers in which

methanol was produced from carbon

monoxide include the secondary dimethyl

ether (DME) synthesis reaction [7]. The

focus on methanol as a product has recently

been done using CO2 as a reactant [7]. The

traditional catalysts have problems with

using CO2 instead of CO due to water

32

Fig 1. Inhibitory effects of H2O (O, O) and CO

(☐) on methanol synthesis (O, ☐) and CO

formation (☐) from the CO2 and H2 over a

Cu/ZnO/ZrO2 catalyst. Reaction conditions:

temperature = 523 K, SV = 1.8 x 105 (1/h),

pressure of CO2 + 3H2 = 5 MPa for H2O

addition and 3.2 MPa for CO2 addition. r0 and r

are the rates of methanol synthesis or CO

formation without and with the addition of H2O

or CO to the feed. O: methanol synthesis with

the addition of H2O to the feed. O: CO

formation with the addition of H2O to the feed.

☐: methanol synthesis with the addition of CO

to the feed. [7]

formation via the CO2 route, which has a

detrimental effect on the catalyst. Saito et al

quantified this detrimental effect [7]:

Liang et al [8] were able, through trial

and error, to discover the optimal ratio of

palladium onto zinc oxide of 1:10 onto the

carbon nanotube support, which gives a good

baseline for other metallic loading onto these

materials. Grobmann et al. [9] conducted a

similar experiment with Cu loaded onto ZnO

on carbon nanotubes, and this catalyst, while

showing excellent activity, was not very

stable.

Karelovic and Ruiz [10] conducted an

experiment that provides the largest basis for

our further testing which measured reaction

rates of methanol conversion based on Cu

particle size loaded onto ZnO. They

Fig. 2. Copper loadings, surface area and size of

crystallites obtained from XRD data. Data for

catalysts recovered after the catalytic test. [10]

Fig. 3. Methanol conversion rates of Cu/ZnO

nanocatalysts based on Cu %weight. [10]

synthesized several different catalysts with

the following Cu weight percentages and

characterizations by BET and XRD.

The same authors [10] found the following

methanol conversion rates (Figure 3).

To summarize, the results show the

highest methanol conversion rate for a

Cu/ZnO nanocatalyst with a 15% weight Cu

loading, the particle size was approximately

30 nm. However, their graph cuts off at

33

230˚C for the reaction temperature during

methanol conversion [10]. Our experiment is

novel in that it investigates the methanol

conversion and thus activity of slightly

different catalysts at these higher

temperatures where the conversion rates were

highest. From the graphs in Karelovic and

Ruiz’s experiment, it is unclear what the

following behavior of the conversion rate

graph is going to be; it could continue

exponentially, approach an asymptote, or

peak then decline. We expect a peak then

decline because temperatures that are too

high would cause catalyst sintering and thus

deactivation. Therefore, our experiment will

verify this optimum operating temperature

experimentally while also comparing the

difference between a synthesized

nanocatalyst, a mixed oxide catalyst, and a

commercially available catalyst. It is also

important to note that the aforementioned

experiment synthesized their catalysts using

the citrate method which is a form of wet

chemistry synthesis [10]. Our synthetic

methods will also differ from theirs since we

used sonolysis technique that provides a

much faster catalytic synthesis.

2. Experimental Section

2.1 Materials

Cu/ZnO supported on gamma-Al2O3

industrial catalyst was purchased from

Haldor Topsoe. n-Hexadecane (95%) was

purchased from Alfa Aesar. Copper (II) oxide

nanopowder (<50 nm particle size) was

purchased from Sigma Aldrich. Zinc oxide

powder (<5 micrometer 99.9%) was

purchased from Sigma Aldrich. Methanol

(99.8%) was purchased from Sigma Aldrich.

Polyethylene glycol 400 was purchased from

Sigma Aldrich. Pressurized CO, H2, He, and

N2 gas tanks were purchased from Airgas.

2.2 Experimental Methods

2.2.1 Catalyst Preparation

To prepare the industrial catalyst for

use in the batch reactor, pellets of the Haldor

Topsoe Cu/ZnO/gamma-Al2O3 catalyst were

placed into a mortar and pestle and ground

down to a powder. The powder was the

placed in an oven at 100˚C to dry before

being placed in the batch reactor.

The nanocatalyst was prepared by

taking 3.0183 g of the aforementioned

ground Cu/ZnO/gamma-Al2O3 catalyst with

80 mL of hexadecane and placing them into

a Misonix Liquid Processor Sonicator 3000.

The sonicator was purged with N2 gas for 15

minutes to ensure that there was no oxygen

present and then the sonicator was turned on.

The solution was kept cool using a Thermo

Scientific Neslab Merlin M33 chiller set to

20˚C. Measurements of temperature and

sonicator power were made every 15 min for

over four hours. After the sonication was

complete, the catalyst was separated from the

hexadecane by centrifuge, washed with

hexanes, and then centrifuged again to rinse

away the hexanes. The sonicated catalyst was

then placed in the oven at 100˚C for at least

24 hours to dry before being placed in the

batch reactor.

To prepare the mixed oxide catalyst,

3.97 g of ZnO and 0.047 g of Cu(II)O (1% by

weight) were added with 90 mL of

hexadecane to the sonicator. The sonicator

was purged with N2 gas for 15 minutes and

then the sonicator was turned on. The

solution was kept cool using the chiller set to

20˚C. Measurements of temperature and

power were taken every 15 min for four

hours. After the sonication was complete, the

hexadecane was removed and the catalyst

dried in the same manner as before.

2.2.2 Parr Batch Reactor

A Parr model 4575 batch reactor with

a 300 mL pressure vessel was used to conduct

all reactions. The reactor was connected to

and controlled by a Parr 4848 process

controller. 0.6756 g of each catalyst and 60

mL of polyethylene glycol were added into

34

the batch reactor, which was then sealed and

purged with nitrogen gas. 100 psi CO and 200

psi H2 were added to the reaction vessel and

the reactor was heated to the operating

temperature. Internal temperature, internal

pressure, and jacket temperature were all

recorded every 15 minutes. Each reaction

was run at the specified temperature for three

hours, then the heating jacket was turned off,

and the system was allowed to cool to room

temperature. The final pressure was noted

once the system had cooled, and liquid

samples were taken for analysis.

2.3 Analytical Methods

2.3.1. Gas Chromatography (GC)

For Gas Chromatography, a Gow-

Mac gas chromatograph series 580 was used.

Experiments were run using helium as the

carrier at a 24 ml/hr gas flow rate (measured

with ProFLOW 6000 from Restek). The GC

additionally used chromosorb 101 packing

material. And the GC was set as follows:

column temperatures: 150°C; detector

temperature: 190°C; current: 200mA. To

analyze the results, Clarity PC

Chromatography software was used to collect

the data for 10 min. Each set of data was

compared to a standard of methanol that was

injected under the same operating conditions

2.3.2. X-ray Diffraction (XRD)

X-ray Diffraction (PXRD) testing of

the catalyst powder was carried out using a

Rigaku Ultima IV diffractometer (Cu Kα; λ =

1.5405 Å). Additionally, a D/teX ultra high-

speed 1D position sensitive detector was

used. Our catalysts were dried in an oven at

100˚C before being packed and prepped for

XRD.

2.3.3. Scanning Electron Microscopy

Scanning Electron Microscopy (SEM)

was carried out using a LEO-1550 Gemini

microscope.

2.3.4. Transmission Electron Microscopy

Transmission Electron Microscopy

(TEM) was performed using the JEOL JEM

1400 microscope. It utilizes an acceleration

voltage of 40-120 kV with a magnification of

x5000-2,000,000/x120-4,000. Catalysts were

suspended in a solution of hexadecane and

mixed by sonic bath prior to TEM. Particle

size was found using Image-J software and

averaged across all images for each catalyst.

2.3.5. Energy Dispersive X-Ray Spectroscopy

Energy-Dispersive X-Ray Spectroscopy

(EDS) was jointly carried out under scanning

electron microscopy.

3. Results and Discussion

3.1 Catalyst Characterization

a. X-ray Diffraction (XRD):

Fig. 4. XRD spectra of catalysts tested for

methanol synthesis. (a) Nanocatalyst

Cu/ZnO/gamma-Al2O3.

(b) Mixed Oxide Catalyst: CuO/ZnO

35

The XRD data compares our two

synthesized catalysts, which we expected to

have a similar interference pattern and

crystallinity in the nanoparticle range. The

nanocatalyst showed similar peaks for both

ZnO and CuO standards with broader peaks,

indicating nano-sized crystallinity. However,

the mixed oxide catalyst showed a high

crystallinity with sharp peaks matching the

ZnO standard and no presence of CuO.

b. Energy Dispersive X-ray Spectroscopy

(EDS):

Fig. 5. EDS characterization of all three catalysts.

(a) Nanocatalyst Cu/ZnO/gamma-Al2O3. (b)

Mixed Oxide Catalyst: CuO/ZnO.

To further investigate the presence of

CuO in the mixed oxide catalyst, we

conducted EDS. The nanocatalyst in Fig. 5a

has well-defined Cu, Zn, Al, and O peaks

whereas the mixed oxide catalyst in Fig. 5b

only defines Zn and O peaks. We attribute the

Al in the nanocatalyst to the gamma-alumina

support and did not expect this in the mixed

oxide data. However, this confirms the lack

of Cu or CuO in the mixed oxide catalyst. It

is possible that this would not appear in either

XRD or EDS testing if the Cu did not attach

to ZnO during the sonication process. It is

also possible that CuO may have been present

in such a low amount from the 1% weight

addition that it did not manifest itself on

either spectrum. This occurred for Karelovic

and Ruiz’s study when they used low weight

percents of 0.5% and 1% of Cu on ZnO [10].

c. Scanning Electron Microscopy (SEM):

Fig. 6. SEM images of catalysts. (a) Nanocatalyst

Cu/ZnO/gamma-Al2O3. (b) Mixed Oxide

Catalyst: CuO/ZnO.

Since the EDS tests were coupled

with SEM images, we were also able to view

our catalysts’ surface morphology. The

images in Fig. 6 show that the mixed oxide

catalyst had more defined particles than the

nanocatalyst. There are clear, three-

dimensional agglomerations present in the

images in addition to singular particles

dispersed throughout.

800 nm

800 nm

(a)

(b)

(a)

(b)

36

d. Transmission Electron Microscopy

(TEM):

Fig. 7. TEM images of catalysts and

corresponding average particle size. From left to

right: (a) Nanocatalyst Cu/ZnO/gamma-Al2O3

(b) Mixed Oxide Catalyst: CuO/ZnO (c)

Quenched Nanocatalyst Cu/ZnO/gamma-Al2O3

(d) Quenched Mixed Oxide Catalyst (e)

Quenched Commercial Catalyst

Table 1. Average catalyst particle size

The mixed oxide catalyst was the

only catalyst to not agglomerate after being

subjected to the reaction conditions within

the batch reactor. The general trend for this

catalyst was that a smaller particle size was

correlated with a higher reaction temperature.

While the SEM images showed

agglomerations present prior to the catalysts

undergoing reaction conditions, the TEM

images still yielded some singular particles

that were averaged to calculate the sizes in

the chart. However, in the portions labeled

“Agglomeration” there were no singular

particles present, making measuring particle

size impossible. This meant we could not

determine whether sonication affected our

nanocatalyst enough to significantly change

particle size. In Fig. 7d, e, and f, the images

are from the post 240˚C batch runs for each

quenched catalyst.

Pre

Batch,

nm

Post

240˚C,

nm

Post

230˚C,

nm

Post

220˚C,

nm

Comm-

ercial

[data not

available]

Agglom-

eration

Agglom-

eration

14.7

Nano-

catalyst

12.0 Agglom-

eration

Agglom-

eration

Agglom-

eration

Mixed

Oxide

52.7 48.8 63.9 75.2

a)

c)

b)

e) d)

37

Table 2. Calculated kinetic results from batch data

3.2. Conversion Kinetics

The activation energies for the

nanocatalyst, commercial catalyst, and mixed

oxide catalyst were 197.1 J/mol, 158.0 J/mol,

and 138.2 J/mol respectively for each

reaction temperature. These were calculated

using the Arrhenius equation and finding the

slope of ln(k) vs. 1/T which gives EA/R. This

was then multiplied by R to give the

activation energy for each catalyst

throughout the three different reaction

temperatures. As indicated in the table above

the mixed oxide catalyst at a reaction

temperature of 240˚C showed the highest

percent conversion to methanol of 36.3%.

These represent the optimized reaction

conditions for highest conversion and lowest

initial energy input.

In terms of percent conversion, the

next best catalyst and reaction temperature

are the nanocatalyst at 220˚C. However, the

SEM and TEM data showed large

agglomerations for this, which most likely

contributed to inhibited catalytic activity,

and the main way this was overcome was by

the large activation energy of 197.1 J/mol.

The catalyst with the overall lowest

conversion was the nanocatalyst. According

to the Table 1, its particle size was 12 nm

which is significantly smaller than the mixed

oxide’s size of 52.7 nm. Typically smaller

particle sizes for catalysts yield higher

activity due to higher surface area exposed

during the reaction. However, our results

contradict this and show that depending on

the reaction conditions coupled with each

catalyst, sometimes a larger particle size is

favored for overall higher reaction efficiency.

It is possible that the large agglomeration of

the nanocatalyst could be due to its uniquely

small particle size, thus inhibiting its catalytic

activity.

Catalyst and

Reaction Temperature

MeOH Formed,

mol

Rate,

mol/hr

Space Time Yield,

kg MeOH /

kg catalyst / hr

Conversion, %

Commercial catalyst 240˚C 6.4 2.1 101.2 7.7

Commercial catalyst 230˚C 5.3 1.8 83.8 6.3

Commercial catalyst 220˚C 15.6 5.2 246.6 18.9

Nanocatalyst 240˚C 3.3 1.1 52.8 4.0

Nanocatalyst 230˚C 9.2 3.1 145.4 11.0

Nanocatalyst 220˚C 18.9 6.3 298.8 22.6

Mixed oxide catalyst 240˚C 30.3 10.1 479.0 36.3

Mixed oxide catalyst 230˚C 8.3 2.8 131.2 9.9

Mixed oxide catalyst 220˚C 8.9 3.0 140.7 10.7

38

Fig. 8 Gas Chromatography Data; a) Baseline methanol peak. b) Peaks for Nanocatalyst Post-220˚C.

3.3 Product Characterization

a. Gas Chromatography

Using the GC methods described

above, pure methanol was found to have a

retention time of 6-8 min as shown in Fig. 8.

For all of our liquid samples, we

observed peaks at similar retention times that

matched for a reference methanol sample.

These data confirmed that we successfully

produced methanol in all of our experimental

batch runs. The small peak between 2 and 4

minutes was observed on most samples,

which we attribute to unreacted syngas

remaining in the PEG liquid.

4. Conclusions

After thorough experimentation using

SEM, EDS, GC, XRD, and TEM for analysis

and characterization of multiple catalysts for

methanol synthesis from syngas, we

conclude that the mixed oxide catalyst is

most efficient of all tested when coupled with

a reaction temperature of 240˚C. This

combination of parameters yielded the fastest

conversion and the highest percent

conversion of methanol with the lowest

activation energy. The lack of particle

agglomeration as indicated by TEM is likely

a contributing factor to the success of this

material. This catalyst, like the others, was

tested at 220 ˚C, 230 ˚C, and 240 ˚C. While

the best-performing variation was mixed

oxide catalyst at 240 ˚C, the next best was the

nanocatalyst at 220˚C indicating that the

synthesis of methanol from syngas is a highly

temperature-dependent reaction and varies

(b)

(a)

40

depending on catalyst. At 240 ˚C, the mixed

oxide catalyst exhibited a 36.3% conversion

and formed 30.3 mol of methanol at 10.1

mol/hr. Since the general temperature

dependency trend showed that the

commercial catalyst and nanocatalyst had

poorer conversion at higher temperatures, we

believe their optimal operating temperature

was the lowest in our study of 220˚C. This

may be due to increased agglomeration at

higher temperatures, particularly with the

large gamma-alumina supports. The mixed

oxide catalyst showed the opposite trend of

increased conversion at higher temperatures,

which is why further research is necessary to

identify its optimal reaction temperature,

which may in fact be even higher than 240˚C.

Further experiments should study other

methods of catalyst synthesis exploring our

sonication technique as well as continue to

look into methods for preventing

agglomeration. It would also be beneficial to

continue to study these catalysts in broader

reaction temperature ranges and other reactor

types such as continuous or larger scale.

Acknowledgements

This project was supported by

funding from the National Science

Foundation. The authors thank the Low

Carbon Energy Laboratory at the Advanced

Energy Research and Technology Center for

providing the laboratory and equipment used

in conducting experiments and analysis. The

Material Science and Chemical Engineering

Department at Stony Brook University is

acknowledged for their financial and

academic support of this project. We would

like to thank Dr. Devinder Mahajan and

Nyima Choephell from the Material Science

and Engineering Department at Stony Brook

for their support and mentorship, Chung-

Chueh Chang in the Materials

Characterization Laboratory for the TEM

images, and Dr. Jim Quinn for the SEM,

XRD, and EDS spectra and information.

References

[1] Pti. "Nitin Gadkari Pushes for

Leapfrogging into Methanol Economy." The

Economic Times. N.p., 11 Sept. 2016.

[2] Yang, Chi-Jen, and Robert B. Jackson.

"China's Growing Methanol Economy and Its

Implications for Energy and the

Environment." Energy Policy 41 (2012):

878-84. Web.

[3] Smith, J. M., Van Ness H. C., and Michael

M. Abbott. Introduction to Chemical

Engineering Thermodynamics. Boston:

McGraw Hill, 2005.

[4] Bromberg, L., and W.K. Cheng.

"Methanol as an Alternative Transportation

Fuel in the US: Options for Sustainable

And/or Energy-secure Transportation." U.S.

Department of Energy: Alternative Fuels

Data Center. U.S. Department of Energy:

Office of Energy Efficiency & Renewable

Energy, n.d. Web.

[5] Mota, N., C. Alvarez-Galvan, RM

Navarro, and JLG Fierro. "Biofuels." Biogas

as a Source of Renewable Syngas

Production: Advances and Challenges:

Biofuels: Vol 2, No 3. Taylor and Francis

Online, n.d.

[6] J. B. Hansen and P. E. Hojlund Nielsen,

in Handbook of Heterogeneous Catalysis, ed.

G. Ertl, H. Knözinger, F. Schüth and J.

Weitkamp, Wiley-VCH, 2008, pp. 2920–

2949.

[7] Saito, M., Fujitani, T., Takeuchi, M., &

Watanabe, T. (1996). Development of

copper/zinc oxide-based multicomponent

catalysts for methanol synthesis from carbon

dioxide and hydrogen. Applied Catalysis A:

General, 138(2), 311-318.

39

41

[8] Liang, X., Dong, X., Lin, G., & Zhang, H.

(2009). Carbon nanotube-supported Pd–ZnO

catalyst for hydrogenation of CO2 to

methanol. Applied Catalysis B:

Environmental

[9] Großmann, Dennis, Axel Dreier,

Christian Lehmann, and Wolfgang Grünert.

"Methanol Synthesis over Cu–ZnO

Aggregates Supported on Carbon

Nanotubes." Applied Catalysis A: General,

n.d.

[10] Karelovic, Alejandro, and Patrico Ruiz.

"The Role of Copper Particle Size in Low

Pressure Methanol Synthesis via CO2

Hydrogenation over Cu/ZnO Catalysts." The

Role of Copper Particle Size in Low Pressure

Methanol Synthesis via CO2 Hydrogenation

over Cu/ZnO Catalysts - Catalysis Science &

Technology (RSC Publishing). The Royal

Society of Chemistry, n.d.

40

41

The Effects of SBS Tri-Block Copolymer Surface Roughness on

Protein Adsorption

Megan Lenore, Nicole Passariello, Asim Rattu, Tadanori Koga, Yizhi Meng, Mani

Sen, Derek Rammelkamp, Weiyi Li

Chemical and Molecular Engineering Program, Stony Brook University, Stony Brook, NY 11794

Abstract:

Polymers can be used for a variety of different tasks including biomedical applications.

However, most polymers are inherently incompatible with biomolecules such as proteins and

DNA. There is growing evidence to suggest that surface topography of a polymer can affect the

biocompatibility, due to the effects that the surface morphology has on the protein

adsorption. Greater protein adsorption to the surface is useful in applications such as orthopedic

implants. However, since immune reactions are common in contact medical devices, ways to

reduce protein adsorption are valuable to explore for blood or tissue contact devices. Here we aim

to develop a biocompatible polymer surface using a polystyrene (PS)-block-polybutadiene (PB)-

block-polystyrene (PS) triblock copolymer (SBS). SBS is not biocompatible on its own, but

allows us to change the surface topology by changing the annealing times of the triblock copolymer

or etching.

We prepared SBS thin films (60-80 nm thick) on both silicon substrates and glass

disks. For comparison purposes, PS and PB homopolymer thin films were also prepared on the

substrates. Atomic force microscopy images verified the concept that changing annealing time

effects the surface topography. Protein experiments using fluorescent BSA protein were

conducted to determine the effects of controlling surface topography on protein adsorption. The

results evidenced that the PS and PB homopolymers show very minimal protein adsorption,

while the SBS triblock copolymer show much more protein adsorbed on the surface. In addition,

we found as the annealing times increased, less protein adsorbed onto the polymer surface.

Keywords: Tri-block copolymer, protein adsorption, annealing, atomic force microscopy

1 Introduction

Polymers are widely used in everyday

life in products such as plastic bags, bottles,

rope, piping, packaging and coatings.

Additionally, they have an important role in

research for use in medical devices. Research

is being done to see if modifications to

polymers can either increase or decrease

protein adsorption on their surfaces. Many

have studied surface microtopography, and

its ability to influence cell behavior, and the

effects that changing these surface

micropatterns have on the amount of protein

adsorbed on the surface [2][8][9]. There are

many ways that surfaces can be modified in

order to be patterned, including spincasting,

molding, and sonication. Each of these

methods have their advantages in surface

patterning and can create different sized

surface structures. Previous literature

concludes that patterned surfaces adsorb

more protein on their surfaces than flat

surfaces [2]. Figure 1 shows this principle;

the pattern surface adsorbed 46% more

protein than the flat surface with only an 8%

increase in the surface area [2].

Many polymers are not

biocompatible and therefore cannot be used

for medical devices because they may be

harmful to living tissue [7]. Two polymers

42

Figure 1. The effects of creating a patterned

surface and protein adsorption [2].

used for the present study are polystyrene

(PS) and polybutylene (PB). In order to use

either of these two polymers with less

biocompatibility for uses in the body, they

must be modified for the specific use of the

medical device. For applications with

orthopedic implants, a surface that adsorbs

protein is necessary: however, less protein

adsorption is desirable for uses in blood or

tissue contact devices. Protein adsorption is

important for designs of biosensors, medical

diagnostics devices, and drug delivery

vehicles.

Although some other polymers are

naturally biocompatible, they are often very

expensive; PS and PB are both fairly

inexpensive polymers and would be ideal for

uses where high protein adsorption is needed.

A proposed solution to this problem would be

the use of a PS-block-PB-block-PS triblock

copolymer (SBS) as the coating for

biomedical devices. Ideally, the block

copolymer will adsorb proteins better than

the PS and PB homopolymers. Block

copolymers are appealing for this application

because of their ability to self-assemble into

ordered microdomains [1]. The way that

these copolymers pattern could be taken

advantage of in order to obtain the highest

possible protein adsorption [1].

Block copolymers have been studied

extensively due to their repulsion between

unlike blocks, which leads to the microphase

separation. Most research in this field

involves thermal annealing for long periods

of time in order to minimize surface-

thermodynamic effects, remove volatile

solvents, and maximize the chances of the

surface reaching thermodynamic equilibrium

morphology. Block copolymers can be used

as a template for surface patterning, and there

are many methods for changing their

surfaces. One study, “Fabrication of

nanopatterns using block copolymer and

controlling surface morphology,” tests

surface morphology changes through several

methods [3]. This study shows that

differences in polymer solution and spin

coating speeds affect the thickness of the

polymer layer, which in turn affects the

overall surface pattern. Additionally,

different sonicating solvents created different

surface pattern, and a combination of

solvents can create fingerprint regions along

the surface. The study also showed the ways

that polymer surfaces can be modified, and

the different methods for removing different

substances on the surface [3]. Research has

also shown that SBS can maintain periodicity

of spheres, cylinder, or lamellae [5].

While the chemical composition of

different polymers or copolymers affects the

ability of proteins to adsorb to a surface, the

surface itself can affect protein adsorption.

Previous research has shown that surface

topography on the nanoscale level can have

an effect on protein adsorption. There are a

few ways these surface patterns can be

created. Some of these techniques are photon

ad electron based lithography, nano-imprint

lithography, and etching and glancing angle

deposition [4]. The combination of polymers

and topography can enhance or diminish

protein adsorption on surfaces, resulting in

better surfaces for many medical

applications.

The goal of this experiment is to test

the effects of surface roughness via a thermal

43

annealing process on protein adsorption. We

hypothesize that greater the surface

roughness (i.e., annealing times) less proteins

will adsorb on the surface.

2. Materials and Methods

2.1 Materials

290k molecular weight polystyrene

was purchased from Sigma Aldrich. 38k

molecular weight polybutylene was

purchased from Polymer Source Inc. Toluene

was purchased from VWR. 85k molecular

weight SBS was purchased from Asahi-Kasei

Chemical Corporation. 30 mg/mL FITC-

BSA was purchased from Protein Mods. PBS

was purchased from Invitrogen. Glass disks

were purchased from Ted Pella, Inc. Silicon

wafers were purchased from University

Wafers. 9” Glass Pasteur Pipet was

purchased from VWR.

PS, PB, and SBS were all dissolved in

toluene and left overnight before spincasting

the polymer films. Samples were annealed in

a vacuum oven for varying durations of time

at 130 C. 30 mg/mL FITC-BSA was diluted

with PBS to the desired concentration of

1mg/mL before performing protein

incubation.

2.2 Experimental Methods

2.2.1 Spincasting

We first spincasted the polymers onto

substrates. The polymers used were PS, PB,

and SBS. The polymers were spincasted

(Headway Research, Garland, TX) onto both

glass disks and silicon wafers so they could

be imaged through optical microscopy as

well as atomic force microscopy. Before

spincasting, glass disks and silicon wafers

were cleaned. The glass disks were cleaned

by sonication (Ultrasonic Cleaner, Branson

200) in ethanol for one minute. Silicon

wafers were cleaned using a 1:1:1 solution of

water, hydrogen peroxide, and ammonium

hydroxide for 20 minutes followed by using

a 1:1:1 solution of water, hydrogen peroxide,

and sulfuric acid. After cleaning the glass

disks and silicon wafers, they were placed on

the spincasting apparatus and spun until

excess liquid was removed, generally 30

seconds. Once the excess liquid was

removed, the polymer was spincasted onto

the substrate for 30 seconds at 2500 rpm.

The silicon wafers were cleaned prior

to us working with them. The polymers were

spincasted onto the silicon wafers so that the

thickness of each polymer film could be

recorded using the ellipsometer. The number

of samples spincasted was based on how

many different annealing times were going to

be tested. For each annealing time that was

going to be tested, both a glass disk and

silicon wafer were spincasted.

2.2.2 Annealing

Once the substrates were spincasted,

they were annealed. They were annealed in a

vacuum oven (Sheldon Manufacturers,

Cornelius, OR) at 130 °C. Samples were

annealed for different time periods so that the

effects of annealing time on surface

topography could be observed. The time

periods they were annealed for were 0 hours,

8 hours, 24 hours, and 48 hours. High

temperature annealing of polymer thin films

improves their uniformity [6].

2.2.3 FITC BSA Protein Experiment

After samples are annealed, the

protein incubation was completed on the

glass disks. The protein incubation had to be

performed in a BSL-2 laboratory, which we

were all trained for. A 1 mg/mL solution of

fluorescein conjugate (FITC) Bovine Serum

Albumin (BSA) protein in PBS was diluted

from a 30 mg/mL stock solution. 100

microliters of this dilute solution were

pipetted onto a glass plate that had been

covered in parafilm. On top of each drop the

annealed spincasted sample was placed with

the surface side down, so the polymer

directly interacted with the BSA

44

protein. Each sample was incubated for 20

minutes, then rinsed in water and dried with

compressed air. The dry samples could then

be analyzed through optical microscopy.

2.3 Analytical Methods

2.3.1 Ellipsometer

The ellipsometer (Rudolph Research

AutoEL, Hackettstown, NJ) was used to

determine the thickness of the polymer

film. In order to find the thicknesses of the

films, the polymer must be spincasted onto

silicon wafers, as opposed to glass, because

the laser used to identify the thickness is

refracted off of the silicon surface. The index

of refraction used for SBS is 1.530.

2.3.2 Optical microscope

After the samples were rinsed and

dried with compressed air, images were taken

using a fluorescent optical

microscope. Since the type of BSA used was

fluorescein conjugated, areas glowed bright

green where protein adsorbed to the surface.

The microscope was set to FITC with a

magnification of 10x. The sample was

placed onto the center of the microscope with

the protein side up. The microscope was

adjusted so that the image on the computer is

clear. Once the image was clear, images at

three different locations on the sample were

taken. On the computer program, the color

was adjusted to be fluorescent green.

2.3.3. AFM

Atomic force microscopy was used to

show the surface topography of each

sample. AFM was taken at a variety of

different specifications including wide and

zoom magnification.

3. Results and Discussion:

First, PS and PB homopolymers were

spincasted and protein adsorption was tested

using the FITC BSA protein experiment. The

results of the protein experiment are shown

below in Figure 2. The PS and PB films were

spincasted to be 41 nm and 227 nm

respectively.

3.1 PS and PB Homopolymers

Figure 2. Optical microscope images of PS

(annealed 24 hrs at 130C: 40.8 nm thickness) and

PB (annealed 24 hrs at 80C: 227.7 nm thickness)

at different incubation times compared to glass

disks with and with BSA. untreated w/o BSA (I),

untreated w/ BSA 20 min (II), PS 10 min (III), PS

20 min (IV), PB 10 min (V), PB 20 min (VI)

The results from the protein

incubation of PS and PB both show that less

proteins adsorb to the substrates when there

is a polymer layer, as opposed to the

untreated glass disk, which does not have

polymer layer on it (see Fig. 2). These images

were used a basis for the next test of PS-

block-PB-block-PS triblock copolymer.

3.2 Thick Out-of-Period SBS thin films (78

nm thick)

3.2.1 AFM

45

AFM images were taken of the 78 nm

SBS thin films at three different annealing

times. We made samples with no annealing,

8 hour annealing, 24 hour annealing, and 48

hour annealing. The sample with no

annealing had dust particles on it, therefore

we could not get a good AFM image. The

other three AFM images are shown in Figure

3.

Figure 3. AFM images of SBS (annealed at

130℃: 78 nm thickness) at varying annealing

times. 8 hrs (I), 24 hrs (II), 48 hrs (III)

These AFM images show dewetting,

or the rupture of the thin film, which is caused

by the surface energy differences between the

surface and the film. In order to determine

whether the film dewetted down to the silicon

wafer surface, a scratch test was completed

and the AFM is shown in Figure 4.

3.2.2 Scratch Test

Figure 4. Scratch test AFM height image of SBS

(annealed at 130C: 78 nm thickness) with 40 μm

scale.

The height AFM image shown in

Figure 4 shows that the film did not dewet

down to the silicon wafer surface, based upon

the cross-sectional analysis. The cross-

sectional analysis shows that vertical distance

between the scratched and unscratched

surfaces is 37 nm, and the total film thickness

is 78 nm. Since the vertical distance is only

37 nm, which is not the total height of the

polymer film, we can conclude that the

polymer film did not dewet down to the

silicon surface. Rather, the SBS film show a

terrace structure, as previously reported [10].

We take advantage of this “rough” structure

for the study.

3.2.3 FITC BSA Protein Experiment on the

SBS 78 nm film

The FITC BSA protein experiment

was completed on 78 nm SBS thin films at

four annealing times. The results are shown

in Figure 5.

Figure 5. Optical microscope images of SBS

(annealed at 130C: 78 nm thickness) on glass

disks at varying annealing times. 0 hr (I), 8 hr (II),

24 hr (III), 48 hr (IV)

The images shown indicate that are

changes in protein adsorption when

introduced to varying annealing times. The

bright fluorescent areas show greater protein

adsorption. The problem with this method is

that the filter used on fluorescent microscopy

makes the entire image a green hue, making

it hard to determine which areas of the film

actually have adsorbed protein. From these

images, the no annealing and 8 hour

46

annealing (Fig. 5, I and II) seem to have

adsorbed the most protein, as seen by their

bright green areas. By that same logic, the

darkness of the 24 and 48 hour samples (Fig.

5, III and IV) show less protein adsorption.

3.3 Thinner In-Period SBS thin film (60

nm)

3.3.1 AFM

We first characterized the surface

structures with AFM. 60 nm SBS films were

spincasted, and the AFM images are shown

in Figures 6 and 7.

Figure 6. Wide AFM images of SBS (annealed at

130C: 60 nm thickness) at varying annealing

times. 0 hrs (I), 24 hrs (II), 48 hrs (III)

Figure 7. Zoom AFM images of SBS (annealed

at 130C: 60 nm thickness) at varying annealing

times. 0 hrs (I), 24 hrs (II), 48 hrs (III)

As seen with the 78 nm films,

different annealing times lead to a

topological change in the films. From here,

we tested the protein adsorption to the 60 nm

SBS films to see if there was a correlation

between the surface morphology and the

amount of protein adsorbed.

3.3.2 FITC BSA Protein Experiment on the

SBS 60 nm film

The FITC BSA protein experiment

was completed on the 60 nm thick films. The

samples were incubated for 20 minutes each.

The results can be seen in Figure 8 below.

Figure 8. Optical microscope images of SBS

(annealed at 130C: 60 nm thickness) on glass

disks at varying annealing times. 0 hr (I), 8 hr (II),

24 hr (III), 48 hr (IV)

Figure 8 shows the protein adsorption on the

60 nm films annealed at the four different

times. The exposure time for the samples was

kept consistent at 31 seconds. The bright

fluorescent green spots on the images

indicate protein adsorption to the surface.

The brighter spots on an image indicates that

more protein was adsorbed to the polymer

surface. Figure 8 images I and II have more

protein than images III and IV.

3.3.3 AFM for the SBS thin films on GLASS

In order to view the FITC BSA under

fluorescent microscopy, the thin films needed

to be spincasted onto glass disks. However,

to make the AFM imaging easier and to test

the thickness on the ellipsometer, the films

needed to be spincasted onto silicon wafers.

Since silicon wafers and glass have differing

chemical formulas (SiOx vs. SiO) we could

not logically assume that the interaction

between the polymer and the surface is the

same for the two. In order to test this, AFM

of the 60 nm SBS samples were tested on

glass disks, seen below in Figure 9.

The patterning of the SBS film is

similar on both the silicon wafers on glass

disks, as seen in Figures 7 and 9. Although

there are some differences in the 8 hour, the

47

Figure 9. AFM images of SBS (annealed at

130℃: 60 nm thickness) at varying annealing

times. 8 hrs (I), 24 hrs (II), 48 hrs (III)

24 and 48 hour samples are very similar in

morphology.

3.4 Discussion

From analysis of the AFM image in

Fig. 4, we concluded that the surface did not

dewet down to the silicon. This conclusion

allows us to confirm the fact that we have

created the rough surfaces for the proposed

protein adsorption experiments.

The AFM images of both 78 nm and

60 nm samples show that as annealing time

progresses, surface morphology changes.

Less annealing time creates more island-like

structures, while greater annealing times

shows these islands merging together into a

conglomerate. The darker areas of the AFM

images show the valleys in the film, while the

lighter areas show the peaks.

SBS has a domain spacing of 30 nm,

as clarified by small-angle x-ray scattering

performed by the Koga group. This spacing

refers to the distance between PB blocks in

the SBS triblock copolymer. Films that

follow the rule of being a multiple of the

domain spacing will show symmetrical

wetting. Since the 78 nm sample did not

follow this rule, the surface pattern could not

have symmetrical wetting, which caused

such a terrace structure to occur. Due to this,

we decided to try to make 60 nm SBS films

to note the difference in surface morphology.

There was a visible difference in

morphology between the 60 nm and 78 nm

films. The differences in morphology

contributed to protein adsorption. Figure 5

and Figure 8 show the protein adsorption on

the 78 nm and 60 nm films, respectively.

While it appears that about the same amount

of protein adheres to the samples based on the

annealing time, the patterns at which they

anneal are different. The protein adsorption

that occurs in Figure 8 images I and II

adheres in bands while in Figure 5 images I

and II still have the same amount of protein,

they do not seem to have any type of bands of

proteins. This occurs because of the actual

surface. The surfaces of the 60 nm films are

less random leading to the proteins to adhere

in the same way on the films.

Figures 6 and 7 show the AFM

images for the 60 nm SBS samples with white

dots across the frame. We attribute these

small dots to dust particles on the sample, or

small defects in the sample, but we are

assuming they had no effect on the protein

adsorption on the surface.

Our hypothesis stated that changing

the annealing time of polymer films would

change the amount of protein that adhered to

the surface. More specifically, the amount of

FITC BSA that adsorbed to SBS films would

change based on the annealing times the films

were subjected to. Our results show that the

longer a sample is annealed, the less protein

will adhere to the sample.

4. Conclusion Changing the surface topography of a

polymer film is one way to achieve the

biocompatible properties that a scientist or

researcher may want. Surface topography

can be changed by varying the length of

annealing times. The longer the SBS films

were annealed in the vacuum ovens, the less

protein adsorption to the polymer surface

occurred.

Biocompatible properties are

important in the study of medical devices and

applications. Coatings are needed to assure

that the devices being introduced into the

body are not rejected. Some devices need to

have the ability to adsorb proteins while other

48

devices do not want to adsorb proteins. The

different characteristics depend on where the

device will be going into the body and what

its purpose is. In order to create these

coatings, polymers are used on the devices.

However, many of the polymers that are

currently available are expensive and could

be further optimized. Our research showed

that controlling the surface topography has a

direct effect on the protein adsorption.

Future researchers can examine a

variety of different things. Researchers can

perform the experiment with different

proteins. Using different proteins will allow

for this polymer film to be used on medical

devices entering different parts of the body.

An ideal annealing time can also be observed

by performing the experiment at more

annealing times.

Acknowledgements This research was conducted at Stony

Brook University. Authors would like to

thank Stony Brook University’s Chemical

and Molecular Engineering Professors Dr.

Tad Koga and Dr. Yizhi Meng for their

guidance and the use of their

labs. Additionally, they would like to thank

graduate students Mani Sen, Derek

Rammelkamp, and Weiyi Li for their

assistance in the experiments.

References: [1] Gu, Xiaodan, "Self-Assembly of Block

Copolymers by Solvent Vapor Annealing,

Mechanism and Lithographic Applications"

(2014). Doctoral Dissertations May 2014 -

current. Paper 7.

[2] Chen, Hong, Wei Song, Feng Zhou,

Zhongkui Wu, He Huang, Junhu Zhang,

Quan Lin, and Bai Yang. "The Effect of

Surface Microtopography of

Poly(dimethylsiloxane) on Protein

Adsorption, Platelet and Cell Adhesion."

Colloids and Surfaces B: Biointerfaces 71.2

(2009): 275-81. Web. 10 Oct. 2016.

[3] Alam, Md. Mahbub, Yu-Rim Lee, Jin-

Yeol Kim, and Woo-Gwang Jung.

"Fabrication of Nanopatterns Using Block

Copolymer and Controlling Surface

Morphology." Journal of Colloid and

Interface Science 348.1 (2010): 206-10.

Web. 16 Nov. 2016.

[4] Lord, Megan S., Morten Foss, and

Flemming Besenbacher. "Influence of

nanoscale surface topography on protein

adsorption and cellular response." Nano

Today 5.1 (2010): 66-78.

[5] Kim, G. and M. Libera. "Morphological

Development in Solvent-Cast Polystyrene-

Polybutadiene-Polystyrene (SBS) Triblock

Copolymer Thin Films." Macromolecules,

vol. 31, no. 8, 21 Apr. 1998, p. 2569-2577.

[6] Pandey, Rajiv K., Arun Kumar Singh,

and Rajiv Prakash. "Directed Self-Assembly

of Poly(3,3’’’-dialkylquarterthiophene)

Polymer Thin Film: Effect of Annealing

Temperature." The Journal of Physical

Chemistry C 118.40 (2014): 22943-2951.

Web. 2 Apr. 2017.

[7] Thevenot, Paul, Wenjing Hu, and Liping

Tang. “SURFACE CHEMISTRY

INFLUENCE IMPLANT

BIOCOMPATIBILITY.” Current topics in

medicinal chemistry 8.4 (2008): 270–280.

Print.

[8] Sivaniah, E., Y. Hayashi, S. Matsubara, S.

Kiyono, T. Hashimoto, K. Fukunaga, E. J.

Kramer, and T. Mates. "Symmetric Diblock

Copolymer Thin

Films on Rough Substrates. Kinetics and

Structure Formation in Pure Block

Copolymer Thin

Films." Macromolecules 38.5 (2005): 1837-

849. Web.

[9] Bonduelle, Colin V., Solmaz

Karamdoust, and Elizabeth R. Gillies.

"Synthesis and Assembly of Butyl Rubber-

Poly(ethylene Oxide) Graft Copolymers:

From Surface Patterning to Resistance to

Protein Adsorption.” Macromolecules 44.16

(2011): 6405-415. Web. 5 Feb. 2017.

49

[10] Lambooy, P., T. P. Russell, G. J.

Kellogg, A. M. Mayes, P. D. Gallagher, and

S. K. Satija. "Observed Frustration in

Confined Block Copolymers." Physical

Review Letters. N.p., 01 May 1994. Web. 12

Apr. 2017.

50

Transdermal Patch Simulation Using the Lattice Boltzmann

Method with Active Diffusion in the Cell and Lipid

Pathways

Angell Cheea, Calvin Chenga, Steven Zhua, and Jiaolong Jianga a Materials and Science Engineering Department, Stony Brook University, Room 314 Old

Engineering, Stony Brook, New York 11794, United States

Abstract The transdermal patch is continuing to grow in popularity as a drug delivery system that

shows promising results. Since transdermal patch testing on humans is expensive, time consuming,

and limited due to ethical constraints, a simulation of this system would provide a relatively

cheaper option. An existing model simulates this system but only accounts for drug diffusion in

the skin lipid layer. Our model expanded on this simulation to also involve the drug diffusion

through the skin cell layer. Through this simulation, it was found that the cell layer introduces a

much more rapid drug diffusion through the stratum corneum (SC). A sensitivity test was done on

the lipid diffusion coefficient, cell diffusion coefficient, and partition coefficient to see their effect

on the overall properties of the transdermal diffusion. From these tests, the cell layer was

concluded to be the most important factor in transdermal diffusion. The cell diffusion coefficient

affected the drug diffusion the most while the lipid diffusion coefficient and the partition

coefficient showed negligible changes.

Keywords: Transdermal Patch, Lattice Boltzmann, Stratum Corneum, Drug Delivery, Brick and

Mortar

1. Introduction Computer modeling of transport

processes provided a relatively inexpensive

means to observe how interactions of

molecules on the atomic level affect a system

as a whole. It encompassed computation and

theoretical knowledge in order to create a

model that can simulate real life situations.

Numerical methods have been developed to

model various situations and help in

processing the simulation [1].

In the paper, we used a lattice

Boltzmann model to study the diffusion of

drugs from a drug patch through the human

skin. Getting data from experimentation can

be expensive and are prone to human error.

This, combined with human experimentation

can make it very difficult for scientists to test

for efficient transdermal patches [2]. By

creating a model, scientists will be able to

modify different parameters to find the key

parameters affecting the drug flux or peak

times in the patch. By running the parameters

through numerous simulations, the accuracy

of the human trials conducted can be greatly

improved.

The simulation will involve the

diffusion of drugs from a transdermal patch

to the outermost layer of the human skin, the

stratum corneum (SC). The stratum corneum

is the first barrier any foreign substance will

encounter when trying to enter the human

body. It has proved to be very efficient in

keeping these substances out [3]. In fact, this

barrier is often considered the rate-limiting

step of the drug diffusion.

51

Figure 1. Brick and mortar representation of the

stratum corneum layer

In order to create this model, we first

needed to create a structure that mimicked the

stratum corneum. The brick and mortar

model was is usually employed to model this

skin layer because of the resemblance [4].

When observed under the microscope, the

stratum corneum has many layers of skin

cells stack atop of each other, staggered in

various ways [5][6]. The brick and mortar

model created a similar structure, where the

bricks represented the skin cell while the

mortar represented the lipid layer in between

the skin cells.

To model the movement of the drug

within each layer, we used the lattice

Boltzmann method. The Lattice Boltzmann

Method (LBM) was developed relatively

recently as a mesoscopic method that could

bring together some properties from both

macroscopic and mesoscopic modeling

techniques [7,8,9,10]. This method was often

used to solve incompressible, time dependent

Navier Stokes equations. The advantage was

that it can simulate complex systems such as

multiphase flows and chemical reactions

between different fluids and their

surroundings. Instead of focusing on the

microscopic behavior of the molecules, the

LBM acted on the averaged microscopic

motion. Using the lattice Boltzmann method

and the brick and mortar model, a simulation

can be created to predict the drug flow.

Previous works have already created a

simulation using both lattice Boltzmann and

the brick and mortar model [11,12]. The

model created by Sun Ning only accounted

for the diffusion of the drug through the lipid

layer. Drug flow through the cell layer was

not computed by the simulation. Therefore,

this model was not completely accurate since

it does not represent the whole system.

In our simulation, the drug will be

allowed to flow through both lipid and cell

layer, while also including the diffusion in

between those layers. The data collected from

our simulation will be compared to the data

from the simulation that only includes the

lipid layer. The overall drug flow into the

stratum corneum should increase when the

drug is allowed to diffuse through the cell and

lipid layer as opposed to when the drug is

only allowed to diffuse through the lipid

layer.

2. Methods/Procedure Palabos is an open source

computation fluid dynamics (CFD) solver

that has a heavy reliance on the lattice

Boltzmann methods. Palabos deals with a

large range of topics, some of which are flow

through a porous media, and multi-phase

flow. By having references to alleviate

complex calculations Palabos is the perfect

software to help model a transdermal patch

diffusion.

We created two lattices to help

simulate the drug diffusion through the cell

and lipid layer. These layers are effectively

mirror images of each other. The first lattice

is the cell lattice, where the cells, represented

by brick-like shapes, are given a basic

dynamic for which the drug is allowed to

travel through. Most of the lipid pathways in

this lattice is set to have no dynamics,

meaning the simulation will ignore any

particles in this range and not compute them.

The lipid layer that immediately surrounding

the brick is given a bounce back dynamic.

From this setup, drugs in the cell will stay in

the cell. If they were to travel to the edge of

52

the cell, they will hit a bounce back layer that

will keep it in the cell and away from the lipid

layer.

Figure 2. Location of the types of dynamics

located in each lattice

The lipid lattice is similar except that

the lipid is given the basic dynamic and most

of the cell layer is given no dynamic. The

edge of the brick layer, however, is also

treated as a lipid layer and given normal

dynamics. The cell layer directly under this

edge layer is set to have a bounce back

dynamic. From this setup, the drug in the

lipid layer will stay in the lipid layer, but is

also allowed to transfer into the edge of the

brick layer. This edge is utilized as an

interface which will be used to model the

transport of drugs from lipid to cell layer.

From the edge of the brick layer, the drug will

not be able to transverse any deeper into the

cell since it will be immediately met with the

bounce back dynamic that is given to the

layer immediately under this edge.

The creation of two lattices is optimal

because it breaks a complex problem into

smaller problems, which are significantly

easier to solve. By separating the two lattices

with their respective conditions,

understanding the formation of the model and

managing each of its subparts becomes much

easier.

The resultant interface layer was

created from the two lattices. In both lattices,

the edge of the brick layer is given regular

dynamics. Because of this, a drug density will

exist in both lattices. All the points on the

model where the drug density exist for both

the cell and lipid lattice was regarded as an

interface layer. This interface layer allowed

the transport of drugs from the cell lattice to

the lipid lattice and vice versa. Using this

method, we modeled the transdermal drug

diffusion with two lattices that solely

computed the diffusion through their

respective layer, and an interface processor

that modeled the drug diffusion between

them.

Figure 3: Interface Layout. Interface layer is

shown in black.

A sensitivity test was performed on

the new model to determine which factors

affected the drug flux the most. Three

parameters were tested: diffusion coefficient

of drug through the lipid layer, diffusion

coefficient of drug through the cell layer, and

the partition coefficient of the drug between

the lipid and cell layer. In our simulation we

used coefficients that simulated the drug

fentanyl. A standard control case utilized a

lipid and cell coefficient of 1.2 * 10-7 cm2/s

[13]. The partition coefficient used for the

base case was 0.14. The partition coefficient

controls the amount of drug that travels

between the lipid and cell layers. The lipid

diffusion coefficient and partition coefficient

are the same values used in Sun’s Thesis [11].

We used the same diffusion coefficient for

the cell and lipid pathway for this sensitivity

test.

The total drug amount that was

measured in the patch and SC is recorded in

equivalent lattice units (ELU). This unit

53

represents the total amount of individual

lattices units in the simulation that contains

the drug. It is difficult to translate this drug

amount into physical units. This data was

calculated with the following equation:

𝐸𝐿𝑈𝑝𝑎𝑡𝑐ℎ = 𝜌𝑝𝑎𝑡𝑐ℎ ∗ 𝑊𝑝𝑎𝑡𝑐ℎ ∗ 𝐻𝑝𝑎𝑡𝑐ℎ (1)

𝐸𝐿𝑈𝑆𝐶 = 𝜌𝑆𝐶 ∗ 𝑊𝑆𝐶 ∗ 𝐻𝑆𝐶 (2)

In the above equations, ρ represents

the average drug density in the structure. W

and H represents the width and height

respectively of the structure. Together, they

represent the total area in which the ELU is

being evaluated over.

3. Results

Figure 4a, 4b and 4c showed the

effect fentanyl diffusing through the model at

different cell diffusion coefficients.

The cell diffusion coefficient

variation did not change the diffusion

through the patch but had a difference in the

amount of drug diffused through the SC

layer. At a higher cell diffusion coefficient,

the drug traveled through the system much

quicker. Because of this, the SC contained a

much lower overall concentration of the drug.

The cumulative flux of the patch did

not correlate very well with the cell diffusion

coefficient. A max peak was observed at a

two times base case, but the other cases

showed no changes. More cases needed to be

simulated regarding cell diffusion coefficient

on the overall flux before a conclusion can be

determined.

In Figure 5a, 5b, and 5c, the lipid

diffusion coefficient was another parameter

tested in the simulation.

Figure 4a. Drug distribution in the patch with

different cell coefficients

Figure 4b. Drug distribution in the SC with

different cell coefficients

Figure 4c. Flux for different cell coefficients

54

Figure 5a. Drug distribution in the patch with

different lipid coefficients

Figure 5b. Drug distribution in the SC with

different lipid coefficients

Figure 5c. Flux for different lipid coefficients

Similar to the cell coefficient test, the

patch concentration was largely unaffected

by lipid coefficients variations. An

increasing lipid coefficient decreased the

total drug amount in the SC, which was the

exact same trend observed in the cell

coefficient test. The variation of the lipid

coefficient however did not have as great of

an effect on the total drug in the SC as did the

variation in the cell coefficient. This may be

because the SC is mostly made up of cell

layer, so a change in the lesser lipid layer

would have an overall lesser effect.

The cumulative overall flux of the

drug decreased as the lipid diffusion

coefficient increased. It did follow the same

overall pattern of spiking and a resultant

gradual decrease, but the peak of this spike

seems to be controlled by the lipid

coefficient. The highest peak occurred when

the coefficient was the smallest and

decreased as the coefficient is increased.

Figure 6a, 6b, and 6c showed the

effects of varying the partition coefficient.

Figure 6a. Drug distribution in the patch with

different partition coefficients

Figure 6b. Drug distribution in the SC with

different partition coefficients

55

Figure 6c. Flux for different partition

coefficients

A higher partition coefficient should

allow the drugs to more easily move between

the lipid and cell layers. This will then free

more room in the SC for the drug to diffuse

into from the patch. Unlike the previous two

tests, varying the partition coefficient seemed

to have more of an effect on the drug amount

in the patch.

Similar to the lipid coefficient test,

the overall drug amount in the SC showed

little change when the partition coefficient

was varied. This was an interesting result

since the drug amount in the patch showed

some variation.

A lower partition coefficient caused

the drug to diffuse out the patch more slowly.

Also, a lower partition coefficient caused the

drug amount to peak at a lower value than the

other base conditions. This was apparent

since a lower partition coefficient did not

allow as much drug to diffuse to and from the

lipid and cell and vise-versa.

Cumulative overall flux and partition

coefficient seemed to have a positive

correlation. Similar to the analysis of

cumulative overall flux and lipid partition

coefficient, the overall flux followed the

same general pattern with a sharp peak and a

gradual decline. Unlike the negative

correlation observed in 5c however, an

increase in partition coefficient resulted in an

increase in the peak of the cumulative overall

flux.

4. Discussion

The human SC is primarily made up

of cells, which is also true in our model. The

“bricks” make up a large portion of our

structure. The lipid layer only existed in the

small surroundings between each cell.

Because of this, the cell layer dynamics will

probably cause the most drastic changes in

the results. Variation of the lipid coefficient

in figure 5a and 5b shows that the lipid

coefficient have minimal effects on the total

drug amount in SC and the patch. As seen in

figure 6a and 6b, the partition coefficient also

has minimal changes in the outcome. Since

the partition coefficient controlled the

movement of drugs between lipid and cell

layer, it should have more effect on the total

drug amount than the lipid coefficient, but

less effect than the cell coefficient. Figure 4b

showed that the cell coefficient did indeed

affect the total drug amount in SC by much

more than the lipid coefficient and the

partition coefficient. As a result, the amount

of drug present on the SC was largely

dependent on the diffusion of drugs through

the cell layers in the SC.

The flux of the drugs however, seems

to follow a different trend. According to the

figure 5c, the lipid diffusion coefficient had

the most effect on the flux. The highest lipid

diffusion coefficient resulted in the lowest

peak flux while the lowest coefficient

resulted in the highest peak. With a high lipid

diffusion coefficient, the drug is able to pass

through the lipid layer in much faster. This

gives the drug less time to diffuse into the cell

layer. The diffusion of the drug through the

cell layer is predicted to be the main pathway

for which the drug diffuses. Because of this,

a high lipid diffusion coefficient can actually

be detrimental to the flux because it decreases

the amount of drug that can enter this main

diffusion pathway, the cell layer. The

partition coefficient in figure 6c showed

negligible changes in flux for the range that

we tested. The cell diffusion coefficient data

in figure 4c remains inconclusive. There was

56

no general pattern that can be observed. The

base case, 0.5x base case, and 4x base case all

have the same peak height, but the the 2x base

case observed a sudden 50% jump in peak

height. This could be an optimal point where

the lipid diffusion coefficient and cell

diffusion coefficient may experience some

resonance effect resulting in more flux.

Further testing will need to be done with

these parameters.

Ning Sun’s model of transdermal

drug diffusion only accounted for the

diffusion of drug through the lipid layer. His

results showed that the cumulative flux

increases sharply, then steadily drops off.

Our results in figure 4c, 5c, and 6c all showed

a similar trend observed in Sun’s results [2].

Our results were similar in the sense that we

also have a large initial spike in flux. Adding

a cell layer however, seems to have given our

results a less steep increase when compared

to Sun’s results. The overall flux after this

peak also decreased more rapidly than Sun’s

results. Sun’s results showed the overall flux

to have a steady linear decrease after the

spike while our results in figure 4c, 5c, and

6c all showed a decrease that's more

comparative to an exponential decay.

The cumulative overall flux was

observed to have a positive correlation with

partition coefficient and a negative

correlation with lipid diffusion coefficient.

The lipid diffusion coefficient and

cumulative overall flux’s negative

correlation made sense when looking at the

data in 5b. The highest flux peak correlated

with the highest drug concentration in the SC.

Higher drug concentration in the SC will lead

to a higher flux since there was a greater drug

concentration that would want to diffuse out

of the SC. This pattern should ideally also be

observed in the sensitivity test for cell

coefficient, but that data is inconclusive as is.

Similarly, the partition coefficient

and cumulative overall flux’s positive

correlation agreed with the data in figure 6b.

The highest cumulative overall flux was the

four times base case, which is also where the

SC observed the highest peak in drug

concentration. All these cases followed the

same peak height order for concentration in

SC and cumulative overall flux. A higher

partition coefficient will allow more drugs to

pass from layer to layer. A higher transfer in

between these layers will allow the drug to

spend less time in the SC and therefore exit

the SC in greater numbers. This will speed up

the rate of drug transfer which can explain the

positive correlation observed between the

cumulative overall flux and the partition

coefficient.

5. Conclusion

By adding the cell layer into the

transdermal patch simulation, the resultant

drug in SC, patch, and accumulative flux

changed drastically. Diffusion of drugs

through the lipid layer only seemed to lead to

a steady and slow flushing of the drug from

the SC. The addition of the cell layer

increased this rate dramatically. Ultimately,

the addition of the cell layer in the

transdermal patch sped up the diffusion of

drugs through the system and lead to a steeper

decreases in overall cumulative flux. Our

various sensitivity tests have concluded that

the cell layer did indeed play the largest role

in the diffusion of the drugs. Variation in the

partition coefficient and the lipid coefficient

showed negligible changes when compared

to variations in the cell coefficient. The

diffusion of drugs from a transdermal patch

and through the stratum corneum was

ultimately most dependent on the cell

diffusion coefficient and least dependent on

the lipid coefficient and partition coefficient.

Acknowledgements

This research was supported Stony

Brook University. We would like to thank

Dr. Dilip Gersappe for providing us

mentorship throughout the project. We

57

would also like to thank our graduate

advisor, Jialong Jiang, for guiding us

through the simulation process. We would

also like to thank Miriam Rafailovich and

Clement Marmorat for guiding us through

the research.

References

[1] Rim, J. E.; Pinsky, P. M.; van Osdol, W.

W; Multiscale Modeling Framework of

Transdermal Drug Delivery. Annals of

Biomedical Engineering. 2009, 37, 1217 -

1229

[2] Zhang, C.; Luo H.; Lin, G.; Zhu, Z.;

Zhang, F.; Zhang, J.; Wu, Y.; Luo, J.; Wang,

H.; Transdermal patches for D-threo-

methylphenidate (free base): Formulation

aspects and in vivo pharmacokinetics.

Journal of Drug Delivery Science and

Technology, 2016, 35, 50-57.

[3] Wei, W.; Quan, P.; Liu, C.; Zhao, H.;

Fang, L.; Design of a Drug-in-Adhesive

Transdermal Patch for Risperidone: Effect

of Drug-Additive Interactions on the

Crystallization Inhibition and In  Vitro/In

 Vivo Correlation Study. Journal of

Pharmaceutical Sciences, 2016, 105, 3153-

3161.

[4] Heisig, M.; Lieckfeldt, R.; Wittum, G.;

Mazurkevich, G.; Lee, G.; Non Steady-state

Description of Drug Permeation Through

Stratum Corneum. I. The Biphasic Brick-

and-Mortar Model. Pharmaceutical

Research, 1996, 13, 421-426.

[5] Elias, P. M.; Structure and Function of

the Stratum Corneum Permeability Barrier.

Drug Development Research, 1988, 13, 97-

105.

[6] Harding, C.; The stratum corneum:

structure and function in health and disease.

Dermatologic Therapy, 2004, 17, 6-15.

[7] Sukop, Michael C.; Daniel T. Thorne,

Jr.; Lattice Boltzmann Modeling. An

Introduction for Geoscientists and

Engineers. N.p.: Springer, n.d. Print.

[8]Frapolli, N.; Chikatamarla, S. S.; Karlin,

S. S.; Entropic Lattice Boltzmann Model for

Compressible Flows. Physical Review E,

2015, 92, 1-5.

[9] Amir, B.; Mauzole, Y.; Hara, T.; Grilli,

S. T.; Janben C. F.; The Simulation of

Turbulent Particle-laden Channel Flow by

the Lattice Boltzmann Method. International

Journal for Numerical Methods in Fluids,

2015, 79, 491-513.

[10] He, X.,; Luo, L.S.; Theory of the lattice

Boltzmann method: From the Boltzmann

equation to the lattice Boltzmann equation.

Physical Review E, 1997, 56, 6811-6817

[11] Sun, Ning.; Applications of Lattice

Boltzmann Methods in Complex Mass

Transfer Systems. 2016 (Doctoral

Dissertation)

[12] Chen, L.; Lian, G.; Han, L.; Use of

Bricks and Mortar Model To Predict

Transdermal Permeation: Model

Development and Initial Validation.

Industrial & Engineering Chemistry

Research, 2008, 47, 6465-6472.

[13] Roy, S. D.; Gutierrez, M..; Flynn, G.L.;

Cleary, G. W.; Controlled Transdermal

Delivery of Fentanyl: Characterizations of

Pressure-Sensitive Adhesives for Matrix

Patch Design. Journal of Pharmaceutical

Sciences, 1996, 85, 491-495.

58

Effect of Anhydrous Sodium Sulfate Salt Loading on Aqueous

Furfuryl Alcohol Washes Extracted from Furfuryl Alcohol

Oligomers using Deionized Water

Patrick Yang, Carmenn Ooi, Marolyn Liang, Jeong Suk Byun, Xiaojun Chan,

Taejin Kim

Materials Science and Chemical Engineering Department, Stony Brook University, Stony Brook,

NY, 11794, U.S.A

Abstract Unreacted furfuryl alcohol (FA) monomers were successfully extracted from FA oligomers and

polymers through a liquid-liquid extraction coupled with the salting-out effect. Over three liquid-

liquid extractions, the initial concentration of FA at 65.1 wt.% and decreased to 21.0 wt.%,

showing net extraction of 44.1 wt.% FA out of the organic solution. The recovery and

enrichment of unreacted monomers is vital in reducing the total costs of chemicals as well as

generated wastes. Using deionized (D.I.) water as a green solvent, polar FA was extracted from

mostly nonpolar FA oligomers and polymers using polar D.I. water in a liquid-liquid extraction.

FA oligomers were enriched from 18.1 wt.% to ~ 46.6 wt.% while FA polymers decreased from

16.8 wt.% to 32.4 wt.%. The FA monomers were then extracted from aqueous solutions by the

salting-out effect. The addition of anhydrous sodium sulfate (Na2SO4) was investigated for

reaction time effect and salt loading on the aqueous wash by the salting-out process.

Graphical Abstract:

59

Keywords: Furfuryl Alcohol, Salting-out Assisted Liquid-Liquid Extraction (SALLE),

Anhydrous Sodium Sulfate, UV-Vis Spectroscopy

1. Introduction

Due to increasing energy

consumption and depletion of fossil fuels, it

is important to utilize renewable resources

for future energy. Biomass conversion is one

of promising pathways to develop renewable

energy. Among all possible sources of

biomass, lignocellulosic biomass, or second

generational biomass currently has the most

interest. First generation biomass includes

edible crops (e.g., corn and sugarcane) to

produce ethanol through a fermentation

process, while lignocellulosic biomass

conversion into fuels or chemicals does not

directly compete with food production. The

abundance of lignocellulosic biomass is

another drive for the development. It was

reported that the annual consumption of

fossil fuels can be fulfilled by 5 - 8% of

annual production of lignocellulosic biomass

[1].

Among all the platform chemicals

that are derived from lignocellulose, furfuryl

alcohol (FA) draws the most attention due to

its wide application in many other useful

chemical products, such as FA oligomers

(OFA) and FA polymers (PFA). Oligomers

or polymers can be synthesized through

dehydration and condensation reactions of

FA.2 Due to their strong heat stability, low

flammability, high acidity and basicity

resistance, PFA is widely applied in the

synthesis of polymer nanocomposites which

can be further applied as adsorbents,

membranes, catalysts, electrodes in fuel cells,

lithium batteries, and electric double-layer

capacitors [2-3].

Traditionally, liquid acid catalysts

such as sulfuric acid and phosphoric acid

have been used in FA oligomerization and

polymerization reactions [4-7]. Even though

homogeneous acid catalysts provide higher

conversion rates, heterogeneous catalysts

(e.g., metal oxides and zeolites) are

preferable due to its ease in catalyst

separation and then recycling [8]. Selectivity

of OFA and PFA can be controlled by

varying reaction time and temperature during

the acidic catalyzed FA condensation

reaction [9]. From the economic and

environment (chemical waste reduction)

points of view, unreacted furfuryl alcohol

should be separated from the products and

reused for OFA or PFA productions.

Compared to other separation

techniques such as membrane separation and

distillation, liquid-liquid extraction is proved

to have reduced extraction time, lower cost

on disposal and reagent purchases on

separation of desired solutes from solution

[10]. Due to the polarity characteristic, it can

be hypothesized that water will only react

with FA molecules and extract them from the

non-polar OFA/PFA solutions. Furthermore,

by using salting-out method, FA monomer is

separated from the aqueous solutions. The

interaction of salt ions with water molecules

causes water molecules unavailable to react

or bind to FA which is later separated into a

different phase [11]. The aim of this work

was to use the salting-out assisted liquid-

liquid extraction in the separation of FA from

OFA/PFA solution. By Hofmeister series, the

extractability of salts increased in the

sequence: SO42- ≈ CO3

2- > CH3COO- ≈ Cl-

[12]. Hofmeister series states that sulfate

anion has a relatively high extractability

compared to other anions. Therefore,

anhydrous sodium sulfate (Na2SO4) was

chosen in this study.

2. Experimental Section

2.1 Materials

Furfuryl alcohol (FA, 98%), sodium

sulfate (anhydrous, ≥99.5%) and methanol

(99%) were obtained from Sigma-Aldrich

60

and used without further purification for the

oligomerization reaction, salting out

experiment, and gas chromatography

respectively. Deionized (D.I.) water was

obtained from Thermo Scientific and was

used for the liquid-liquid extraction.

2.2 Methods

2.2.1 Furfuryl Alcohol Oligomerization

FA oligomers (OFA) were

synthesized over 1.2 g of molybdenum oxide

(MoO3) with 3.00 g of FA at 100oC for six h

and ambient pressure. The oligomerization

process was conducted in the 7 mL clear

glass vials placed within an aluminum block

designed to hold these glass vials which was

covered by heating tape (HTS/Amptek

1.30A, 156W). After six hours, the vials were

immediately transported into ice water and

held for five minutes to stop any further

reactions. To separate the oligomer solution,

which includes unreacted FA monomer, from

the solid catalyst, the vials were centrifuged

for five minutes at 4000 rpm and then the

solution was carefully pipetted off the top.

Furfuryl alcohol oligomers were then stored

in the refrigerator for further use.

2.2.2 Liquid-liquid Extraction

Liquid-liquid extraction of unreacted

FA monomers from its oligomers and

polymers was conducted using D.I. water as a

solvent. A constant 2:1 ratio by volume of

D.I. water to oligomerized solution was

applied during each liquid-liquid extraction.

Plastic centrifuge test tubes 15 mL)) with

built in plastic lids were used to conduct

liquid-liquid extraction. As an example, FA

oligomers were added to the plastic

centrifuge tube to the 4 mL mark and then

topped with 8 mL of D.I. water, until a total

volume of 12 mL was reached. The

oligomerized FA solution and D.I. water was

fully mixed by vigorous shaking for ten

minutes before being transported to a

centrifuge (UNIX Powerspin LX Centrifuge).

To counterbalance the centrifuge, another

centrifuge tube containing 12 mL of water

was used. The solution was then centrifuged

at 4000 rpm for five minutes to fully separate

the solution into two layers: an aqueous (top)

and an organic (bottom) solution. The

separated aqueous layers from each

extraction were then stored in separate large

glass bottle for use during the salting-out

separation. Remaining oligomer solution was

again washed with D.I. water and the

aqueous top layers were separated using the

same method described before. This liquid-

liquid extraction step was repeated three

times. Remaining oligomerized solution was

stored in the refrigerator for further

characterization.

2.2.3 Salting-out loading effect

The loading of anhydrous sodium

sulfate salt was varied from 0.45 g to 0.70 g

at room temperature with 4.00 g of the

aqueous wash solution that was separated

during the liquid-liquid extraction process.

To avoid salt agglomeration, the FA aqueous

solution was immediately mixed with sodium

sulfate at the beginning of the salting-out

process. After the salt had fully dissolved, the

solution was allowed to stand so that the

solution reached equilibrium. The organic

layer that is salted-out at equilibrium either

forms on the bottom of the vial or at the top

of the aqueous solution depending on salt

loading.

2.3 Analytical Methods

2.3.1 Gas Chromatography

Gas Chromatography/Mass Spectrum

(GC/MS) was used to identify the FA

oligomer composition. Tests were conducted

using the Perkin Elmer Clarus 680 GC/MS

equipped with a Perkin Elmer SQ8T mass

detector. The injector temperature of the

GC/MS was 250 °C and the split ratio 30:1.

Gas Chromatography (GC) was conducted

using a Perkin Elmer Clarus 680 equipped

61

with a flame ionization detector with

split/splitless injector. The GC has an Elite-

5MS capillary column (30 m x 0.25 mm x 1.0

um) with helium as the carrier gas. GC/FID

coupled with the GC/MS data can help

quantify the percent composition of the

oligomerized solution made up of unreacted

FA monomers and FA short chain oligomers

and long chain polymers.

2.3.2 UV-Visible Spectroscopy

Qualitative analysis of FA remaining

in the aqueous solution after the salting-out

process was performed using by Ultraviolet-

Visible (UV-Vis) Spectrometer (Evolution

220 UV-Vis Spectrophotometer, Thermo

Scientific). The UV-Vis spectrum was

obtained over the range of 200 - 800 nm

wavelength with scanning intervals of 1 nm.

Samples were prepared and tested within a

disposable semi-micro UV-cuvette

(BrandTech Scientific Inc) of dimensions

12.5 mm x 12.5 mm x 45 mm.

3. Results and Discussion

3.1 Extraction of FA from Oligomer Stock

Solution

Figure 1 shows total volume of the

organic layer decreasing as the number of

liquid-liquid extractions completed increased.

After three washes, the volume of organic

layer decreases from 3.8 ± 0.1 mL to 1.8 ±

0.1 mL. A decrease in the total volume of the

organic layer is due to interaction between

D.I. water and the hydroxyl group (-OH) of

FA. The polar end of FA allows movement of

FA molecules from the organic phase to the

also polar aqueous one, creating a partition of

FA between both layers. Apart from FA

oligomers that contain a terminal hydroxyl

group, the rest of the oligomers and long

chain polymers that are mostly nonpolar will

not have a high partition into the aqueous

layer and remain in the organic layer [13].

Figure 1. Total volume of the organic phase after

three liquid-liquid extractions using D.I. water.

Using gas chromatography coupled

with mass spectroscopy (GC/MS), a total of

five dimers and two trimers of furfuryl

alcohol were observed in the oligomerized

solution. The five FA dimers that were

observed were 2,2’-Difurylmethane, 2-(2-

furylmethyl)-5-methylfuran, Difurfuryl ether,

4-Furfuryl-2-pentenoic acid γ-lactone, 5-

Fufuryl-Furfuryl Alcohol, and two FA

trimers observed are 2,5-Difurfurylfuran and

2,2’-(Furylmethylene) bis(5-methylfuran).

The data obtained from GC/MS provides

helpful information in percent composition of

the solution through the identification of

corresponding retention time peaks of FA

and its dimers and trimers in the GC/FID

analysis.

Through the combination of data

collected from both GC/MS and GC/FID, the

composition of initial and final oligomer

solutions were determined as shown in

Figure 2. After a total of three liquid-liquid

D.I. water extraction washes, the

concentration of FA shows a decrease from

62

65.1 wt.% initially to 21.0 wt.%. This

indicates that D.I. water can be used

effectively as a polar solvent to extract water

soluble/polar unreacted FA monomers from

water-insoluble non-polar FA oligomers and

long chain polymers. We also observed that

the concentration of five dimers and two

trimers of FA increased from 18.1 wt.% to

46.6 wt.% while long chain FA polymers

increased from 16.8 wt.% to 32.4 wt.%

demonstrating that the FA was successfully

extracted from its oligomer and polymer

products.

Figure 2. A comparison of percent composition

of initial and final FA oligomerized mixtures,

including compositions of FA, FA dimers &

trimers, and FA long chain polymers.

3.2 Extraction of FA from Aqueous Solution

The reaction time effect for the

salting out effect was qualitatively

investigated and is shown in Figure 3 (a-f).

Wannachod et al.9 experimented with the

effect of salt on liquid-liquid extraction,

looking at the tie-line data for a mixture of

furfuryl alcohol, n-butanol, and water at

298.15 K. The authors observed that with

increasing sodium chloride (NaCl)

concentration in the system, the salt proved

to be beneficial in increasing the separation

of FA from water. In hydration theory, salt

ions bind with water molecules leading to a

decrease in the solubility of FA in water

leading to higher separation of FA in water.

Figure 3 (a-b) shows salt loadings of

0.45 and 0.50 g respectively, where the

organic layer is observed forming at the

bottom of the solution during the first two

hours. As the reaction time increases from

one minute to two hours, the amount and the

sizes of the sphere of the organic layer

separated from the aqueous washes increases.

The sizes of the spherical organic layer that

formed at bottom of the aqueous layer also

increased with increasing salt loadings.

Based on Figure 3 (c-f), salt loadings of 0.55,

0.60, 0.65, and 0.70 g respectively, the

organic layer is observed forming at the top

of the aqueous layer instead of the bottom.

With higher salt loadings, the aqueous

solutions could reach equilibrium at a much

faster rate.

In Figure 3 (b) with salt loading of

0.50 g, the solution reached equilibrium after

twenty hours. The organic layer turned from

a bunch of small bubbles at the bottom to a

much larger sphere with a few smaller ones

in the middle of the vial. The location where

the organic layer forms changes from the

bottom at a salt loading of 0.45 g to the top

with a 0.55 g loading. With a salt loading of

0.50 g, the organic layer formed

approximately at the middle of the aqueous

63

Figure 3. Addition of anhydrous sodium sulfate loadings from 0.45 g to 0.70 g with 0.05 g increment in

4.00 g aqueous wash over a period of 20 hours at room temperature (from left to right, a to f).

layer. This phenomenon suggests that the

movement of the organic layer, from the

bottom to the top of the aqueous solution, is

due to the increasing density of the aqueous

layer caused by increased salt loading.

The visual difference between the initial

aqueous wash solution and after anhydrous

sodium sulfate is added is presented

quantitatively in Figure 3 (a-f). Initially the

aqueous wash solution was an opaque light

yellow color, that yellow color being

attributed to FA and its oligomers/polymers

that were dissolved within the water during

the liquid-liquid extraction. However, the

addition of salt increases the transparency of

aqueous washes over time as the salting-out

effect occurs. The transition of an opaque

light yellow color of the batch aqueous wash

to a transparent aqueous layer as the reaction

time increases indicates achievement of

equilibrium in the system. The same

phenomenon could be observed when the salt

loading in the aqueous wash solution

increased. Higher salt loadings shortened the

total time needed to achieve equilibrium in

the solution. The salt solution reaches

saturation somewhere between a salt loading

of 0.65 to 0.70 g which is indicated by the

formation of salt at the bottom of the vial in

the 0.70 g sample.

64

Figure 4. UV-Vis spectra of aqueous washes with increasing salt (anhydrous sodium sulfate) loadings of

0.45 g to 0.70 g . For reference, a spectrum without any salt loading (0.00 g) is also shown.

3.3 Purity and Quantity of Extracted FA

with Increasing Salt Loading

Shown in Figure 4 are a series of

UV-Vis spectra collected between 300-800

nm of various aqueous wash solutions with

increasing sodium sulfate salt loadings. The

UV-Vis spectrum for the original aqueous

wash before any salt was added (0.00 g)

shows a spectrum that is much more shifted

to the right than any of the other samples

with salt loadings. On further inspection of

the data between wavelength ranges of 345 -

360 nm, the spectrum for the aqueous wash

shifts towards the left with increasing salt

loadings. This shift shows the trend that as

the concentration of FA in solution

decreases, the absorbance at a given

wavelength also decreases. As the

salt loading increases from 0.45 to 0.70 g,

the remaining quantity of organic FA

oligomer solution containing FA that was

dissolved in the aqueous washes decreases

which is indicated with the shift in spectra

towards the left. The spectrum shift is

relatively constant as the salt loading

increases with the exception of 0.50 - 0.55 g

when the FA layer moves from the bottom

to the top.

To make sure that the addition of

anhydrous sodium sulfate salt loadings did

not have any effect on the UV-Vis spectra

collected, a sample of pure D.I. water and

two other salt solutions samples were

prepared and tested to compare the

absorbance of the solutions using UV-Vis.

The two salt solutions were prepared by

dissolving 0.45 and 0.70 g of anhydrous

sodium sulfate in 4.00 g of D.I. water, the

same salt loading to the amount of liquid

used for the salting out of the aqueous wash

layers. There is no noticeable difference in

the spectrum between all three samples.

There is almost no absorbance detected for

the samples with salt dissolved inside. This

data suggests that the salt loading has no

effect on the final aqueous wash UV-Vis

spectrums collected. Based on Figure 4, a

higher salt loading decreases the amount of

the FA that is remaining within the aqueous

wash solution. This relationship suggests

that with increasing salt loading, more FA

was extracted and removed from the

aqueous wash. Therefore, increasing salt

loading is shown to help to enhance the

extraction of FA and other organic

compounds from the aqueous wash solution.

65

4. Conclusions

Unreacted FA monomer remaining

from the molybdenum oxide catalyzed

oligomerization reaction forming OFA and

PFA products was separated using a two-

step procedure: liquid-liquid extraction

followed by salting-out process. As the

number of extraction increased, the volume

of organic layer decreased which indicates

that FA monomer can be separated from the

organic solutions by D.I. water. The FA

concentration in the organic layer decreased

from 65.1 wt.% to 21.0 wt.% after liquid-

liquid extraction. Consequently, unreacted

FA monomers were separated from the

aqueous solutions using anhydrous sodium

sulfate for the salting-out effect. As salting-

out time and salt loading increased, the FA

collection efficiency increased. In addition

to the quantity of extracted FA, a higher salt

loading shortens the time needed to achieve

FA and D.I. water separation equilibrium.

The movement of the eluting unreacted FA

layer was observed to be forming in

different locations with different salt loading

which indicates a different density of salt

solutions. The UV-Vis spectroscopy

provided proof of the presence of unreacted

FA monomer extracted in the aqueous salt

solution. The qualitative data which

indicates the decreasing FA in the aqueous

layer with increasing salt loading was

provided and shown by the shifting of UV-

Vis spectrum towards the left. This

qualitative data shows that the amount of FA

being extracted is increasing with higher salt

loadings.

Acknowledgements

We gratefully acknowledge the financial

support from the Department of Materials

Science and Chemical Engineering at Stony

Brook University. We would also like to

thank the Advanced Energy Research and

Technology Center (AERTC) at the Stony

Brook University for providing us with the

facilities. We would especially like to thank

Dr. Miriam Rafailovich and Clement

Marmorat for all their help and guidance of

senior thesis course.

References

[1] Elgharbawy, A. A.; Alam, M. Z.;

Moniruzzaman, M.; Goto, M. Ionic liquid

pretreatment as emerging approaches for

enhanced enzymatic hydrolysis of

lignocellulosic biomass. Biochemical

Engineering Journal 2016, 109, DOI

10.1016/j.bej.2016.01.021.

[2] Wang, H.; Yao, J. Use of poly (furfuryl

alcohol) in the fabrication of nanostructured

carbons and nanocomposites. Industrial &

Engineering Chemistry Research 2006, 45

(19), DOI 10.1021/ie0602660.

[3] Marefat Seyedlar, R.; Imani, M.;

Mirabedini, S. M. Curing of poly (furfuryl

alcohol) resin catalyzed by a homologous

series of dicarboxylic acid catalysts:

Kinetics and pot life. Journal of Applied

Polymer Science 2016, 133 (43), DOI

10.1002/app.44009.

[4] Conley, R. T.; Metil, I. An investigation

of the structure of furfuryl alcohol

polycondensates with infrared spectroscopy.

Journal of Applied Polymer Science 1963, 7

(1), DOI 10.1002/app.1963.070070104.

[5] He, L.; Li, D.; Dong, D.; Yao, J.; Huang,

Y.; Wang, H. Effects of polymerization

conditions on the properties of poly (furfuryl

alcohol) composite membranes. Journal of

Applied Polymer Science 2012, 124 (4),

DOI 10.1002/app.35356.

[6] Chan, X.; Roy, A.; Ooi, C.; Yang, P.;

Morais, F.; Kim, T. Catalysts Loading Effect

of Tungsten Oxide Catalytic Furfuryl

Alcohol Oligomerization. Materials Today:

Proceedings 2016, 3, DOI

10.1016/j.matpr.2016.01.106.

[7] Kim, K.; Jeong, J.; Rahman, M.; Zhu, E.;

Mahajan, D. Characterizations of furfuryl

alcohol oligomer/ polymerization catalyzed

66

by homogeneous and heterogeneous acid

catalysts. Korean Journal of Chemical

Engineering 2014, 31 (12), DOI

10.1007/s11814-014-0322-x.

[8] Burket, C.L.; Rajagopalan, R.; Marencic,

A. P.; Dronvajjala, K.; Foley, H. C. Genesis

of porosity in polyfurfuryl alcohol derived

nanoporous carbon. Carbon 2006, 44 (14),

DOI: 10.1016/j.carbon.2006.05.029.

[9] Chan, X.; Nan, W.; Mahajan, D.; Kim,

T. Comprehensive investigation of the

biomass derived furfuryl alcohol oligomer

formation over tungsten oxide catalysts.

Catalysis Communications 2005, 72, DOI:

10.1016/j.catcom.2015.08.027.

[10] Ebrahimzadeh, H.; Yamini, Y.;

Kamarei, F.; Shariati, S. Homogeneous

liquid–liquid extraction of trace amounts of

mononitrotoluenes from waste water

samples. Analytica Chimica Acta 2007, 594

(1), DOI: 10.1016/j.aca.2007.05.013.

[11] Wannachod, T.; Hronec, M.; Soták, T.;

Fulajtárová, K.; Pancharoen, U.;

Arpornwichanop, A. Effects of salt on the

LLE and tie-line data for furfuryl alcohol—

n-butanol–water at T= 298.15 K. Journal of

Molecular Liquids 2016, 218, DOI:

10.1007/s10953-017-0619-6.

[12] Valente, I. M.; Gonclaves, L. M.;

Rodrigues, J. A. Another glimpse over the

salting-out assisted liquid-liquid extraction

in acetonitrile/water mixtures. Journal of

Chromatography A 2013, 1308, DOI:

10.1016/j.chroma.2013.08.014.

[13] Chan, X.; Yang, P.; Ooi, C.; Cen, J.

Orlov, A.; Kim, T. ACS Sustainable

Chemistry & Engineering 2016, 4 (8), DOI:

10.1021/acssuschemeng.6b01067.

Pages 67-77 currently unavailable

78

Thermal Conductivity, Electrical Conductivity and

Mechanical Properties of Polypropylene/Graphene and

Polystyrene/ Graphene Nanocomposites Peter Ryzyk1, Thomas Hurson1, Joo Yong Yi1, Joshua Weinstein1,

Miriam Rafailovich1, Xianghao Zuo1, Vladimir Samuilov 2 1Chemical and Molecular Engineering Department, Stony Brook University, Room 314 Old

Engineering, Stony Brook, New York 11794, United States 2Materials and Science Engineering Department, Stony Brook University, Room 314 Old

Engineering, Stony Brook, New York 11794, United States

Abstract

In recent years, many studies have been conducted regarding polymer

nanocomposites, namely polymers doped with various forms of graphene. The impact

toughness, tensile strength, and thermal conductivity of Polypropylene/ Graphene and

Polystyrene/ Graphene nanocomposites were compared at different Graphene concentrations

varying from 5% to 25%. The electrical conductivity of Polypropylene/ Graphene

nanocomposites of the same composition was also determined. The goal of these tests was to

find an optimal composition that enhances the electrical and thermal conductivity as well as

tensile strength of polypropylene without compromising the impact toughness for the

potential application of a battery anode. At 10% loading of GNP, an over 100% increase in

thermal conductivity and a 60% increase in tensile strength were achieved with only a 4%

loss to impact strength. The electrical conductivity increased from 260 S/m at 10% loading of

GNP to 2 * 107 S/m at 15% loading of GNP. The results conclude that the nanocomposites

with a load between 10% GNP and 15% GNP by weight displayed ideal material

characteristics.

1. Introduction

Polymer nanocomposite research

often has many different objectives, but a

common goal of creating materials with

improved properties in any of many

possible categories. Flame retardancy,

electrical and thermal conductivity smoke

suppression, tensile strength, impact

strength, glass transition temperature,

melting point, elasticity, etc; there are

applications where each of these properties

may be deemed more valuable. The

addition of additives to create a

nanocomposite is the most common way

to alter these properties. However, it is not

without drawbacks, and not very

predictable [2]. Thus, experimental

research is necessary.

Usually, the addition of additives

will disrupt at least partially the matrix of

the polymer [1]. This is a good example

because creating disruptions is a good

technique for fire retardancy and smoke

suppression, as the combustion may hit a

very small layer of non-combustible

graphene nanoparticles (GNPs), slowing

combustion [1]. However, this same layer

may be a point of weakness in terms of

impact strength, where it may break along

that layer.

Our research dealt with this issue

among others. The earlier stages of our

project attempted a composite using

Polystyrene (PS). PS is a polymer with a

density of 1.052 g/cm3 and a molecular

weight of the repeat unit of 104.15 g/mol

[15]. The PS did not handle the disruption

acceptably, and the result was a brittle

79

material with few possible useful

applications.

In this research, we originally

proposed to use graphene as a fire

retardant by synthesizing it with

polystyrene into a nanocomposite.

Graphene is a 2-dimensional layer of

carbon that has outstanding properties of

high thermal conductivity, superior

mechanical stiffness, and large specific

surface area [13]. There is previous

research that used functionalized graphene

oxide and obtained improved fire safety

data using a masterbatch-melt blending

approach, but we used pure graphene

nanoparticles in this research. We

expected that addition of high graphene

content into polystyrene can effectively

improve fire safety and mechanical

property of the nanocomposite. To

determine the most effective

graphene/polystyrene concentration ratio,

several samples with different

concentrations were prepared and tested.

Our project goal from the very

beginning was to use GNPs in unusually

high doses in a nanocomposite to improve

properties. Preliminary research suggested

up to 40 wt% GNPs could be added to a

polymer nanocomposite for some

potentially extraordinary properties.

Initial testing with polystyrene

produced a very brittle material, so much

so that it was difficult even to create molds

of for testing. In response, we decided to

try polypropylene (PP) as our principal

polymer. Polypropylene has a low glass

transition temperature which allows it to

act more like a fluid at room temperature,

making it better suited for these kinds of

composites [3]. PP has a density of 0.86

g/cm3 and a molecular weight of the repeat

unit of 42.08 g/mol [15]. PP has a

crystalline structure as opposed to the

amorphous structure of PS. The difference

in the degree of crystallinity attributes to

higher heat resistance and sharper melting

points. Moreover, a highly crystalline

structure is prone to being more brittle due

to the compactness and static organization

of the molecules [16].

The result was immediate

improvement and a promising material.

We then created samples of the same

graphene concentrations as the PS

nanocomposites but conducted with PP,

and were able to compare the two

materials afterwards. We successfully

improved properties in tensile strength and

thermal and electrical conductivities

without noticeable detriment to impact

toughness.

2 Experimental Section

2.1 Materials

XGNP-H-5 Graphene

Nanoplatelets were purchased from XG

Sciences. The nanoplatelets have an

average particle diameter of 5 microns.

Polypropylene pellets were purchased

from Amco Plastic Materials Inc.

Polystyrene pellets were purchased from

Amco Plastic Materials Inc.

Both the PP and PS

nanocomposites were prepared in the

following loadings of GNPs by weight:

0%, 5%, 10%, 15%, 20%, and 25% using a

Brabender mixer filled to its capacity of

200g run at 256 °C for twenty minutes.

The nanocomposites were then pelletized

by hand and molded to meet the various

testing standards using the appropriate

molds and a Carver Hot Press at 256 °C

and compressed to 6000 psi for five

minutes.

2.2 Experimental Methods

2.2.1 IZOD Impact Toughness Test

The nanocomposites were molded

into the appropriate ASTM D256-10e1

standard mold with dimensions 64 x 12.7 x

3.2 mm with a notch in the center and then

tested on a Testing Machines Inc. Monitor

Impact Tester that records the maximum

energy absorbed by the sample in J/m [4].

80

2.2.2 Tensile Strength Test

The nanocomposites were molded

into the appropriate ASTM D638-14

standard dogbone shaped mold and tested

on an Instron 5542 Single Testing Column

System. A plot of force versus stroke is

returned and from that the Young’s

modulus is calculated [5].

2.2.3 Thermal Conductivity Test

Thermal conductivity of the

samples was measured on a Unitherm

Model 2022 Thermal Conductivity

Instrument using the ASTM E1530

Guarded Heat Flow Meter Method. The

thermal conductivity tests were performed

at 25 °C and recorded in W/(m*K) [6].

2.2.4 Electrical Conductivity Test

The Electrical conductivity of the

samples was measured by cutting portions

of the nanocomposite into 1 cm2 blocks

and attaching two wire probes to each end

and using an EC meter to read the

conductivity.

Table 1. Various compositions of

nanocomposites after mixing the polymer and

graphene for both PP and PS.

3. Results and Discussion

The polymers, once combined with

graphene powder, turned from

clear/translucent white to black throughout

at every concentration due to the natural

black/grey color of graphene. This can be

assumed to mean the GNPs were present

on the surface covering the polymer and

within the matrix itself [7].

Five samples of each

concentration of GNPs for both

nanocomposites were prepared for the

IZOD impact test for consistency. After

the test was performed, the average impact

strengths were recorded in J/m and

averaged with their respective errors.

Using the results from Table 1,

impact strength of PP/GNP and PS/GNP

nanocomposites were plotted for

comparison with their respective

concentrations in Figure 1.

Both concentrations were found to

have an overall decreasing trend in impact

strength. This trend may be due to the

incorporation of graphene in the polymer

matrix, disrupting the crystal structure. It

is important to note that the shape of the

molds

for this test included a notch in the middle

along the edge to propagate fracturing the

material. Thus, the combination of the

disruption and the shape of the molds are

possible contributors to this decrease.

Beyond a load of 10 wt% GNP,

fluctuation in impact strength is present in

both nanocomposites – more so in PS.

This can be attributed to experimental

error such as surface deformities in some

of the samples. Most notably, the PS/GNP

composites undergo a 48% reduction in

impact strength when GNP concentration

is increased from 5 to 10%. On the

contrary, the impact strengths of the

%

GNPs

% Polymer

(PP and

PS)

Weight

GNPs

(g)

Weight

Polymer

(g)

0 100 0 200

5 95 10 190

10 90 20 180

15 85 30 170

20 80 40 160

25 75 50 150

81

PP/GNP nanocomposites were reduced by

only 8.6%.

Figure 1. Impact strengths of PP/GNP and

PS/GNP nanocomposites obtained from IZOD

impact test.

Similarly, five dogbones samples

of each concentration of GNPs for both

nanocomposites were molded for tensile

testing. Stress (extension %) and strain

(MPa) were recorded every 0.1 seconds.

45 to 50 data points from the elastic region

– the region before the material begins to

yield – were plotted on a stress versus

strain graph. The slope of the line these

points created is known as the elastic or

Young’s modulus [8]. The moduli for each

sample were calculated and then averaged

for each concentration with their

respective errors.

The elastic moduli for both

nanocomposites were then plotted against

each other for comparison in Figure 2.

The polystyrene nanocomposite

immediately starts a trend in decreasing

elastic modulus as GNP concentration is

increased. A study done by Qiu et al.

suggested that adding a small

concentration of functionalized graphene

oxide (FGO) (1 wt%) to polystyrene

increased the tensile strength by 23.3%

[9]. At such small concentrations, good

dispersion of FGO in the polymer matrix

occurs. However, when increased to 5

wt% FGO, the tensile strength decreased

significantly [9], which is also apparent in

the results of this research. As

concentration increases past a certain point

– in this case being 5 wt% – GNPs start to

reaggregate as part of their nature to self-

assemble; Thus, dispersion becomes poor

[10].

Figure 2. Young’s moduli for both PP/GNP

and PS/GNP nanocomposites obtained from

tensile testing.

On the other hand, the PP

nanocomposites had a gradual and steady

increase in elastic modulus as GNP

loading increased. Although the modulus

for PP is lower than PS, it steadily

increases as GNP concentration is

increased. The explanation of this

phenomenon is somewhat complex and

beyond the scope of this research. Simply

put, GNPs have a restrictive effect on the

movement of the polymers molecular

chains, thereby increasing its modulus

[11]. Compared to pure PP, GNP loading

of 10% increased the modulus by 1.5 times

its original value. GNP loading of 25%

increased the modulus by almost 3 times

its original value.

The thermal conductivity tests

were performed at 25 °C and recorded in

W/(m*K). The GNP loadings of the molds

for PP composites for this test were 0, 7.5,

13.5, 20, 26.5 and 32 GNP wt%

82

respectively. The PS nanocomposites were

consistent with the previous tests with the

addition of 30 and 35 GNP wt%. After the

results were obtained, trendlines were

given to the set of points in order to

establish a trend between GNP loading and

thermal conductivity, illustrated in Figure

3.

Figure 3. Thermal Conductivity for both

PP/GNP and PS/GNP nanocomposites.

This test resulted in a linearly

correlated relationship between GNP

loading and thermal conductivity in both

cases. Pure PP and PS started with a

thermal conductivity of 0.223 and 0.153

which are consistent with their literature

values of 0.22 and 0.13 at 25 °C

respectively [12]. According to the

trendline, PS with 30 wt% GNP loading,

compared to pure PS, increased thermal

conductivity four-fold while PP at the

same loading of GNP increased its original

thermal conductivity five-fold.

Electrical conductivity for the

PP/GNP nanocomposites were performed

for a variety of different frequencies.

However, for the sake of consistency, the

data for 100 mHz will be used for

comparison between the various graphene

concentrations. Each sample had an area of

1 cm2. Resistances along with their

corresponding frequencies were recorded

and compiled for comparison in Figure. 4.

The natural logarithm of electrical

conductivity was compared to the GNP

content in order to establish an overall

trend between them.

Figure 4. The natural log of Electrical

conductivities and Polypropylene with

different GNP concentrations at 100 mHz.

The electrical conductivity for the

PP/GNP nanocomposites have a sharp

spike from 10 to 15 wt%, increasing it

from 260 to 2✕107 S/m. This means that

around these concentrations, the

nanocomposites reached their percolation

threshold in that pseudo-circuits were

formed within the composites structure.

4. Conclusion

The influence by addition of

graphene nanoparticles(GNPs) into

polypropylene(PP) and polystyrene(PS)

matrix is deeply studied in this research.

Our primary goal of this research has

changed from studying PP/GNP

nanocomposite to comparing the

properties of PP/GNP and PS/GNP after

we figured out that PS/GNP

nanocomposite will have a limited use due

to its brittleness.

As shown in Figure 1, an overall

decreasing trend is found in both PP/GNP

and PS/GNP nanocomposites from IZOD

impact strength due to possible

incorporation of graphene in both

polymers. From tensile testing, it is found

that PP/GNP has a increasing trend while

83

PS/GNP has a decreasing trend.

Especially, increasing the graphene

content in PS/GNP to 5 wt% of graphene

significantly lowered. The increase in

graphene concentration has a linear

increase in thermal conductivity for both

PP and PS nanocomposites.

When choosing the optimal

concentration of GNP, IZOD Impact test

becomes a deciding factor. Based on the

analysis from all tests, it’s suggested that

the most applicable concentration for

PP/GNPs is 10 wt% because when the

wt% increases from 5 wt% to 10 wt%

thermal conductivity has a steady increase

by 28.6% from 0.35 W/m*k to 0.45

W/m*k and elastic modulus also increases

by 8.2% from 16.88 MPa to 18.38 MPa. In

contrast to other trends, decreasing trend is

shown in IZOD tests. Impact strength

significantly drops by 28% from 19.62 J/m

to 14.22 J/m when the concentration

changes from 10 wt% to 15 wt%.

Electrical conductivity test showed a huge

increase from 10 to 15 wt%, which

remains consistent with the optimal range

of GNP content chosen. A possible

application of the nanocomposite could be

a battery anode because of its high

electrical conductivity, corrosion

resistance [14], and high impact and

tensile strength.

Acknowledgements

We gratefully acknowledge the

support from the Program in Chemical and

Molecular Engineering at Stony Brook

University in the form of materials and the

use of their laboratories. We would also

like to thank our PI Xianghao Zuo for his

general assistance and Professor Vladimir

Samuilov for his assistance in electrical

conductivity testing.

References

[1] Kuryla, William C. “Flame

Retardancy of Polymeric Materials”. New

York, M. Dekker., 1973. EBSCOhost

[2] “Additives for Polymers” [Electronic

Resource]. New York, NY: Elsevier

Science Pub. Co., n.d. EBSCOhost

[3] Masaya Kawasumi, Naoki Hasegawa,

Makoto Kato, Arimitsu Usuki, and Akane

Okada. “Preparation and Mechanical

Properties of Polypropylene−Clay

Hybrids”. Macromolecules 1997 30 (20),

6333-6338

[4] ASTM D256-10e1, Standard Test

Methods for Determining the Izod

Pendulum Impact Resistance of Plastics,

ASTM International, West Conshohocken,

PA, 2010, www.astm.org

[5] ASTM D638-14, Standard Test

Method for Tensile Properties of Plastics,

ASTM International, West Conshohocken,

PA, 2014, www.astm.org

[6] ASTM E1530-11(2016), Standard Test

Method for Evaluating the Resistance to

Thermal Transmission of Materials by the

Guarded Heat Flow Meter Technique,

ASTM International, West Conshohocken,

PA, 2016, www.astm.org

[7] Zhang, W., et al., “Polydiacetylene-

Polymethylmethacrylate/Graphene

Composites as One-Shot, Visually

Observable, and Semiquantative Electrical

Current Sensing Materials.” ACS Appl.

Mater. Interfaces 5.11 (2013): 4603–4606

[8] Callister, W.D. “Materials Science and

Engineering: An Introduction.” Wiley

New York, 2007.

[9] Qi, X., et al., "Enhanced Electrical

Conductivity in Polystyrene

Nanocomposites at Ultra-Low Graphene

Content." ACS Appl. Mater. Interfaces

ACS Applied Materials & Interfaces 3.8

(2011): 3130-133.

[10] Yasmin, A., et al. “Processing of

expanded graphite reinforced polymer

84

nanocomposites.” Composites Science and

Technology. 66 (2006) 1182–1189.

[11] Huang, C., et al., “Processing of

expanded graphite reinforced polymer

nanocomposites” Appl. Sci. 5 (2015)

1196-1210.

[12] Harper, C.A. “Handbook of Building

Materials for Fire Protection”. New York:

McGraw-Hill, 2004.

[13] Tu, Zhaokang, Jiang Wang,

Changjiang Yu, Hanwen Xiao, Tao Jiang,

Yingkui Yang, Dean Shi, Yiu-Wing Mai,

and Robert K.y. Li. "A Facile Approach

for Preparation of Polystyrene/graphene

Nanocomposites with Ultra-low

Percolation Threshold through an

Electrostatic Assembly Process."

Composites Science and Technology 134

(2016): 49-56. Science Direct. Web. 17

Dec. 2016.

[14] Al-Tayyib, A-HJ, Al-Zahrani, M M.

“Corrosion of Steel Reinforcement in

Polypropylene Fiber Reinforced Concrete

Structures” American Concrete Institute.

American Concrete Institute Materials

Journal, 2, 87 (1990) 108-113.

[15] Paul J. Flory, Principles of Polymer

Chemistry, 1st Edition 1953 Cornell

University.

[16] Jordan, J., et al. “Experimental trends

in polymer nanocomposites—a review.”

Mat. Sci and Eng. A. 393.1 (2005): 1-11.

85

Effects of Graphene Oxide on Proton Exchange Membrane

Fuel Cells Henry Ho, Jesse Matsuda, Mailun Yang, Likun Wang, Miriam Rafailovich

Materials Science and Chemical Engineering Department, Stony Brook, NY, 11794, USA

Abstract

Proton exchange membrane fuel cells (PEMFCs) have a high power density that is

capable of producing clean and reliable energy. In order to commercialize PEMFCs, power

output must be improved while reducing costs therefore we focus on the use and viability of

graphene oxide on Nafion membranes and electrodes for use in PEMFCs. A GO solution is

prepared by measuring out methanol, water, and GO powder and sonicating the solution. After

sonication, the solution is applied as a monolayer onto Nafion using a Langmuir- Blodgett

Trough (LB-Trough) which produces our experimental proton exchange membrane (PEM). This

is conducted with the goal of increasing the efficiency of the fuel cell. The fuel cell was initially

tested on a small single cell test station. Afterwards the fuel cell was tested using our large cell

test station. Using the same technique to produce the GO PEM, a membrane electrode assembly

(MEA) was produced consisting of the PEM sandwiched between two platinum (Pt) electrodes

and subsequently hot pressed. Variables such as relative humidity, temperature and gas flow is

controlled. With the GO applied, the fuel cell is able to yield a higher power output compared to

the control, which is the standard PEMFC without GO modifications. The polarization curve that

was generated using current density and power density showed that the fuel cell with GO was

able to output more power per square centimeter at 0.299278 W/cm2, an increase of 22.1%,

compared to the power per square centimeter at 0.244678 W/cm2 of the control fuel cell.

1. Introduction

With the world’s increasing energy

demand, PEMFCs have the potential to

become a prominent source of renewable

energy production. An attractive aspect of

PEMFCs is the lack of toxic emissions from

the production of energy as the only

products are water and energy.

Figure 1 shows the basic diagram of

a fuel cell. The left side is the anode in

which the H2 gas enters. The cathode is the

right side in which it is either connected to

O2 or to atmosphere. The center consists of

the proton exchange membrane or a specific

electrolyte. Here a proton is stripped of its

electrons. The electrons are then used to

create power by forcing them through a

circuit as they are unable to pass through the

electrolyte due to the membrane. At the

cathode, the protons react with oxygen to

produce water.

Figure 1. Basic Diagram of a PEMFC. [1]

86

At the current state of research,

energy produced from PEMFCs is limited

by the financial costs associated with the

miniscule amount of energy produced in

relation to the initial cost. The major barriers

that hinder commercialization of PEMFCs

are associated with cost, performance, and

durability. The cost is due to the use of

expensive platinum catalyst to promote the

reaction; therefore, alternative catalyst

components are being investigated for lower

cost while maintaining performance.

Performance and durability of the PEMFC

can be improved by improving the MEA

through mitigating degradation. The

Department of Energy has imposed a

standard for commercial fuel cells to operate

for 40,000 hours when stationary and 5,000

hours when in transit [1].

Since 2006, the cost of automotive

fuel cells has decreased by more than 50%

while durability has doubled [2]. The cause

of such is done by utilizing a platinum alloy

to reduce the amount of platinum and by

improving the MEA. Yet it is still possible

to further improve on this technology.

Our goal is to enhance the power

output of PEMFCs. This can be done by

enhancing the power output by using

inexpensive material. The PEMFC output

can be increased by improving the

membrane or the electrode.

The membrane of PEMFCs is one of

the core components that has drawn

considerable attention to it in regards to

what material should be used. Currently,

Nafion membranes produced by Dupont

represent most PEMFCs. The unique

structure of the Nafion membranes provide

chemical stability as well as desirable proton

conductivity under 100% relative humidity

[3]. However, the proton conductivity of

Nafion membranes is extremely dependent

on the presence of water. At elevated

temperatures or low relative humidity, a

sharp decline in proton conductivity is

noticed for the Nafion membrane due to

dehydration [4]. As such, one approach in

aiding the membrane conductivity at low

relative humidity and elevated temperatures

is by incorporating hydrophilic inorganic

additives into Nafion membranes to improve

water retention capacity [5]. Graphene oxide

(GO) is considered as an amphiphilic

material with hydrophilic regions containing

oxygenic groups and may be utilized as an

inorganic additive in conjunction with

Nafion membranes for its unique two-

dimensional structure and high surface area

[6,7].

GO is a promising material for

PEMFCs due to its excellent mechanical and

chemical properties. The functional groups

of GO allow it to have exceptional

properties in reduction oxygen reactions as

well as making it a better support than

traditional platinum on carbon electrodes for

the catalysts involved in PEMFCs [8]. The

main functional group in GO is epoxide that

acts potential once water molecules bind to

the sites, this occurs even at low relative

humidity and at room temperature [9].

Figure 2 illustrates the interaction between

the epoxide group and water molecules as

well as how predicted movement of proton

transfer.

Figure 2. Illustration of proton conduction on

epoxide groups. [10]

Studies have indicated GO can be

implemented in MEAs to enhance the

performance of PEMFCs. We conducted

87

experiments to analyze the effects on GO on

Nafion membranes and determine if the

product meets and exceeds expectations.

Two methods of preparation are used;

applying GO solution with a Langmuir-

Blodgett Trough (LB-Trough) and by spray

coating the Nafion. The resulting coated

membranes were tested in fuel cell test

stations to determine current and power

densities.

2. Experimental Section

2.1 Experimental Methods

2.1.1 Creating GO solution

The Nano Graphene Oxide (NGO)

Powder (purchased from Graphene

Supermarket) is used to create a Graphene

Oxide Solution. The solution consists of 5

mL methanol, 1 mL DI water, 6 mg

graphene oxide. The solution is sonicated

using Branson 3510 for 40 minutes before

stored. This solution was homogenous.

2.1.2 Creating GO and Nafion Solution

A solution consisting of 5 mL

methanol, 1 mL DI water, 6 mg graphene

oxide, and 18 mL liquid Nafion is mixed

thoroughly using a sonication machine for

40 minutes. This solution was homogenous.

2.1.3 Coating Nafion 117 with GO solution

The graphene oxide solution was

applied to the Nafion 117 (Purchased from

DuPont) membrane by utilizing the

Langmuir-Blodgett Trough. One half mL

(0.50) of the GO solution was added to each

side of the trough. The barriers are set to

close at a rate of 8 cm2/minute until it hit a

target of 50 mN/m on the platinum sensor.

While the barriers were closing, 0.25 mL of

GO solution was added to each side every

100 cm2. The Nafion 117 membrane is then

lifted.

2.1.4 GO Nafion 117 on small test station

The Nafion 117 is hydrated in DI

water for 5 minutes. It is then placed in

between two Commercial Platinum Carbon

(0.1 mg/cm2) Gas Diffusion Electrodes

which were purchased from

FuelCellsEtc.com and tested using the small

test station. The cathode is open to

atmosphere while the anode connects to H2.

The H2 gas flow rate is set to 78 ccm.

2.1.5 Spraying Electrodes with GO solution

A similar solution of GO is sprayed

onto Nafion membrane using an air spray

gun.

2.1.6 GO electrodes on small test station

This MEA is tested under the same

conditions as the coated experiment on the

small test station. The test station is shown

in figure 3.

2.1.7 Coating Nafion 212 with GO solution

The same steps were conducted in

regards to coating the Nafion 117 with the

LB-trough. Once the coated membrane is

obtained, it is placed in between two

electrodes. This MEA is then hot pressed

using Carver hydraulic unit model #3912 at

130 °C for two minutes for the electrodes to

adhere to the membrane.

2.1.8 GO Nafion 212 in large test station

The MEA is placed into the large test

station, (University Test Stations from

FuelCellTechnologies.com) shown in figure

4. The large test station temperature is set to

60 °C, no back pressure, and 100% relative

humidity. Current density is increased

stepwise from 0.0 A/cm2 to 7.0 A/cm2 at set

intervals, minimum gas flow rate of 50 sccm

for H2 and minimum gas flow rate of 100

sccm for O2. The gas flows were concertedly

increased as the current increased.

88

2.2 Analytical Methods

2.2.1 Isothermal Curves

Isothermal curves are obtained from

the use of the LB-trough to analyze the

pressure as a function of surface area. This

is to analyze the ability of the solutions to

maintain a monolayer.

2.2.2 Polarization Curves

The data collected by the fuel cell

test stations allow us to create polarization

curves. Three assemblies are prepared:

control, sprayed, and coated. The control

refers to the MEA that has no GO applied.

The sprayed assembly has the electrodes

sprayed with GO solution. Lastly, the coated

membrane refers to the assembly with the

Nafion membrane coated with GO using the

LB-trough. The polarization curves allow us

to visualize which fuel cell will yield the

higher output. These polarization curves

were obtained using the following test

stations: Small Test Station and the Large

Test Station.

Figure 3. Small Test Station

The small test station provides

hydrogen to the fuel cell but is also open to

air. The fuel cell would be influenced by

ambient temperature and humidity. The

large test station allows us to control these

parameters.

Figure 4. Large Test Station

3. Results and Discussion

Figure 5 depicts voltage and power

density vs current density for the control

fuel cell, the GO coated fuel cell, and the

GO sprayed fuel cell. The fuel cell that has

the membrane coated with GO through the

LB-trough yields the highest voltage to

current density and power density. The next

is the fuel cell that has its electrode sprayed

with GO. Finally, the control has the lowest

values of the three as shown in Figure 7. We

determined that the use of GO spray is an

inefficient means of applying GO as the

particles tended to accumulate on the surface

of the nozzle preventing GO from being

applied as well as developing an uneven

coating on the membrane. A suggested

method in improving this process to make

this a viable option is to use an air spray gun

made of a material that GO does not adhere

to.

As it is determined that the GO coat

is more beneficial, we moved onto testing

the GO coat in the large test station. Figure 6

89

shows that the GO coat can output roughly

22.0% more than the control at the peak,

0.95 W/cm2 vs 0.78 W/cm2. Figure 6 shows

that when operating at 0.6V the standard

PEMFC can output 0.245 W/cm2 whereas

the PEMFC with GO coating can output

0.299 W/cm2. Tateishi et al had results of

their paper GO to reach power density

output at approximately 0.93 mW/cm2 at

0.6V, about one third of the output from the

LB-trough coated membrane [9].

From the isothermal curve that is

created during the application of GO via

LB-trough, we theorized that the GO

monolayer continuously deformed and

folded over, creating a multilayer. The

steady decrease shown in Figure 8. The

increase is a result of adding GO solution as

the surface area decreased every 100 cm2.

The monolayer formed from the GO

solution constantly fell apart meaning that a

stable monolayer is not present throughout

the application. However, by introducing

liquid Nafion into the GO solution, the

isothermal curve procured from the LB-

trough, Figure 9, shows that no collapse

occurred. Figure 9 instead shows that the

surface pressure increases showing that the

layers were being pushed on top of the water

rather than into the water. This means that

the monolayer formed with the addition of

liquid Nafion held up significantly better

than the GO solution without liquid Nafion.

The Nafion is able to support the

formation of a layer because it contains both

hydrophilic and hydrophobic areas. Liquid

Nafion contains sulfonic acid functional

groups that self-organize into arrays of

hydrophilic water channels. Interspersed

between the hydrophilic channels are

hydrophobic polymer backbones which

provide mechanical stability.

Figure 5. A graph of the current densities and

power densities of the control, GO coated, and

GO sprayed obtained from the small test station.

Figure 6. A graph of the current densities and

power densities of the control and GO coated

obtained from the large test station.

Figure 7. Histogram depicting the power density at 0.6V.

90

Figure 8. Isothermal curve that is obtained

from LB-trough for GO solution.

Figure 9. Isothermal curve that is obtained

from LB-trough for GO and Nafion Solution

4. Conclusion The gathered data indicates that GO

has a beneficial effect on the PEMFC.

Coating the Nafion membrane has a larger

effect on the PEMFC and efficiency than

spraying the electrodes. A suggested method

in improving the spraying process to make it

a viable option is to use an air spray gun

made of a material that GO does not adhere

to. Figure 6 shows that the power density

for the coated is 22.0% higher than the

control at peak output. Based on the

analysis, it is apparent that the most efficient

method in enhancing the output of PEMFCs

is the coating of the membrane with GO.

The GO and nafion solution is also shown to

be able to form layers whereas the GO

solution has a preference to enter the water

instead of creating a layer.

Acknowledgements

We gratefully acknowledge the

educational support from the Department of

Chemical and Molecular Engineering at

Stony Brook University. The guidance from

Miriam Rafailovich, Clement Marmorat, and

Likun Wang has been invaluable.

References

[1] Department of Energy. Fuel Cells.

https://energy.gov/eere/fuelcells/fuel-cells

(accessed Dec 21, 2016). [2] Department of

Energy. Fuel Cell Technologies Office:

Accomplishments and Progress.

https://energy.gov/eere/fuelcells/fuel-cell-tec

hnologies-office-accomplishments-and-prog

ress (acessed Dec 21, 2016). [3] Zhang, H.;

Shen, P. Recent development of polymer

electrolyte membranes for fuel cells. Chem.

Rev. 2012, 112, 2780–2832. [4] Hickner,

M.A.; Ghassemi, H.; Kim, Y.S.; Einsla,

B.R.; McGrath. J.E. Alternative polymer

systems for proton exchange membranes

(PEMs) Chem. Rev. 2004, 104, 4587–4611.

[5] Tripathi, B.P. ; Shahi, V.K. Organic-

inorganic nanocomposite polymer

electrolyte membranes for fuel cell

applications. Prog. Polym. Sci. 2011, 36,

945-979. [6] Chen, D.; Feng, H.; Li, J.

Graphene oxide: preparation,

functionalization, and electrochemical

applications. Chem. Rev. 2012, 112, 6027–

6053. [7] Chien, H.-C.; Tsai, L.-D.; Huang,

C.-P.; Kang, C.-y.; Lin, J.-N.; Chang, F.-C.

Sulfonated graphene oxide/Nafion

composite membranes for high-performance

direct methanol fuel cells. Int. J. Hydrogen

Energ. 2013, 38, 13792–13801. [8] Devrim,

Y.; Albostan, A. Graphene Supported

Platinum Catalyst-Based Membrane

Electrode Assembly for PEM Fuel Cell. J.

Electron. Mater. 2016, 45.8, 3900-907. [9]

91

Tateishi, H.; Hatakeyama K.; Ogata, C.; et

al. Graphene Oxide Fuel Cell. J. of the

Electrochem. Soc. 2013, 106, F1175-F1178.

[10] Koinuma, M.; Ogata, C.; Kamei, Y.; et

al. Photochemical Engineering of Graphene

Oxide Nanosheets. J. Phys. Chem. 2012,

116, 19822-19827

92

Synthesis and Characterization of Gold-Palladium

Nanoparticles Catalyst For Improved Hydrogen Fuel Cell

Performance

Adam Bennetta, Helen Liua, Allen Trana, Likun Wangb,

Miriam Rafailovicha,b* a,bChemical and Molecular Engineering Department, Stony Brook, NY, 11794, USA

Abstract

Alternative energy sources are becoming increasingly more important in meeting

current energy demands, due to the issues faced by conventional energy sources such as

rising costs, political volatility, depletion of resources and carbon emissions. One promising

renewable energy source is the proton exchange membrane (PEM) hydrogen fuel cell, which

creates only water as a major byproduct. Currently, platinum (Pt) is the predominant catalyst

being used for PEM hydrogen fuel cells. The total system cost is predominantly due to the

high cost of Pt. Pt catalyst activity in the hydrogen fuel cell suffers from exposure to carbon

monoxide (CO), which limits the viability of PEM fuel cells for vehicles and stationary

applications. Gold (Au) has been investigated as a promising catalyst for the oxidation of CO

to carbon dioxide (CO2), the addition of which to the PEM fuel cell can increase its longevity.

However, due to the high cost of Au, an alternative catalyst could greatly reduce the total cost

of a fuel cell system while maintaining high performance, making it a much more feasible

option. This research looks at the use of Gold-Palladium (Au-Pd) nanoparticles as a catalyst

for the improvement of hydrogen fuel cell performance. In this study the bimetallic Au-Pd

catalyst were synthesized via the Brust Method and characterized using extended X-ray

absorption fine structure (EXAFS) and transmission electron microscopy (TEM) techniques.

The subsequent testing on the catalyst was done at the fuel cell test station located at Stony

Brook University in order determine its power density. Through the characterization and test

station results it was determined that the synthesized Au-Pd catalyst had an alloy structure

and produced roughly 15% more power than the control catalyst.

1. Introduction

Fossil energy sources are currently

facing not only political issues such as

volatility and an unpredictable nature, but

also environmental concerns of resource

depletion and carbon emissions.

Alternative energy will be vital for our

increasing global energy demands and

renewable energy sources such as fuel

cells can play a key role in combating

these problems. A fuel cell is a device that

uses a fuel to convert chemical energy into

useable electricity. Fuel cells are an

attractive energy source due to the fact that

they don’t emit greenhouse gases and they

have a high energy density and efficiency

[1].

In a proton exchange membrane

(PEM) fuel cell, a polymer membrane is

held between the cathode and anode. This

membrane allows protons to pass while

preventing electrons from doing so.

Keywords: Energy, PEM fuel cell, Catalyst, Nanoparticles, Hydrogen fuel cell, Au-Pd

2

Nafion, a copolymer, is commonly used as

the membrane due to its high conductivity

and mechanical durability [2].

An external wire is placed on the

fuel cell connecting the electrodes and as

the electrons build up they travel from the

anode into the cathode producing an

electric current. Water and heat is also

produced during this reaction. As of now

PEM fuel cells are one of the most

promising alternative energy sources for

transportation as well as having

commercial and residential applications.

Figure 1. Schematic of PEM Fuel Cell [3]

Currently the most common type of

fuel cell is the hydrogen fuel cell.

Hydrogen is the most abundant element on

earth and can also be generated by splitting

a water molecule with a DC current

through the process of electrolysis [1]. In

the PEM hydrogen fuel cell (PEM HFC),

hydrogen is flowed through the membrane

on the anode side and reacts with oxygen

on the cathode side. The reaction occurs in

the presence of an electrolyte (the

membrane). The reaction occurring at the

anode is referred to as the hydrogen

oxidation reaction (HOR) and the reaction

occurring at the cathode is called the

oxidation-reduction reaction (ORR).

Table 1. Hydrogen PEM Fuel Cell Reactions

Anode

(Oxidation)

H2→ 2H++2e-

Cathode

(Reduction)

O2+2H++2e-

→H2O

Total Reaction H2+ O2 → H2O

In a fuel cell, the catalyst properties

are important in that they have a large

effect on performance. The rate-

determining step in the electricity

production of the fuel cell is usually the

ORR at the cathode [4]. In order for the

reaction to move forward, toward the

formation of electricity and water the

activation energy of the reaction must be

surpassed. Catalysts are needed for fuel

cells to lower the reactions activation

energy.

The catalyst is used at the cathode

and anode of the fuel cell and works by

promoting the HOR and the ORR.

Different catalysts and supports are widely

studied in order to find the most efficient

and cost effective fuel cell for operation.

Although current technology exists for

PEM HFC applications, its wide spread

implementation is not yet feasible due to

the high overall cost and questions of

durability. In order for the PEM HFC to

become a viable option for vehicle and

93

3

stationary applications the costs needs to

be greatly reduced [1].

Platinum (Pt) is the predominant

catalyst used in the PEM HFC and is

responsible for the bulk of the cost. The

durability of Pt catalysts is also

questionable due to carbon monoxide (CO)

poisoning [5]. The effects of CO poisoning

on the Pt catalyst are especially of interest,

since even just 25 ppm of CO can reduce

PEM HFC output by 50% [6]. Many

sources of hydrogen gas come from

natural gas reforming and thus contain a

considerable amount of CO2, which can

become CO while the PEM HFC is

operating and thus poison the Pt catalyst

[7]. Current research is geared towards the

cost reduction and increasing efficiency

and durability by addressing the effects of

CO poisoning. A major challenge that

remains is finding a suitable catalyst which

can oxidize CO. The application of a

suitable catalyst which can oxidize CO

allows the utilization of cheaper hydrogen

gas sources by reducing the purity

requirement of the feed hydrogen gas.

Nanoparticles are particles between

1 and 100 nm in size and are often seen to

have increased catalytic activity due to

their small size which prove a larger

surface area (source). Gold (Au) and

Palladium (Pd) nanoparticles have both

been shown to have catalytic properties in

CO oxidation. It has been shown that

supported Au nanoparticles are extremely

effective catalysts for oxidizing CO [6].

The catalytic activity of Au can even

further be enhanced by incorporating a

second metal as an alloy [5]. Pd has been a

metal of interest to combine with Au, since

it can add electrons to the system and thus

increase catalytic activity.

The most noticeable improvement in

catalyst performance occurs with a gold-

palladium core-shell nanoparticle

conformation. The palladium atoms on the

shell withdraw atoms from the gold core,

shifting the d-band center of palladium

such that the adsorption of O2 and O-O

bond breaking is promoted. The negative

charge on the palladium shell stabilizes

oxygen atoms as they dissociate, lowering

the energy barrier for O2 to dissociate

[9,10].

It has been shown that synthesis

methods greatly affect the size and

morphology of bimetallic nanoparticles.

Current methods to synthesize Pd

bimetallic catalysts lead to polydispersity

or clusters of nanoparticles. These

methods work, however, make

characterization difficult. The morphology

of nanoparticles has a great effect on the

catalyst activity [5]. Current research

involving Au-Pd nanoparticle catalysts

involve either core shell or alloy structure

and are synthesized in various ways. A

bimetallic core shell nanoparticle is

composed of two phases, one metal in the

core and the other surrounding it. A

bimetallic alloy is a random mixture of the

two metals [11].

Figure 2. Possible representative architectures

of bimetallic nanoparticles (a) alloy, (b) core-

shell, (c) cluster-on-cluster, (d) sub-shell, and

(e) intermetallic [11]

94

4

In this research the bimetallic

nanoparticle catalyst was synthesized via

the Brust Method, which is known to

produce small, high-surface area thiol-

stabilized nanoparticles through the

reduction of the metal. The Langmuir-

Blodgett (LB) trough was used to spread

the synthesized particles over water in a

trough that uses the surface tension to

compress them onto the Nafion membrane.

Bimetallic nanoparticles are

extremely difficult to characterize so the

purpose of this research is to determine

whether the Au-Pd nanoparticles

synthesized via the Brust Method have an

alloy or core shell structure. Extended X-

ray absorption fine structure (EXAFS) was

conducted at the Stanford Synchrotron

Radiation Lightsource (SSRL). EXAFS

experiments are used in order to model the

coordination environment around the

absorbing metal atoms. Transition electron

microscopy (TEM) was also done in order

to determine the size and dispersity of the

nanoparticles. The goal of this

characterization is to determine what the

exact structure of the synthesized Au-Pd

catalysts are.

After the catalysts were

characterized they were tested on the

hydrogen fuel cell test station at Stony

Brook University to determine the power

output of the fuel cell with the nanoparticle

catalysts. This was done by creating

polarization curves of the voltage against

current and power against current, which

allows us to compare the power output

between a control and the Au-Pd catalysts.

We hypothesize that PEM HFCs

assembled with the membranes with the

synthesized Au-Pd nanoparticles will have

increased power output as compared to

HFCS with just the platinum catalysts,

thus improving the performance of PEM

fuel cells. If successful this research can in

turn find ways to improve the durability of

the fuel cell system.

2. Experimental Section

2.1 Materials

HAuCl4 was purchased from Sigma

Aldrich (99%). K2PdCl4 was purchased

from Sigma Aldrich (99%). 0.1 mg/cm2 Pt

Loading Electrode Catalyst Paper was

purchase from Fuel Cell Store.

2.2 Experimental Methods

2.2.1 Nanoparticle Synthesis

In preparing the nanoparticle

solution, 393.83 mg (1 mmol) of

tetrachloroaurate (HAuCl4) and 326.43 mg

of palladous potassium chloride (K2PdCl4)

were dissolved in 36 mL of deionized

water. Following, 2654.59 mg of

tetraoctylammonium bromide (TOABr)

was dissolved in 96 mL of toluene. This

solution was added to the tetrachloroaurate

and palladous potassium chloride mixture

and then magnetically stirred for 20

minutes until the mixture separated into

two distinct layers. 200 µL of

dodecanethiol and 453.96 mg of sodium

borohydride (NaBH4) dissolved in 30 mL

of deionized water were added to the two-

layer solution, and magnetically stirred at

room temperature for 3 hours. The

aqueous layer was removed from the

solution via separatory funnel and the

remaining top layer was rotary evaporated

until 5 mL remained. 200 mL of ethanol

was added to the top layer solution and

refrigerated overnight at 4ºC. The top

solution was removed from top layer

solution and the remaining sample was

centrifuged at 5000 rpm for 10 minutes

then washed with ethanol multiple times.

The sample was dried in the vacuum

desiccator for 2 days.

95

5

2.2.2 LB Trough Coating

The Langmuir-Blodgett (LB)

Trough was calibrated to less than 0.25

mN/m surface pressure prior to

nanoparticle coating. The 212 Nafion

membrane is placed on the platinum plate,

attached to a hook in the center of the LB

Trough. 100 µL of nanoparticle solution

was added via micropipette to each side of

the LB trough. The solvent from the

nanoparticle solution was allowed 10

minutes to evaporate before starting the

coating process. The target pressure was

set to 5 mN/m with pushing rate of 6

mm/min.

2.2.3 Fuel Cell Test Station

The fuel cell test station was

operated at 60C. The 212 Nafion

membrane, was wetted with deionized

water and placed in a pre-fabricated

membrane electrode assembly (MEA). On

the fuel cell test apparatus (Fuel Cell

Technologies, Inc., SFC-TS), 78 ccm of

hydrogen gas was constantly flowed into

the MEA. The cell was held at 6V for 1

hour to stabilize it, and then cycled

between 0.5 A/cm2 and 1 A/cm2 9 times.

The cell was then operated at 0.2 A/cm2

for 6 hours to fully humidify the cell.

Following the break-in procedure, the VIR

software in LabVIEW was initiated to

conduct performance tests on the cell.

2.3 Analytical Methods

2.3.1 Transmission Electron Microscopy

(TEM):

The nanoparticle sample was

subjected to TEM observations using the

JEOL JEM 1400 Transmission Electron

Microscope. A few drops of nanoparticle

solution using a micropipette was diluted

in a petri dish of toluene and placed on a

TEM grid. Images were observed at 100kx

and 200kx magnifications. High

Resolution TEM was also performed on

the nanoparticle sample.

2.3.2 Extended X-Ray Absorption Fine

Structure (EXAFS):

Extended X-Ray Absorption Fine

Structure measurements were performed

on the nanoparticle solution at Stanford

Synchrotron Radiation Lightsource

(SSRL). X-Rays of narrow energy

resolutions were shone at the sample and

the transmitted x-ray intensity was

recorded. Dependent on sample thickness,

absorption coefficient, and atom type a

number of photons are absorbed by the

sample. When the incident x-ray energy

matches the binding energy of the

electrons of an atom in the sample, the

number of x-rays is increased and the

transmitted x-ray intensity drops, resulting

in the absorption curve.

3. Results and Discussion

The TEM images were observed to

be spherical nanoparticles in Figure 3. The

nanoparticle size distribution is relatively

similar, with TEM gridded AuPd

nanoparticle size averaged at 2.02 nm and

the LB trough AuPd nanoparticle size

averaged at 1.86 nm. This suggests a high

surface area for the nanoparticles.

(a)

96

6

(b)

(c)

Figure 3. TEM Images of (a) Au , (b) Pd, and

(c) AuPd Nanoparticles

Figure 4. AuPd NP TEM Gridded TEM Size

Distribution

Figure 5. AuPd NP LB Trough TEM Size

Distribution

(a)

(b)

Figure 6. (a) HR TEM of AuPd NPs (b)

magnified view of crystalline platelet structure

From the HR TEM, it can be seen

that the AuPd NPs have a crystalline

platelet structure, which is suggested in

literature to by the structure needed by

nanoparticle catalysts in order to

effectively oxidize CO. The platelet

structure has good contact with the

support, and has enough surfaces for the

reaction to occur, since the oxidation

reaction occurs on the edges and steps of

the platelet, rather than the smooth

surfaces [8].

97

7

The XANES (Fig. 7) analysis

shows that the Au nanoparticles samples

prepared through conventional methods

and LB trough were consistent with

metallic Au foil, with lower-intensity

features due to the presence of thiol-

stabilized NPs. The EXAFS spectra also

shows that the gold NP samples were close

to that of metallic gold, but showed lower

amplitudes again due to the presence of

Au-S bonds.

Figure 7. XANES for AuNP

Figure 8. EXAFS for AuNP

Figure 9. Pd K-Edge XANES for thiol-

stabilized PdNPs

Figure 10. Pd K-Edge EXAFS for thiol-

stabilized PdNPs

The Pd K edge XAS spectra for the

palladium nanoparticles are shown in

figures 8 and 9. The XANES spectra for

both samples are different from the

XANES for metallic Pd. They do however

resemble the XANES spectra for PdS very

closely. Similarly the EXAFS spectra and

the Fourier transformed EXAFS for the

nanoparticles show a very good indication

of a strong Pd—S contribution by the low

frequency oscillations and a maximum in

the low wavenumber range. The metallic

Pd is also present in the sample as shown

by the 2 and 3 Å peak indicating a Pd—Pd

bond. The XAS comparison of the

nanoparticle samples prepared with

conventional methods versus the LB

method show us there is no significant

difference between the two. No difference

between the samples was seen in

agreement with the XANES that both

samples had a large contribution of Pd—S

bonds.

From EXAFS analysis, it can be

seen that there are no Pd-Pd bonds in the

AuPd NPs sample. This suggests that the

AuPd NP is in an alloy configuration

rather than a core-shell configuration,

since a core-shell configuration would

show both Au-Au bonds and Pd-Pd bonds.

The final part of this research

involved determining the power output of

98

8

the synthesized Au-Pd nanoparticle

catalyst. This was done at the fuel cell test

station at Stony Brook University. 0.1

mg/cm2 Pt loading was used on the

electrode for the control and the Au-Pd

catalyst to compare the power output. The

membrane used was Nafion 212, which

has a thickness of 50.8 micrometers.

Hydrogen was flowed through the fuel cell

at a rate of 78 CCM (cubic centimeter per

minute). The operating temperature was

set at 60 ⁰C.

Figure 11. Voltage against current for Au-Pd

Catalyst with 0.1 mg/cm2 Pt catalyst loading

and control of only 0.1 mg/cm2 Pt catalyst

loading.

From the polarization curves we

determined that the hydrogen fuel cell had

a higher maximum power output with the

Au-Pd nanoparticle catalyst as opposed to

just the control. The maximum power

output of the Au-Pd catalyst was 0.314

watts whereas it was only 0.275 for the

control. Approximately 14.18% more

power was generated with the Au-Pd

nanoparticle catalyst.

Figure 12. Power against current for Au-Pd

Catalyst with 0.1 mg/cm2 Pt catalyst loading

and control of only 0.1 mg/cm2 Pt catalyst

loading on electrode.

9Table 3. Max power and current for Au-Pd

Catalyst with 0.1 mg/cm2 Pt catalyst loading

and control of only 0.1 mg/cm2 Pt catalyst

loading on electrode.

Catalyst Max

Power

(watts)

Max

Current

(amps)

Au-Pd 0.314 0.748

Control 0.275 0.699

Table 2: Structure parameters (coordination numbers N, interatomic distances R and disorder

factors σ2), obtained in fitting of experimental EXAFS data

99

9

In the fuel cell hydrogen is

oxidized at the anode and oxygen is

reduced at the cathode producing water.

Platinum catalyzes both of these reactions.

These results found that the use of an Au-

Pd nanoparticle catalyst catalyzes the fuel

cell reactions at a better rate yielding a

better power output.

4. Conclusions

The majority of the current work on

fuel cells is aimed towards their potential

use in vehicles, PEM fuel cells being the

most promising. In order for PEM fuel

cells to become a viable option for

vehicles and stationary applications the

cost needs to be reduced and the durability

needs to be increased. Fundamental

research is needed for the fuel cell

membrane and catalyst layer. The

characterization techniques of EXAFS in

conjunction with TEM showed that it is

highly likely the catalyst we synthesized

was an alloy. The XAS spectra showed us

that the obtained coordination numbers

and Pd—Pd and Pd—S distances are

consistent. Therefore it can be concluded

that the investigated samples contain both

metallic nanoparticles, as well as the low

molecular weight Pd-thiol complexes. The

absence of Pd-Pd bonds in the EXAFS

analysis of the AuPd NPs strongly

indicates that the synthesized catalyst is in

the alloy configuration.

The synthesized catalyst had an

average size of 1.98 nm indicating that the

Brust method was effective was effective

in synthesizing Au-Pd nanoparticles with

an effective surface area. From the HR-

TEM, it could be seen that the synthesized

AuPd NP had the desired crystalline

platelet structure, further indicating the

efficacy of the Brust method.

The research performed shows a

promising result of the Au-Pd alloy

catalyst in the PEM HFC. The Au-Pd alloy

nanoparticles increased the output of the

fuel cell showing that it is an effective

catalyst for fuel cell reactions. The power

output of the Au-Pd catalyst was

approximately 14.18% higher than the

control.

Acknowledgements

We gratefully acknowledge the

financial support from the Department of

Materials Science & Engineering and the

Program in Chemical and Molecular

Engineering at Stony Brook University

through research funding. We also thanked

the Advanced Energy Center (AERTC) to

provide laboratory and equipment and

SSRL to give us analysis of our

nanoparticles.

References

[1] Department of Energy Fuel Cell

Technologies Office Multi-year Research,

Development and Demonstration Plan

[2] Wang, Y., Chen, K. S., Mishler, J.,

Cho, S. C., & Adroher, X. C. (2011). A

review of polymer electrolyte membrane

fuel cells: Technology, applications, and

needs on fundamental research. Applied

Energy, 88981-1007.

[3] Jones, R. L. (2017, March 27). Energy

Storage & Delivery. Retrieved from

https://www.nist.gov/programs-

projects/energy-storage-delivery

[4] Reda, M. R. (1970, January 01). The

Rate Limiting Step (RLS) for the Oxygen

Reduction Reaction at the Cathode of

Polymer Electrolyte Membrane Fuel Cell.

[5] Hutchings, G., & Kiely, C. (n.d).

Strategies for the Synthesis of Supported

Gold Palladium Nanoparticles with

Controlled Morphology and Composition.

100

10

Accounts Of Chemical Research, 46(8),

1759-1772.

[6] Baschuk, J.J., and Xianguo Li.

International Journal of Energy Research

25, no. 8 (200): 695-713.

[7] Janssen, G.J.M., and N.P. Lebedeva. In

Presented at the Conference: Fuel Cells

Science and Technology vol. 2004, pp. 6-7.

2004.

[8] Haruta, M. Gold Bull (2004) 37: 27.

doi:10.1007/BF03215514

[9] Chen, D. et al. Core-shell Au@Pd

nanoparticles with enhanced catalytic

activity for oxygen reduction reaction via

core-shell Au@Ag/Pd constructions. Sci.

Rep. 5, 11949

[10] Staykov, A., Derekar, D., &

Yamamura, K. (2016). Oxygen

dissociation on palladium and gold

core/shell nanoparticles. International

Journal Of Quantum Chemistry, (20),

1486.

[11] Scott, R. J. (2015). Rational design

and characterization of bimetallic gold-

palladium nanoparticle catalysts. Canadian

Journal Of Chemical Engineering, 93(4),

623-630.

101

102

Pressure-Time Dependence and Simulation of Carbon Dioxide

Hydrate Formation

Sandhiya Kannan, John Mikhail, Raphael Prodromou,

Maya Endoh, Tadanori Koga

Chemical and Molecular Engineering Program, Stony Brook University, Stony Brook, NY 11794

Abstract:

Two concurrent objectives in modern industry – to acquire natural gas for energy and to minimize

the greenhouse effect by reducing carbon dioxide (CO2) concentration in the atmosphere – have

led to an interesting research opportunity. By replacing the methane naturally present in ocean-

floor hydrates with carbon dioxide, both concerns may be addressed simultaneously. The time

necessary for nucleation of these hydrates upon exposure to carbon dioxide gas is not yet known,

and would allow for more efficient process development for hydrate formation on the ocean floor.

This project focuses on an analysis of the formation time of CO2 hydrates as a function of pressure.

These hydrates are formed in the laboratory through replication of ocean-floor low temperature

and high pressure conditions using a cooled pressure chamber. Once water is loaded into the cell,

the system is isolated and pressurized with CO2 gas; a pressure transducer with a LabVIEW

interface is used to record the pressure of the isolated system over time. The observation of a

pressure drop indicates that some of the gaseous CO2 had been captured in a hydrate; the nucleation

time of the hydrate could be determined from these data as well. We found that the pressure drop

due to hydrate formation took place approximately twelve hours after the start of each trial. In

addition, computer simulations of the water and CO2 system at the nanoscale were performed to

establish a functional model which may be used for future work; Lennard- Jones interatomic

potentials and harmonic bond styles were used to calculate the force interactions. By analyzing

simulation results based on this model, it is possible to obtain an additional perspective of the

hydrate formation and visualize the clathrate structure at an atomic level. Continued study of the

hydrate formation process would allow its deployment as an efficient method for simultaneous

methane extraction and carbon dioxide sequestration.

Keywords: Carbon Dioxide Hydrates, Clathrates, Computational Modeling

1 Introduction

According to the United States

Energy Information Administration’s

International Energy Outlook 2016, the

world’s energy consumption has been

increasing in recent years and is predicted to

increase by 48% between 2012 and 2040,

with natural gas continuing to account for the

greatest increase in energy consumption

globally [1]. While natural gas tends to burn

cleaner than other fossil fuels, the

deployment of hydraulic fracturing, shale gas

production, and other technologies still

produce significant quantities of greenhouse

gas emissions, including carbon dioxide.

Methods of carbon dioxide sequestration are

being researched along with new and

unconventional sources of natural gas, but

recently there have been developments

towards a means of achieving both

simultaneously.

Methane seeping through faults on

the ocean floor forms hydrate/clathrate ice

structures, which consist of water molecules

arranged in crystalline cage structures

trapping methane gas molecules in the center,

103

Figure 1. Natural formation of methane hydrates

with clathrate structure on the ocean floor [2].

as seen in Figure 1.

These hydrates form through

exothermic reactions and are stable at low

temperatures and high pressures such as

those found at the ocean floor. By

destabilizing these hydrates, it would be

possible to collect the methane gas released

to the ocean surface. In our planned project,

we expect to observe a similar but more

exothermic hydrate formation of CO2

molecules within our system

and the formation of a Type I clathrate

structure. Based on previous studies, the heat

released from the hydrate formation should

be enough to dissociate methane hydrates

under the same pressure and temperature

conditions, allowing for a simultaneous

sequestration of carbon dioxide and

extraction of methane for natural gas.

1.1 Previous Work

Previous studies have focused on the

methane hydrate formation process, such as

one by Tsutomu, Ebinuma, and Ishizaki

which aimed to examine the equilibrium

conditions of methane hydrates to locate the

bottom of the gas hydrate stability zone

(BGHS) and establish the formation and

dissociation rates for the hydrates [3]. It was

hypothesized in this study that confining

methane hydrates in small pores of porous

glass would make it possible to observe the

effect of pore size and temperature on hydrate

dissociation. Pieces of porous silica glass

with pore diameters of 100, 300, and 500 Å

were dried and inserted in a high-pressure

cell along with distilled, deionized water

which filled the pores. The cell was

maintained at room temperature for one day,

and then the air was evacuated before

methane gas was introduced. A cryostat was

used to set the pressure cell to a

predetermined temperature, and hydrate

formation was observed over the course of

several days. Upon formation of the hydrate,

pressure in the cell was observed to gradually

drop to equilibrium while the temperature

rose.

By examining the equilibrium data, it

was found that smaller pore diameters result

in a larger temperature shift of the

equilibrium line. Pores that were smaller than

or on the order of 300 Å showed a heat of

methane diffusion different from the bulk

material. Dissociation temperature and

pressure trends in the experiment were,

however, accurately predicted using the

Clausius-Clapeyron equation; the shift in the

equilibrium lines of the methane hydrates

was similar to the results obtained from

Melnikov and Nesterov. The latent heat of

the confined hydrate was smaller than when

the hydrate was formed in bulk [3].

In a study by Jacobson et al., clathrate

nucleation was studied by varying the size

and hydrophilicity of guest molecules [4].

They examine clathrate formation using

molecular dynamics simulations with a

constant pressure and temperature

thermodynamic ensemble and a coarse-

grained model, meaning fine details of the

molecules were blurred into more

macroscopic properties. Clathrate formation

proceeds by a mechanism for nucleation,

which cannot be determined exactly from

empirical thermodynamic analysis of the

104

system. Molecular dynamics enables a much

more detailed picture of the experiment by

allowing the researchers to examine

individual atoms or molecules and their

trajectories over the course of the simulation.

The authors present the “blob

mechanism” of clathrate formation, in which

nucleation occurs in stages starting from a

small collection of molecules beyond a

certain critical density. The dissolved guest

collects into a blob, followed by an

amorphous clathrate, and then finally a

crystalline clathrate. For carbon dioxide,

relatively small clathrate cages are formed,

either 512 or 51262. Also, approximately 30

cages are needed to initiate formation of the

amorphous clathrate. Carbon dioxide and

methane, both being relatively small and

hydrophobic molecules, use the same cage

structures and are thus susceptible to the

memory effect, in which hydrates form

quickly because the appropriate cages are

already present in the medium.

A study done by Horvat and Mahajan

demonstrated the interactions associated with

CO2, CH4, and sand systems [5]. In the first

trial, CH4 hydrates were formed in a high-

pressure cell with no sand within 96 hours.

With the introduction of CO2 there was an

instant formation of CO2 hydrate as the

atmosphere in the cell became 99% CH4. In

the second trial, CO2 hydrate was allowed to

form before the introduction of CH4. CH4

hydrate formed initially, but over time the

hydrates dissociated and the CH4 entered the

gas phase. In the third trial, CH4 formed

hydrates in sand and then CO2 was

introduced. Gas analysis showed that most of

the hydrates were mostly CO2. In the fourth

trial, CO2 was allowed to form hydrates in the

sand and then shortly after CH4 was

introduced. The composition of the gas in this

run was low in CO2. Lastly, CH4 was allowed

to form hydrates in the sand before the

introduction of CO2. Upon depressurization,

CO2 concentration increased thus indicating

that most of it was in hydrate form. This study

showed that CO2 was the dominant hydrate

and gives insight into the relative speed of

hydrate formation.

In this study, we aim to analyze the

nucleation time of the carbon dioxide

hydrates as a function of pressure.

Simulations will also be used to predict and

model the hydrate structure and formation

process. By gaining a greater understanding

of the carbon dioxide hydrate formation and

structure, it would possible to optimize a

process for the simultaneous methane

extraction and carbon dioxide sequestration.

2 Materials and Methods 2.1 Hydrate Formation

In this experiment, ocean floor

conditions were emulated using a chiller and

high-pressure cell custom-designed per

specifications for a 2010 study by Koga et al.,

[7] as seen in Figure 2. The temperature was

set to 4 ℃ and a range of pressures between

the hydrate formation pressure and vapor-

liquid equilibrium of carbon dioxide. Carbon

dioxide flowed from a compressed gas

cylinder, which was monitored by pressure

gauges, into a high-pressure cell loaded with

water. The high-pressure cell was connected

to a chiller flowing cold water through its

walls to maintain the temperature of 4 ℃.

Figure 2. Pressure gauges and transducer (left)

and high-pressure cell with thermocouple (right)

The primary objective was to monitor

the pressure of the system as a function of

time to observe the exothermic hydrate

formation of CO2. The temperature in the cell

was also verified via the thermocouple

105

connected to the top of the cell. The

measurements were recorded using a

LabVIEW virtual interface, as seen in Figure

3.

Two trial runs were conducted under

specified experimental conditions, at

pressures of 420 psi and 550 psi, respectively.

Initially, 22 mL deionized water was added

on top of a Teflon platform in the chamber.

Figure 3. LabVIEW virtual interface displaying

pressure in cell as a function of time, recorded

with pressure transducer

The system was then sealed and purged of air

using carbon dioxide, prior to being

pressurized at the specified level for each

trial. The system was left pressurized for 1

day for the 420 psi trial and 2 days for the 550

psi trial, and data were collected using the

pressure transducer.

2.2 Model

To obtain an atomic-scale view of the

hydrate formation process, computational

models were used to represent carbon dioxide

and water interacting at specified temperature

and pressure. Interatomic interactions for

atoms i and j were modeled using both

Lennard-Jones and Coulombic potentials, per

Equation 1 [14].

𝐸𝑖,𝑗 =

{4𝜀𝑖,𝑗 [(

𝜎𝑖,𝑗

𝑟)12

− (𝜎𝑖,𝑗

𝑟)6

] +𝐶𝑞𝑖𝑞𝑗

𝜖𝑟, 𝑟 < 𝑟𝑐

0, 𝑟 ≥ 𝑟𝑐

(1)

Here, εi,j and σi,j are Lennard-Jones

parameters, C and ϵ are electrostatic

constants, q is partial charge, rc is cutoff

distance, and r is the distance between atoms

i and j. Bonding interactions for two atoms

are modeled using a harmonic bonding style,

according to Equation 2 [14].

𝐸𝑏𝑜𝑛𝑑 = 𝐾(𝑟 − 𝑟0)2 (2)

2.2.1 Derivation of K value

Equation 2 can also be written as

follows:

𝐸𝑏𝑜𝑛𝑑 =1

2𝑘(𝑟 − 𝑟0)

2 (3)

For simple harmonic motion, the frequency

may be written as:

𝑓 =1

2𝜋√

𝑘

𝑚 (4)

𝑘 = 𝑚(2𝜋𝑓)2 (5)

IR data was used to determine the equivalent

wavelength for light of a bond. Thus, the

following substitution was made for

frequency:

𝑓 = 𝑐𝜆−1 (6)

𝑘 = 𝑚(2𝜋𝑐𝜆−1)2 (7)

Finally:

𝐾 = 2𝑚(𝜋𝑐𝜆−1)2 (8)

2.2.2 Summary of modeling parameters

106

Table 1. Bonds [7][8][9][10][11][12]:

Molecule Bond r0 (nm) K (ag/ns2)

H2O O-H 0.10 3.9015e5

CO2 C=O 0.12 1.4105e6

Table 2. Interatomic potentials [13]:

Molecule Atom εi,i

(ag⋅nm2/ns2)

σi,i

(ag) qi (n⋅e)

H2O H 0 0 0.4238

O 78.18 0.3166 -0.8476

CO2 C 28.129 0.2757 0.6512

O 80.507 0.3033 -0.3256

For LJ parameters between different

types of atoms or atoms in different types of

molecules, ε and σ were combined using the

Lorentz-Berthelot rules [13].

2.3 Simulation

To set the desired pressure and

temperature, the system is first run under an

isothermal-isobaric ensemble. The timestep

is set to τ = 0.00045 nanoseconds and the

simulation is run for 10,000,000τ. After this,

a canonical ensemble is used and the

simulation is run for an additional

10,000,000τ. This second run simulates the

beginning of the hydrate formation process

once the water and carbon dioxide have been

pressurized.

3. Results

3.1 Hydrate Formation

Carbon dioxide hydrates were

observed to form in the range of pressures

specified previously. The hydrate formed

within the pressure cell with specific

crystalline structures, and upon

depressurization and warming of the chamber

the carbon dioxide began to dissipate from

the hydrate. An image of the hydrate within

the pressure chamber is seen in Figure 5.

We initially confirmed formation of

the hydrate from visual inspection of the

water during and after our experiments. After

depressurization, we observed the presence

of a white solid at 4 ℃ and less than 580 psi

– this solid could not be ice. Popping sounds

were heard coming from the ice after being

exposed to ambient conditions, which

indicated release of CO2 from the hydrate. In

addition, the pressure

Figure 5. CO2 hydrate under 550 psi pressure

and 4℃

data obtained in the experiments showed the

sharp change in slope associated with hydrate

formation. This sharp change in the pressure

vs. time graph is the result of carbon dioxide

hydrate forming rapidly after nucleation sites

are developed. The driving force for this

process is the excess pressure that is above

the hydrate transition.

107

Figure 6. Hydrate upon depressurization

Figure 7. Cell pressure as a function of time at 420 psi and 550 psi, respectively, at 4℃. The red box

shows the characteristic sharp change in slope in the graph of pressure vs time, which is indicative of

hydrate formation.

3.2 Pressure Data

Pressure data from the transducer for

each trial were plotted as a function of time.

As seen in Figure 7, a sharp drop in the

pressure was seen around 720-730 minutes

after the start of the 420-psi run, when the

pressure dropped due to hydrate formation. A

more significant pressure drop is seen in the

550-psi trial at a similar nucleation time.

3.3 Simulation Results

First, a computational model for the

simplified H2O/CO2 system was developed,

as seen in Figure 8. The positions and

orientations of water molecules were

generated using the solvate plugin of the

Visual Molecular Dynamics program

(VMD) [6]. Six molecules of carbon dioxide

were manually added to the model at

random positions and orientations. This

relatively small number of CO2 molecules

was chosen so each may be isolated from the

rest and analyzed independently after

hydrate formation. Additionally, data was

compiled on hydrate formation pressure as a

function of temperature, phase diagrams,

and the cage occupancy of carbon dioxide.

These findings are summarized in figures 9

108

Figure 8. Model of molecules of carbon dioxide

surrounded by water. Carbon (blue), oxygen

(red), hydrogen (white)

and 10. Figure 9

takes literature data and compares it

with the minimum pressure of hydrate

formation for CO2 in water. Data for pure

water was plotted using values obtained from

the Colorado School of Mines’ CSMHYD

software for prediction of hydrate

equilibrium [15].

Figure 10 shows the occupancy rates

of small (512) and large (51262) clathrate

cages, with water acting as the host

molecules and carbon dioxide the guests.

Large cages are nearly saturated with CO2 at

all tested temperatures, as indicated by the

occupancy rates in excess of 98% for all data.

Small cages do not show the same

consistency, but they begin with roughly 75%

occupancy at 0.5 ℃ and increase steadily

with both temperature and pressure. The

proposed rationale for this increase in

occupancy is that hydrate formation

pressures scale at nonlinear rates with respect

to temperature, and it seems this increase in

pressure is sufficient to negate the effects of

greater vapor pressure of CO2 at higher

temperatures.

Figures 9 and 10. CSMHYD data for pure water and CO2 hydrates.

4. Discussion

In the experimental portion of the

project, we demonstrated that a CO2 hydrate

can form at a temperature of 4 ℃ and a

pressure between 300 and 580 psi. From the

experimental data collected, the time

necessary for hydrate formation is between

720 and 730 min. The hydrate formation time

was not significantly different for the trials at

different pressures. A greater pressure drop

was observed for the trial with an initial

pressure of 550 psi. This is a result of the

greater driving force inducing the hydrate

formation of the carbon dioxide.

For our computational model and

simulation, we are able to represent the water

and carbon dioxide system using Lennard-

Jones and Coulombic interatomic potentials

109

and a harmonic bonding style. However, the

timescale of the simulation (about 10

microseconds) is much less than the time

required for hydrate formation, which is on

the order of one day. This was a necessary

restriction because the simulation time

representing this actual timescale would be

infeasibly long with our current model and

resources.

5. Conclusion

We were successful in detecting the

hydrate formation of carbon dioxide in a

pressure vessel at 420 and 550 psi. We

detected a pressure drop at the time of hydrate

formation, which was approximately twelve

hours after initiation for both trials. In

addition, we created a computational model

of the CO2 and water system. This model may

be used for future work, but in the present it

is unrealistic for us to simulate the full

hydrate formation with our modeling

parameters because of the large time relative

to the atomic scale. In future experiments,

with the addition of saline water and porous

silica we expect that the time of formation

would be less than that for our current

findings. In future experiments, we plan to

use saline water as well as porous silica glass

to more closely emulate ocean floor

conditions. In addition, we will perform

experiments at BNL and use XRD to confirm

our results. Overall, an understanding that the

nucleation time for these carbon dioxide

hydrates is approximately twelve hours will

allow for further development of the hydrate

formation process. This can then be

optimized for ocean-floor conditions with the

saline water and silica trials, eventually being

tested simultaneously with methane hydrate

formation.

Acknowledgements

We would like to express our sincere

gratitude to Mani Kuntal Sen for his guidance

and mentorship in the laboratory.

Additionally, we would like to acknowledge

Brookhaven National Laboratory for access

to their facilities and the 28-ID beamline at

NSLS II.

References

[1] United States. Energy Information

Administration. Office of Energy Analysis.

International Energy Outlook 2016. N.p.,

May 2016. Web.

[2] Kimantas, Janet. "More Methane

Surprises." AJ – Canada's Environmental

Voice. Alternatives Journal, Dec. 2014.

Web.

[3] Uchida, Tsutomu, Takao Ebinuma, and

Takeshi Ishizaki. "Dissociation Condition

Measurements of Methane Hydrate in

Confined Small Pores of Porous Glass." The

Journal of Physical Chemistry B 103.18

(1999): 3659-662. Web.

[4] Jacobson, Liam C., Waldemar Hujo, and

Valeria Molinero. "Nucleation pathways of

clathrate hydrates: effect of guest size and

solubility." The Journal of Physical

Chemistry B 114.43 (2010): 13796-13807.

Web.

[5] Horvat, Kristine, and Devinder Mahajan.

"Carbon dioxide-induced liberation of

methane from laboratory-formed methane

hydrates." Canadian Journal of Chemistry

93.9 (2015): 998-1006.

[6] Humphey, W., Dalke, A. and Schulten,

K., VMD - Visual Molecular Dynamics", J.

Molec. Graphics, 1996, vol. 14, pp. 33-38.

[7] Koga, Tadanori, et al. "Hydrate

Formation at the Methane/Water Interface

on the Molecular Scale." Langmuir 26.7

(2010): 4627-4630.

[8] “Vapor Pressure of Carbon Dioxide”.

Wolfram Alpha LLC. 2016. Wolfram|Alpha.

[9] “Bond Distance of Water”. Wolfram

Alpha LLC. 2017. Wolfram|Alpha.

[10] “Bond Distance of Carbon Dioxide”.

Wolfram Alpha LLC. 2017. Wolfram|Alpha.

[11] W.E. Acree, Jr., J.S. Chickos, "Phase

Transition Enthalpy Measurements of

110

Organic and Organometallic Compounds.”

NIST Chemistry WebBook, NIST Standard

Reference Database Number 69. Eds. P.J.

Linstrom and W.G. Mallard, National

Institute of Standards and Technology,

Gaithersburg MD, 20899. Web.

[12] Mohr, Peter J., and Taylor, Barry N., J.

Phys Chem. Ref. Data 28, 1713, 1999; Rev.

Mod. Phys. 72, 351, 2000. Web.

[13] Molecular dynamics simulation of CO2

hydrates: Prediction of three phase

coexistence line. Web.

[14] S. Plimpton, Fast Parallel Algorithms

for Short-Range Molecular Dynamics, J

Comp Phys, 117, 1-19 (1995). Web.

[15] Sloan, E. Dendy. CSMHYD. Computer

software. Center for Hydrate Research.

Colorado School of Mines, 26 Aug. 1998.

Web.

Process Optimization for a Wood Stove with a Combustion Catalyst

Yunxiao Shawn Jiang1, Kevin J. Lee1, and DongNi Qiu1

1. Chemical and Molecular Engineering Program, Materials Science and Chemical Engineering Department,Stony Brook University

Abstract Incomplete combustion of wood releases a substantial amount of carbon monoxide (CO), which is hazardous to human health. One retrofit option to reduce CO emission is the installation of a combustion catalyst. The operating condition of the catalyst affects its performance, hence particular attention is placed on the process optimization of wood stoves on improving the heating efficiency and decreasing the CO emission. In this study, a combustion catalyst was added into a wood stove that was modified with baseline features which included an airflow system (AFS) and dividers. The dividers increased the residence time of the combustion gases and slowed down the burn rate while the AFS provided better combustion gases mixing, and the combustion catalyst resulted in improved CO emission. Both the dividers and AFS features increased heating efficiency as well as CO emission. The configuration with AFS, dividers, and the combustion catalyst saw a decrease in time weight average CO (TWA-CO) concentration of 3682 ppm, a 25% decrease compared to the AFS and dividers configuration and simultaneously maintained the heating efficiency achieved from the AFS and divider configuration.

Keywords: wood stove, combustion catalyst, air flow systems, dividers, residence time, turbulence

1. Introduction During wood combustion,

specifically incomplete combustion, a substantial amount of CO is released into the air. Exposure to CO can be fatal since it deprives the body of oxygen and even when oxidized to CO2, it is still a greenhouse gas [1,2]. Limiting CO emissions can help protect human health as well as the environment. Additionally, standards for wood stoves emissions are being updated in various parts of Europe allowing for less CO emission [3].

New pellet stoves and wood stoves have improved performance compared to old wood stoves in order to meet these new standards [4]. Some ways that new stoves

improve their performance compared to old wood stoves include better mixing of fresh air and combustion gases, sufficient amounts of oxygen inside the stove, and optimal temperature inside the stove. Many different retrofit modifications are also available to improve the performance of an existing wood stove. One promising retrofit modification is to install a combustion catalyst into the stove. The purpose of installing a combustion catalyst into the wood stove is to use small quantities of noble metals, usually platinum, palladium or rhodium, on the surface of the catalyst as oxidation sites to effectively oxidize pollutants, such as CO [5]. Installing a catalyst into a domestic wood stove/boiler is

111

a viable approach in reducing emissions because of its low complexity and high compatibility. Many studies have been done to determine the viability of using a combustion catalyst. For example, the study by Hukkanen et al. showed that using a combustion catalyst can reduce emissions by oxidizing CO [6]. The catalyst used was a set of three metal wire mesh nets that consisted of platinum and palladium, encircled by a steel frame for inserting and removing from the flue stack [3]. CO emissions were reduced by 21% during the whole combustion process, and by 79% during the burn-out (the end of combustion) stage [3]. Another study with platinum cerium palladium catalyst on a wood stove showed a greater reduction of 70% for CO [3]. When considering the viability of using a combustion catalyst, how the catalyst is operated can affect performance. Studies have shown that the performance of a combustion catalyst is affected by how it is operated which affects its heating efficiency and CO emissions [7]. Side reactions may also occur inside the catalyst and must also be considered. Kaivosoja et al. showed that the side reactions that occur in the catalyst can actually increase polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) emissions [8]. Therefore when considering the benefits of using a combustion catalyst to reduce CO, other emissions should also be taken into consideration. Additionally, the composition of the catalyst can also affect its usefulness. The study by Sudhanshu Sharma and M. Hegde looked at how to produce a three way catalyst and how it performed [9]. The catalyst used in their study was a “Ce0.98Pd0.02O2-𝛿 on γ-Al2O3,” the catalyst achieved 100% conversion of CO using a space velocity of 21,000 h-1 and below a temperature of 245 °C [9]. Lastly, process of operating a

catalyst inside the wood stove may further be optimized by installing additional features, such as a preheater for the catalyst or secondary air inlet, inside the wood stove to improve its performance.

In this study, four configurations of the wood stove were tested for the highest heating efficiency and lowest CO emission: the unmodified wood stove, the wood stove with dividers, the wood stove with AFS and dividers, and the wood stove with AFS, dividers, and catalyst. CO and O2 concentrations and flue stack temperature

were measured during the burn testsT stack and heating efficiency was calculated using the Stack Loss Method (SLM). 2. Material and Methods 2.1 Materials

Figure 1. Overview of the testing station

The wood stove used is an all cast iron, updraft, front loading stove, it has a height/width/depth of 32/28.7519.5 in. and a maximum heat output of approximately 50,000 Btu/hr, dependent on how operated [9]. A Testo 330 Flue gas analyzer was used

112

to measure CO, CO2, and O2 concentration, exhaust temperature, and draft in the flue stack through the gas analyzer port that was 3 ft above the top of the stove. The measuring range for CO was from 0 to 8000 ppm, with resolution of 1 ppm and accuracy of ± 10% of measured value; the measuring range for CO2 was from 0 to 10000 ppm, with accuracy of ± 3% of the measured value; the measuring range for O2 was from 0 to 21 vol.%, with resolution of 0.1 vol.% and accuracy of ± 0.2 vol.%. The measuring range for temperature was from -40℃ to 1200℃, with resolution of 0.1℃ and accuracy of ± 0.5℃ when measured temperature is between 0 to 100 ℃ and ± 0.5% of the measured value at the rest of range.

Approximately 20 ± 1 lb of red oak wood was used for the burn tests. Each wood log was cut into pieces of 9 inches in length, weight ranging between 1 - 4.5 lb. Moisture content was measured by Delmhorst BD-10 Pin Analog Wood Moisture meter one day in advance of the burn test. The moisture content of all wood logs fell into the range of 8 ± 1%. Wood logs were stacked uniformly and equally in weight in both chambers during the burn tests.

2.2 Dividers

Figure 2. Diagram showing the dividers and

divided firebox

The dividers separated the firebox into two equal-sized chambers for fuel load and exhaust, allowing separate ignition and forcing longer path lengths of combustion gases. The dividers were made of stainless steel. The air inlet allowed air into the firebox from the bottom of the first chamber, and combustion gases had to travel through two sharp turns to reach the outlet. The addition of dividers improved the turbulence and mixing of combustion gases and increased the residence time inside the firebox. 2.3 AFS

Figure 3. Diagram showing the components and

airflow of the AFS

The AFS was designed to increase the draft in firebox and thus increase the oxygen supply for complete combustion. The AFS was placed in the flue stack, above the gas analyzer port and injected a stream of fast moving air upwards by a blower through the venturi tube. The forced air inflow created a vacuum upstream in the flue stack and induced additional air intake in the firebox. Proof of concept experiments were done on a firebox model in lab to verify the induced draft. The additional air intake increased oxygen supply for complete combustion. It also resulted in a faster flow rate of combustion gases through the

113

firebox, which promoted better mixing in the stove. 2.4 Catalyst

A 2’’ 5.66’’ honeycomb catalyst × was obtained from FireCat Combustors, model Combustor ACI-43C. A small piece of the catalyst was taken for EDX analysis. 2.5 Burn Test

The wood stove was set up on a test stand (see Figure 1). The air draft inducer was used to regulate the air intake of the wood stove. Dividers were located inside the combustion chambers to be used to promote better mixing inside the wood stove as well as creating a secondary combustion. A combustion catalyst was installed along with a catalyst bypass arm control such that the catalyst could be inserted into and removed out of the direct path of the flue gas externally during the burn test in the back of the wood stove. Within the flue stack, Testo was inserted into the gas analyzer port to record CO and O2 concentrations. The gas analyzer port was located three feet above the top plate of the wood stove. In addition, the mass of wood loaded into the wood stove was recorded using the scale on the test stand just below the wood stove in order to standardize the test duration. For the AFS+Dividers+Catalyst configuration, the catalyst was inserted into the wood stove when the outside wall temperature of the first chamber reached 200 ℃.

Data was recorded for CO and O2 concentration and flue stack temperature every five minutes through the gas analyzer port using Testo gas analyzer and LabVIEW. The test was terminated when the amount of wood dropped below 10% of the initial weight.

2.6 Heating efficiency

The heating efficiency was determined using the Stack Loss Method, which estimates the heating efficiency of the wood stove by the flue stack temperature and the mass of dry O2, , inside the OD 2 flue stack [11].

is calculated by:OD 2

(EQ. 1) 4.7365 5.731 DO2 = 1 × O2%21%−O2%

+ 1

Which determines and , where LDG LW G and are the dry gas heat and wetLDG LW G

gas heat losses in the flue stack respectively:

(EQ. 2).001033 O T 0) LDG = 0 × D 2 × ( stack − 7

(EQ. 3).482 .004351 LW G = 9 + 0 × T stack The efficiency, HE, is then given by [11]:

E 00% L % H = 1 − DG − LW G − 1 (EQ. 4) Both O2% and were measured during T stack the burn test at a 5 minute interval. 2.7 Scanning Electron Microscope (SEM) for catalyst characterization

An SEM analysis was done to determine the composition of the catalyst used. The catalyst sample was taken from the catalyst by using metal pliers to chip off a piece of the catalyst. The sample was mounted on carbon tape and was spot coated with a 2 nm thick gold palladium coating to improve the conductivity of our sample. The SEM was done using a focused ion beam scanning electron microscope (ZEISS Crossbeam 340). During the SEM, eight different locations on the catalyst sample were observed. Electron images, EDS layered images, and a map sum spectrum was acquired for each of the eight locations.

114

3. Results and Discussion 3.1 Characterization of the Catalyst

Energy Dispersive X-ray Spectroscopy (EDS) layered images of the catalyst sample revealed how elements are dispersed along the surface of the catalyst.

Figure 4A-E. Electron Image from SEM test of catalyst, EDS layered images of Al, O, Na, and Ce

respectively

Figure 4A showed an electron image of sampled location on the catalyst surface while EDS layered images of the location revealed how different elements were concentrated on the catalyst surface. At this location, O and Al could be found in most places on the surface of the catalyst. This leads to the conclusion that O and Al are part of the inert support of the catalyst. Meanwhile Na was concentrated in small

clusters where Al and O was not present. This may be due to Na being introduced onto the catalyst surface as a contaminant and entering crevices along the surface of the catalyst. Very little Ce was found on the catalyst surface; however, figure 4E shows traces of Ce dispersed throughout the surface of the catalyst suggesting that it is part of the noble metal washcoat.

Figure 5. EDS analysis of the catalyst sample

115

Figure 5 showed that the catalyst sample had negligible amounts of many different elements. Some of these elements could be contaminants on the surface of the catalyst. For example, Fe might have originated from the metal pliers that were used to acquire the catalyst sample. Additionally, the sample was mounted using carbon tape, so carbon could have been introduced to the sample as well. While contamination may have been present on the

surface of the catalyst, the presence of Ce, Al, and O suggests that the catalyst used in this test may be similar to the one used by Sudhanshu Sharma and M. Hegde which had a Ce0.98Pd0.02O2-δ wash coat and a monolith consisting of γ-Al2O3 [9]. Since the composition of the catalyst used here is similar to the one used by Sharma and Hedge, the catalyst should also have similar characteristics, such as CO conversion rates.

Figure 6. CO concentrations of tests with different configurations

116

Figure 7. O2 concentrations of tests with different configuration

3.2 CO Emission and O2 Concentration

The graphs of CO emission and O2 concentration are shown above for each of the configurations and combustion phases are labeled with numbers and dash lines. Phase 1 and 2 are the start-up phases (ignition of the first chamber) of the AFS+Dividers configuration and Catalyst configuration respectively. Phase 3 and 4 represent the ignition of the second chamber for the AFS+Dividers configuration and Catalyst configuration respectively.

From the CO emission and O2 concentration data (Figures 6 and 7), there were multiple sharp decreases during the burn. Comparing Figures 6 and 7, nearly every sharp decrease in O2 corresponds to an increase in CO caused by the incomplete combustion of individual wood logs. The data from the AFS+Dividers, Dividers, and Catalyst configurations verified the

conclusion where oxygen decreases can be observed between 50 - 70 minutes when the second chamber was reported to be flaming from visual observation and temperature monitoring. Incomplete combustion resulted in higher CO due to insufficient combustion time, low temperature, and poor mixing between oxygen and the combustion gases. The dividers addressed the problems of insufficient time and poor mixing by increasing the residence time and turbulence inside the firebox. The AFS increased draft into the firebox, generated more turbulence, and therefore assisted the combustion.

All three modified configurations showed three major trends: the start-up phase, second chamber ignition phase, and the burn-out phase. The start-up phase from 0 - 20 minutes had high CO emission (see Figure 6) from incomplete combustion caused by low temperature (cold start) of the

117

wood stove. The CO emission decreased as temperature of the wood stove increased. A significant increase in CO emission occurred at 40 - 80 minutes due to the start-up phase of the second chamber. Temperature decreased as the burn test reached the burn-out phase. This resulted in another increase of CO emission that was lower than the two start-up increases in CO emission due to less wood mass.

The unmodified wood stove had the lowest overall CO emission compared to the other three modified configurations. Additionally, it had a relatively high overall O2 concentration due to the lack of dividers, which enabled more airflow despite the absence of AFS. Another reason for the high O2 concentration was due to the unmodified stove having a high burn rate. Figure 7 showed that after 50 minutes into the test, O2 concentration began increasing. This was because after 50 minutes, the burn test reached the burn out phase. The low CO emission from the unmodified test was because the unmodified stove reached higher temperatures than the other stove configurations which allowed for more CO to be converted into CO2.

The dividers configuration had less CO emission than the unmodified stove after the ignition of second chamber (60 - 80 minutes)because the combustion in the second chamber acted as an afterburner for the emissions from the first chamber, converting CO into CO2. Turbulence of combustion gases inside the firebox increased due to the introduction of dividers changing the pathway of air. The dividers also increased gases residence time inside the wood stove. The incomplete combustion caused by low temperature was most notable during the cold start phase of the tests. The addition of dividers created a second ignition which was a warm start due to the increased stove temperature. The O2

concentration after the second chamber ignition (60-80 minutes) was lower than the case when the first chamber was ignited (20-40 minutes). Similarly, the CO concentration after the ignition of second chamber (60 - 80 minutes) was lower than after the ignition of the first chamber (20-50 minutes). However, the drawback was that the overall oxygen concentration was low when compared to the unmodified stove. In order to increase oxygen concentration inside the wood stove, the AFS was installed along with the dividers.

The AFS+Dividers configuration had higher CO concentration compared with the dividers and the unmodified configurations. Two peaks could be observed during 20 - 40 minutes and 60 - 80 minutes, which correspond to the ignition of two chambers. The induced airflow through the stove helped better mixing of combustion gases in the stove, but shortening the residence time of gases. The disadvantage could be resolved by adjusting the power of AFS during the burn-out phase, but for the consistency of burn tests, AFS was kept at the maximum power throughout the tests. The higher O2 concentration, especially during 20 - 60 minutes, demonstrated the AFS was functioning as intended. Therefore, an addition of the catalyst could be used to oxidize CO.

The catalyst configuration had an overall lower CO emission than the AFS+Dividers configuration. The wall of the first chamber reached 200℃ approximately 15 minutes into the test, at this time the catalyst was moved into the pathway of the gas inside the wood stove at this time. Approximately 40 minutes into the test, there was a drastic decrease in O2 concentration in the wood stove. This is likely due to the second chamber igniting and the oxidation reaction occurring inside the catalyst. During this time, lower CO

118

emissions compared to the AFS+Dividers configuration were observed, likely due to oxidation of CO by the catalyst. However, the catalyst used during the test did not perform as well as the catalyst used by Sharma and Hedge. At 220℃, the catalyst used by Sharma and Hedge achieved 100% CO conversion, while the catalyst used in this test achieved a maximum drop in CO concentration of 2000 ppm or ~25% [9]. This could be due to the catalyst not being

sealed properly allowing gases to travel out of the wood stove without having to go through the catalyst. Additionally, CO emissions during the burnout phase were stable at 3000 ppm suggesting the catalyst was not active during burnout phase. The TWA-CO for AFS+Dividers and Catalyst configuration were 4612 and 3682 ppm respectively. The 25% decrease confirmed the effectiveness and aligned with the literature data (21%) [3].

Figure 8. Heating efficiency of tests with different configuration 3.3 Heating Efficiency

Figure 8 showed an overall decreasing trend of the heating efficiency as a function of time. Phases of all configurations are labeled with numbers and dash lines. Phase 1 and 2 represent the start-up phase of the AFS+Dividers configuration and the Catalyst configuration

respectively. Phase 3 and 4 represent the ignition of the second chamber for the AFS+Dividers configuration and Catalyst configuration respectively. Lastly, phase 5 represents the burn out phase of all configurations.

All three modified configurations showed an increase in heating efficiency at the onset of the second chamber, which

119

suggested dividers improve the heating efficiency of the wood stove. Meanwhile, the unmodified wood stove had lower heating efficiency when compared to each of the three modifications done. This is because there was no staged combustion in the unmodified stove and all of the wood was available for combustion at the beginning of the burn. It also further verifies the high temperature inside the flue stack of the unmodified configuration by equation 2 ~ 4 where high flue stack temperature corresponds to more CO oxidation and less heating efficiency. Therefore, combustion occurred at a much faster rate than staged combustion which decreased CO emissions but also decreased heating efficiency.

The increase in heating efficiency during the Dividers test compared to the unmodified configuration was due to staged combustion. Since not all of the wood was immediately available for combustion, the burn was prolonged, and maintained a higher heating efficiency. Figure 8 shows a jump in heating efficiency from 60-80 minutes into the test, this correlates to the second chamber of wood igniting during the test.

The AFS+Dividers and Catalyst configurations overlapped with each other throughout most time during the test, and their heating efficiency were higher than the unmodified and divider only configurations. The improved performance in efficiency was attributed to AFS as it increased the flowrate of gas into and out of the firebox, therefore lowered the temperature of the flue gas, which resulted in higher efficiency. The addition of a combustion catalyst caused additional exothermic reactions when oxidizing volatiles and CO. However, the Catalyst configuration did not produce significantly higher efficiency compared with AFS+Dividers, because the temperature of the flue gas had not reached

the optimum temperature required for the catalyst. From the data, the catalyst had noticeable better heating efficiency from 100 minutes onward when compared to the AFS+Dividers configuration from the blockage of the catalyst. 4. Conclusion

From the results of testing different configurations of the wood stove, the AFS+Dividers configuration provided the best heating efficiency, but also emitted the most CO. The unmodified wood stove gave the lowest CO emissions, but also had the lowest heating efficiency. Using the catalyst with the AFS and dividers provided high heating efficiency while emitting significantly less CO than the AFS and dividers modifications. Therefore, using a combustion catalyst with an AFS and dividers would be the optimal configuration for retrofitting an old wood stove.

Acknowledgements Funding for this project is provided

by the New York State Energy Research and Development Authority (NYSERDA award № 63043). Special thanks to NYSERDA and Robert Carver, Brookhaven National Laboratory and Dr. Tom Butcher, Stony Brook University, Jake Lindberg and Professors Devinder Mahajan, Sotirios Mamalis, Benjamin Lawler, Jon Longtin, and Simon Chung-Chueh Chang. References [1] Veronesi, Bellina, et al. "Particulate Matter Inflammation And Receptor Sensitivity Are Target Cell Specific." Inhalation Toxicology 14.2 (2002): 159-183. Academic Search Complete. Web. 16 [2] Environmental Protection Agency (EPA), Air Sources Emissions

120

[3] Ozil, F., Tschamber, V., Haas, F., Trouvé, G., 2009. Efficiency of catalytic processes for the reduction of CO and VOC emissions from wood combustion in domestic fireplaces. Fuel Processing Technology 90, 1053e1061. [4] Ozgen, Senem, et al. "Emission Factors from Small Scale Appliances Burning Wood and Pellets." Atmospheric Environment, vol. 94, Sept. 2014, pp. 144-153. EBSCOhost, doi:10.1016/j.atmosenv.2014.05.032. [5] Bensaid, S., et al. "After-Treatment of Household Wood-Fired Stove Emissions: From Catalyst Formulation to Full-Scale System." Catalysis Today, vol. 197, no. 1, 15 Dec. 2012, pp. 76-89. EBSCOhost, doi:10.1016/j.cattod.2012.06.026. [6] Hukkanen, A., et al. “Reduction of Gaseous and Particulate Emissions from Small-Scale Wood Combustion with a Catalytic Combustor.” Atmospheric Environment, vol. 50, Apr. 2012, pp. 16- 23. EBSCOhost, doi:10.1016/j.atmosenv.2012.01.016. [7] Thran, Daniela, et al. "Improvement Of Efficiency And Emissions From Wood Log Stoves By Retrofit Solutions." Chemical Engineering & Technology 2 (2017): 340. Academic OneFile. [8] Kaivosoja, T., et al. "Effects of a Catalytic Converter on PCDD/F, Chlorophenol and PAH Emissions in Residential Wood Combustion." Chemosphere, vol. 88, no. 3, July 2012, pp. 278-285. EBSCOhost, doi:10.1016/j.chemosphere.2012.02.027. [9] Sharma, Sudhanshu and M. Hegde. "Single Step Direct Coating of 3-Way Catalysts on Cordierite Monolith by Solution Combustion Method: High Catalytic Activity of Ce0.98Pd0.02O2-δ." Catalysis Letters, vol. 112, no. 1/2, Dec. 2006, pp. 69-75. EBSCOhost, doi:10.1007/s10562-006-0166-z.

[10] “Vermont Castings Operation Manual.” Hearth, https://www.hearth.com/i mages/uploads/vcresimanual9_17.pdf. 7 May 2017. [11] “Fired Steam Generators”, Performance Test Code 4: 2008, ASME, New York, NY; the US std. since 1998.

121

122

In-situ Water Management for the Optimization of

Methanol Dehydration to Dimethyl Ether

Jillian Gannon 1, JinYing Lin 1, Danielle Wahl 1

1. Chemical and Molecular Engineering Program, Stony Brook University, Stony Brook NY

11794

Abstract

The production of Dimethyl ether (DME) on a large scale is urgent as it is a promising

clean fuel. This study focuses on DME production through methanol dehydration, which is

catalyzed by a solid acid, typically γ-alumina (γ-Al2O3). Previous studies have found that the

byproduct water inhibits methanol dehydration through competitive adsorption with methanol on

the active sites of the catalyst. This poisons the catalyst and renders it inactive. In several

published papers, simulations of in-situ water removal in methanol dehydration, which is

designed to be accomplished by sweep-gas or membrane separation in fixed bed reactors, show

that methanol conversion is improved with in-situ water removal. Sweep-gas and membrane

separation methods cannot be performed in batch reactor so it was determined that using a water

absorbent/adsorbent might be a solution for solving this issue. Molecular sieves have been found

to be an excellent water adsorbent. This study aims to determine the ability of molecular sieves

that have a 5Å pore size to adsorb the water as it is generated in the batch reactor experiment in

order to improve the conversion of methanol to DME. In each of the nine experimental runs, the

reactor pressure was found to increase isothermally at a stable set temperature of 280 °C and

analyzing the product using gas chromatography (GC) gave results that proved that DME was

produced. The GC results also helped to determine the methanol conversion baselines: the γ-

Al2O3 baseline as 26.7% ± 8.8%, the molecular sieve baseline as 25.5% ± 6.2%, and the

experiment, ran with both γ-Al2O3 and molecular sieves, as 47.5% ± 8.6%. The results proved

that the molecular sieves improved methanol conversion. However, DME production data is

needed for further evidence that the improvement in the conversion of methanol to DME was

achieved by molecular sieves.

Keywords: Dimethyl ether, methanol dehydration, water removal, molecular sieves.

1. Introduction

Due to an increasing energy demand

and a reduction in the supply of fossil fuels,

focus has shifted to the use of bioenergy to

replace other energy sources. Due to its

abundance and renewable properties, it has

been shown that biomass could be a viable

source for energy [1]. Dimethyl ether

(DME) is a promising clean fuel alternative

with similar thermal efficiencies to diesel

fuel. At ambient conditions, DME is a

colorless, non-toxic, highly flammable gas.

It can be handled as a liquid when slightly

pressurized [1][2]. Compared to traditional

diesel fuel, DME has low NOx emissions,

almost no smoke and less engine noise. In

addition, DME is a useful chemical

123

intermediate for the preparation of many

important chemicals including dimethyl

sulfate and high value oxygenated

compounds [1][2]. Moreover, DME has

been used as an aerosol propellant to replace

chlorofluorocarbons which can destroy the

stratospheric ozone layer [3]. Consequently,

the utilization of DME is environmentally

favored.

DME can be produced through

synthesis directly from CO, CO2, and H2,

known as synthesis gas or syngas, or by

methanol (MeOH) dehydration catalyzed by

a solid acid, typically γ-alumina. For several

decades, the production of DME has been

focused on being synthesized directly from

syngas. Because of this, the role of the

active components and the reaction

mechanism is well understood. However,

the catalytic dehydration of methanol to

DME has not been as well studied [1].

Figure 1: Production of DME from syngas [10]

Many catalysts have been used and

modified to optimize the production of DME

from either syngas or methanol such as H-

ZSM-5 and Cu/ZnO/Al2O3 [1]. The problem

with the production of DME from methanol

or syngas is the byproduct of water shown in

Figure 1. Two methanol molecules combine

over a catalyst, which is typically γ-alumina

(γ-Al2O3), to form DME with water as a

byproduct. The water poisons and

deactivates the catalyst by blocking the

active sites on the catalyst and preventing

the consumption of methanol through

competitive adsorption. This results in lower

DME production and shortened catalyst

bedtime [1].

To address this issue, in-situ water

removal is proposed to improve the yield of

DME and avoid the poisoning of the

catalyst. Due to the impact water has on the

reaction, various methods have been

analyzed to limit the amount of water

present in the reaction. Many published

papers have shown that limiting the amount

of water present in the reactor can improve

the conversion of methanol to DME when

using methods such as the sweep-gas

method in fixed bed reactors, membrane

separation method in membrane reactors

with hydrophilic membranes, or the

combination of membrane and sweep gas

[4][11]. In one simulation, methanol

dehydration using a membrane reactor was

modified with a high water perm-selective

membrane and a conventional membrane

reactor. The result showed methanol

conversion in the conventional membrane

reactor was 68%, while with the modified

membrane reactor it was 82.5% [4]. The

methods proposed in these previous papers

are not applicable for a batch reactor,

however these results suggest that inorganic

water absorbents or adsorbents may be a

suitable component useful for water removal

in a batch reactor.

Molecular sieves have shown to

perform well in the adsorption of water.

124

Molecular sieves are a type of desiccant

primarily composed of alumina, silica, and

other metal oxides, with uniform pore-size

distributions. They are widely used in

heterogeneous catalysis [9]. It has been

shown that using molecular sieves alongside

a catalyst in the production of DME from

methanol can not only improve diffusion in

the pores, but also improve activity,

selectivity, and stability [6]. Additionally,

molecular sieves have been used to separate

water-based azeotropic mixtures such as

ethanol in water and isopropanol in water

[7]. Molecular sieves of 5Å diameter are

often used in the petroleum industry

specifically for separating compounds in

chemistry laboratories and drying starting

materials in a reaction. Due to the precise

and uniform sized pores, these molecular

sieves are commonly used as adsorbents for

both gases and liquids [9].

Our study focuses on the in-situ

water management for the optimization of

methanol dehydration to DME using γ-

Al2O3. We hypothesize that in situ water

removal can be assisted by using a

molecular sieve 5A desiccant which can

improve the conversion of methanol to DME

when using a batch reactor. To accomplish

in-situ water removal in a batch reactor,

molecular sieves with a 5 Å pore size were

used as a water adsorbent. Such molecular

sieves have been found to be particularly

suitable to absorb water [8]. In this study,

molecular sieves of a 5 Å diameter were

placed in a batch reactor in order to limit the

amount of water present to prevent the

poisoning of the catalyst. Methanol

dehydration reactions were run both with

and without the water adsorbent. The

methanol conversions of these products

were compared in order to confirm that

molecular sieve use improves the conversion

of methanol to DME.

2. Materials and Methods

2.1 Chemicals

Methanol (99.8%, anhydrous, 322415)

and polyethylene glycol (PEG-400, 81172),

were purchased from Sigma-Aldrich and

were used as the reactant and solvent,

respectively, for the synthesis of DME in a

standard batch reactor. Commercial gamma-

alumina, purchased from Acros Chemicals,

was used as a catalyst. Molecular sieves (5

Å, 8 to 12 mesh, 197295000), purchased

from Acros Chemicals, were used as water

adsorbents. Nitrogen gas, purchased from

Airgas, was used for purging and

pressurizing the system. DME (≥99%,

anhydrous, 295299), purchased from Sigma

Aldrich, was used as a reference sample for

GC-TCD analysis.

For GC-TCD analysis, Helium gas,

purchased from Airgas, was used as the

carrier. The molecular sieves 5A and γ-

Al2O3 were dried in a Thermo Scientific

HeraTherm oven at 120 °C before being

used for moisture removal.

2.2 Batch Reactor Setup

The batch reactor used to run all

experiments was a Parr model 4547 batch

unit fitted with a 300 mL pressure vessel.

This batch unit was connected to a model

4848 process controller and a Parr model

4831 temperature controller. The batch

reactor setup is shown in Figure 2 below. In

each run, the reaction took place in the

reactor vessel (A) that connected to the

125

reactor head (B) and placed on the reactor

stand (C). The reactor controller (D)

controlled the stirring speed and monitored

the reactor conditions. The heating mantle

(E) was connected to the temperature

controller (F) to control the temperature of

the system due to heating mantle

incompatibility with the reactor controller.

As shown in Figure 3, the reactor head had a

pressure transducer (1) and an automatic

stirrer (2), which was connected to the

reactor controller, a secondary pressure

gauge (3), and two thermocouples (4). One

of the thermocouples was connected to the

reactor controller for reporting the system

temperature and the other was connected to

the temperature controller for controlling the

temperature of the system. The reactor head

also included a gas inlet (5), gas outlet (6),

and dip tube (7).

Figure 2. Parr batch reactor setup Figure 3. Reactor head setup

Table 1. Type and amount of chemicals used in the three experiments

Experiment # 1 (Control Group 1) 2 (Control Group 2) 3 (Experimental Group)

Description γ-Al2O3 Baseline Molecular Sieves

Baseline

Experiment

Methanol (mL) 10 10 10

PEG-400 (mL) 60 60 60

γ-Al2O3 (g) 1.0 0 1.0

Molecular Sieve 5A (g) 0 11.0 11.0

1

2.3 Synthesis of Dimethyl Ether

Using the batch reactor, three

experiments were run using the same

quantities of methanol and PEG-400, but

with differing amounts of γ-Al2O3 and

molecular sieves. Specific amounts are listed

in Table 1. Each experiment was run three

times. In the experimental run, 60 mL of

PEG, 10 mL of methanol, 1.0 g of γ-Al2O3

and/or 11.0 g of molecular sieves were

placed into the 300 mL vessel. The reactor

was sealed and purged with N2. During

purging, the stirrer was turned on to ensure

that the dissolved air leaves the system.

After purging, the reactor was pressurized to

64 psig using N2 gas. The mixture was

stirred for fifteen minutes and an initial

liquid sample was obtained from the vessel

through the dip tube valve. Each reaction

started with initial conditions of 25 °C, 60

psig, and 300 rpm. Temperature and

pressure were monitored using the reactor

controller and were recorded every fifteen

minutes throughout each experiment. All

experiments were run isothermally at 280 °C

for three hours. After three hours, the batch

reactor was cooled to room temperature and

a final liquid sample was obtained. A gas

sample was obtained from the gas outlet

valve.

2.4 Methanol Adsorption Test

In order to determine the amount of

methanol that would be adsorbed by the

molecular sieves, 6 mL PEG-400, 1 mL

methanol, and 1.1 g molecular sieve were

added to a small glass vial. In another glass

vial, 6 mL PEG-400 and 1 mL methanol

were added. These two vials were stirred

and placed in the sonicator bath until the

mixture became uniform. Methanol

concentration in the vials was measured by

GC after a three-day period.

2.5 Calibration for Methanol

concentration and peak area

In order to calibrate the methanol

peak area from the GC results and the

methanol concentration in the sample, five

methanol/PEG-400 samples containing

different methanol concentrations were

prepared by mixing different volumes of

methanol with 6 mL PEG-400 in small vials.

The samples were placed in the sonicator

bath until the mixture became uniform.

2.6 Gas Chromatography (GC)

A Gow Mac series 580 Gas

Chromatograph with Thermal Conductivity

Detector (TCD) (column 8’, packing 0V101)

was used to measure the methanol and DME

concentrations in the samples. Helium gas

was used as the carrier gas with flow rate

measured by the ProFLOW 6000 from

Restek and was adjusted to 20-30 mL/hr.

The GC injector temperature, detector

temperature, and column temperature were

set to 150 °C, 190 °C, and 150 °C,

respectively. The detector current was set to

200 mA. For data acquisition and analysis,

the Clarify PC chromatography software

was set to collect the voltage output data for

10 minutes. For liquid samples, a 5 μL

sample was measured using a 10 μL

Hamilton model gas tight 1801 glass syringe

and was used to inject the sample into the

injector port. In the analysis of gas samples,

the sample size was 5 μL initially, but was

later increased to a sample size of 0.5 mL.

After a sample was injected into the GC, the

127

2

software was started for data acquisition.

Samples of pure methanol and pure DME

were used for calibration because GC is

based on comparative analysis. For each

sample, three measurements were made.

3. Results and Discussion

3.1 Batch Reactor Run Data

For the purpose of this study, the γ-

Al2O3 (catalyst) baseline is defined as

Experiment 1 and the molecular sieve

baseline is defined as Experiment 2. Three

runs were performed for each of the three

experiments, totaling nine experimental

runs. In each experimental run, reactor

temperature and pressure were recorded

every 15 minutes, as shown in Figure 4.

In Figure 4a, the reactor temperature

reached the desired set point of 280 °C in

roughly 30 minutes and generally

maintained that temperature for the

remainder of the reaction. In Figure 4b, the

reactor pressure increased sharply in the first

half hour due to the quick increase in

temperature. After t = 30 minutes, pressure

continued to increase gradually and then

leveled off at t = 90 minutes.

The increase in reactor pressure after

the temperature stabilized at the set point

indicated that methanol was converted to

DME. This is due to the fact that the vapor

pressure of DME is much higher than that of

methanol. When the pressure leveled off and

even slightly dropped, it indicated that the

equilibrium was reached and the reverse

reaction took place. Additionally, the results

show that the experiment time could be

shorten to 2 hours due to the leveling off of

the pressure.

128

2

Figure 4. Reactor Temperature (a) and Pressure (b) as a function of time for nine runs, Dotted lines with

square markers represent runs for Experiment #1 Catalysis Baseline, Dashed lines with diamond markers

represent runs for Experiment #2 Molecular Sieve Baseline, Solid lines with circular dot markers

represent runs for Experiment #3

3.2 GC Results

GC-TCD was used to determine the

methanol concentration in the liquid samples

and the DME concentration in the gas

samples. Pure methanol and DME samples

were used as references. Figure 5 shows the

voltage output was collected during GC data

acquisition. The software recorded the peaks

and reported the retention time, peak

intensity, and peak area. The recorded peaks

for different molecules have different

retention times which are listed in Table 2.

Table 2. Retention times of different molecules

Chemical Water MeOH DME

Retention

time (min) ~2 6-8 5-6

The peak intensity and area are

directly proportional to the chemical

concentration in the sample. As the

concentration of a chemical in the sample

decreases, the peak intensity decreases.

Figure 5 is a GC result example for the final

liquid samples of experiment #3. The DME

peak can be seen on the left and the

methanol peak on the right. The GC spectra

of the final liquid sample of all three

experiments have DME and methanol peaks

as shown in Figure 5. The presence of the

DME peak in the liquid sample may have

resulted from collecting the final liquid

samples during pressurization, therefore a

portion of DME product liquefied and

dissolved in the liquid mixture. The DME

peak helps to confirm that DME was

produced in all three experiments.

129

128

1

Figure 5. GC spectrum for Experiment #3 final liquid sample

The GC results for the gas sample (5

μL) of Experiment #1 and #2 only have a

water peak present. After the gas sample

size was changed to 0.5 mL, the GC result

of the gas sample (0.5 mL) showed peaks

for water, DME, and a very small amount of

methanol. The GC results for the gas

samples (0.5 mL) of Experiment #1 and #2

also showed a huge water peak and a small

DME peak.

The DME peak in gas samples also

helped to confirm that DME was produced

in all three experiments. The water peak is

higher than the DME peak in the 0.5mL GC

results and only a water peak was present in

the GC result of a 5 μL gas sample. This is

because water has a higher thermal

conductivity than DME. The gas samples

were collected using a syringe at different

pressures and flow rates because the flow

rate could not be controlled using the gas

outlet valve present in Figure 3. This means

that the gas samples were collected at

different conditions. Consequently, the

DME peaks in the GC results for gas

samples could not be compared to each

other, nor to pure DME. DME and water

concentrations and yields in the gas sample

could not be determined.

Experiment #2, involving only

molecular sieves, was not expected to have

any methanol conversion to DME, due to a

lack of a catalyst. However, the GC results

suggested that DME was produced.

Molecular sieves are primarily composed of

alumina and silica [9]. Methanol

dehydration to DME generally is catalyzed

by a solid acid catalyst. The catalyst activity

of a silica-alumina catalyst in DME

production was studied in previous work [1].

Therefore, DME production in Experiment

#2 should be a result of the methanol

conversion catalyzed by alumina and silica

in molecular sieves.

130

2

Figure 6. Methanol Peak Area vs. Methanol wt% in Methanol/ PEG-400 solution

The reported areas under the

methanol peak in the GC data of liquid

samples were used in the calculation of the

methanol conversion for the experiments.

By measuring the methanol peak areas for

five methanol/PEG-400 solution samples

containing different methanol

concentrations, a calibration of methanol

peak area and methanol concentration (wt%)

was performed (Figure 6). A fitting curve

was generated to determine the linear trend

line. The slope was 220.8 ± 2.3 and y-

intercept was -4.4 ± 18.2.

Table 3 shows the average methanol

peak areas of the initial and final liquid

samples for the three experiments and the

methanol concentration, which was

converted using the calibration above. The

percent methanol conversion was

determined by comparing the concentration

difference between the initial and final

liquid samples to the initial concentration.

A methanol adsorption test was

conducted by comparing the methanol

concentration of the sample with molecular

sieves to the one without molecular sieves.

The relative difference is 9.4 % ± 1.1 %.

This result showed that 9.4% of the

methanol in the sample was adsorbed by

molecular sieves. Methanol conversions of

Experiment #2 and #3 were adjusted based

on the adsorption test result. This adjustment

can be seen in Table 3.

Figure 7 shows that methanol

conversion in Experiment #3 is about twice

that of each of the baselines. The results

indicate that methanol conversion is

improved by the addition of molecular

sieves. However, in order to confirm that

methanol was only consumed in its

conversion to DME, additional DME

131

1

Table 3. Determination of methanol conversion

Sample

MeOH Peak

Area Avg.

(mV*s)

Area meas.

error (mV*s)

MeOH

wt%

MeOH

wt%

error

MeOH

Conversion

%

MeOH

Conversion

% error

Initial

liquid 2484.0 ±130.9 11.3 ± 0.8 - -

Exp. #1

Final

liquid

1820.4 ±122.3 8.3 ± 0.7 26.7 ± 8.8

Exp. #2

Final

liquid

1618.1 ±40.0 7.3 ± 0.3 25.5 ± 6.2

Exp. #3

Final

liquid

1071.8 ±128.2 4.9 ± 0.7 47.5 ± 8.6

production data is needed. It in unconfirmed

whether the amount of DME produced is

equivalent to amount of methanol consumed

in each experiment. It is also necessary to

characterize the fresh and used molecular

sieve samples in order to determine its

function in water removal and methanol

conversion.

Figure 7. Methanol conversion % for

Experiment #1 (with γ-Al2O3 only), Experiment

#2 (with molecular sieves only) and Experiment

#3 (with both γ-Al2O3 and molecular sieves).

4. Conclusions

In this study, three experiments were

run with the same amount of methanol and

PEG-400. Experiment #1 was run with γ-

Al2O3 only, Experiment #2 with molecular

sieves only, and Experiment #3 with both γ-

γ-Al2O3 and molecular sieves. From the

experiment data, the increase in pressure at

an isothermal temperature of 280 °C

indicated the conversion of methanol to

DME. The stabilization and then decrease in

pressure after 2 hours shows that the

experiment time could be reduced to 2

hours. This will reduce energy input.

The GC results indicated that DME

was produced in all three experiments. The

production of DME in Experiment #2 was

the result of methanol conversion catalyzed

by the alumina and silica components in the

molecular sieves. The methanol conversion

of three experiments was also determined

from the GC results: Experiment #1, 26.7 %

± 8.8%; Experiment #2, 25.5% ± 6.2%;

Experiment #3, 47.5% ± 8.6%.

The high methanol conversion in

Experiment #3 indicated that methanol

conversion is improved by the addition of

molecular sieves. However, additional DME

production data is needed in order to

132

123

confirm whether the amount of DME

produced is equivalent to amount of

methanol consumed in each experiment.

This information is needed confirm that

methanol is only consumed in the

conversion to DME.

Future work would include

additional runs for each of the three

experiments to obtain sufficient DME

production data and the characterization of

fresh and used molecular sieves to establish

its role as an in-situ water removal material

during conversion of methanol to DME. The

particle diameter of the molecular sieves in

relation to the particle size of methanol

should also be considered. The molecular

sieve used was 5 Å and the diameter of

water is only 3 Å. It could potentially be

beneficial to use a molecular sieve with a

smaller pore size (3 Å or 4 Å) in order to

reduce the amount of methanol absorbed by

the molecular sieves while still being able to

absorb water.

5. Acknowledgements

We greatly appreciate the support we

received from Dr. Devinder Mahajan,

Elizabeth Taveras and Nyima Choephell.

This work was performed at the Advanced

Energy Research and Technology Center

(AERTC) in Stony Brook, New York,

11794.

6. References

[1] Jun, K.W.; Lee H.S.; Roh, H.S.; Park,

S.E. Catalytic Dehydration of Methanol to

Dimethyl Ether (DME) over Solid-Acid

Catalysts. Bulletin of the Korean Chemical

Society 23.6 (2002): 803-07. Web. 29 Mar.

2017.

[2] Lee, S.G.; Sardesai, A. Liquid phase

methanol and dimethyl ether synthesis from

syngas. Topics in Catalysis, Volume 32,

Issue 3-4 (2005): 197-207. Paper.

[3] Jiang, S.; Hwang, J.S.; Jin, T.H.; Cai,

T.X.; Cho, W.; Baek, Y.S.; Park, S.E.

Dehydration of Methanol to Dimethyl Ether

over ZSM-5 Zeolite. Bulletin Korean

Chemistry Society, Volume 25, Issue 2

(2004): 185-189. Paper.

[4] Iliuta, I.; Larachi, F.; Fongarland, P.

Dimethyl Ether Synthesis with in-Situ H2O

Removal in Fixed-Bed Membrane Reactor:

Model and Simulations." Industrial &

Engineering Chemistry Research 49.15

(2010): 6870-877. Web. 29 Mar. 2017.

[5] Liu, D.H.; Yao, C.F.; Zhang,J.Q.; Fang,

D.Y.; Chen, D.S. Catalytic Dehydration of

Methanol to Dimethyl Ether over Modified

γ-Al2O3 Catalyst. Fuel 90.5 (2011): 1738-

742. Web. 29 Mar. 2017.

[6] Banat, F.; AI-Asheh, S.; AI-Lagtah, N.

Adsorptive Distillation Using Molecular

Sieves and Low-cost Biobased Adsorbents

for the Break-up of the Isopropanol–Water

Azeotrope. Adsorption Science and

Technology, Volume 21, Issue 9 (2003): 821

- 830. Paper.

[7] Tang, Q.; Xu, H.; Zheng, Y.Y.; Wang,

J.F.; Li, H.S.; Zhang, J. Catalytic

Dehydration of Methanol to Dimethyl Ether

over Micro/mesoporous ZSM-5/MCM-41

Composite Molecular Sieves. Applied

Catalysis A: General 413-414 (2012): 36-

42. Web. 29 Mar. 2017.

[8] Komarneni, S.; Pidugu, R.; Menon, V.C.

Water adsorption and desorption isotherms

of silica and alumina mesoporous molecular

sieves. Journal of Porous Materials, Volume

3, Issue 2, (1996): Pg. 99-106. Paper.

[9] n.a. Molecular Sieves - Technical

133

124

Information Bulletin. Sigma Aldrich. Sigma-

Aldrich Corp., n.d. Web. 31 Mar. 2017.

[10] n.a. DME(Dimethyl Ether). Toyo

Enngineering Corporation. Web. 21 Apr

2017. http://www.toyo-

eng.com/jp/en/products/energy/dme/

[11] Lee, K.H.; Youn, M.Y.; Sea, B.K.

Preparation of hydrophilic ceramic

membranes for a dehydration membrane

reactor. Desalination, Volume 191, Issues 1–

3 (2006): 296-302. Paper.

134

JUCER Volume VI

Spring 2017