2018 Ingenium Journal

U N D E R G R A D U AT E R E S E A R C H AT T H E S W A N S O N S C H O O L O F E N G I N E E R I N G

2018

Highlighting Undergraduate Research at the University of Pittsburgh Swanson School of Engineering Spring 2018

The University of Pittsburgh is an affirmative action, equal opportunity institution. Published in cooperation with the Office of University Communications. DCS110990 Please note that neither Ingenium nor the Swanson School of Engineering retains any copyright of the original work produced in this issue. However, the Swanson School does retain the right to nonexclusive use in print and electronic formats of all papers as published in Ingenium. University of Pittsburgh Swanson School of Engineering Benedum Hall 3700 Oâ&#x20AC;&#x2122;Hara Street, Pittsburgh, PA 15261 USA

UN D E RG RA D UAT E RE SE A R C H AT T H E S WAN S O N S C H O O L O F EN G I N EER I N G

Table of Contents 4 A Message from the Associate Dean for Research 5 A Message from the Co-Editors-in-Chief 6 Editorial Board Members Articles 7 Materials Computation of Magnetic Properties of Cobalt Nanoparticles

59 Modeling Interferon Response in Pandemic H1N1 Influenza Virus Infected Mice Using Gene Expression Data

12 Computational and Experimental Modeling of Cytochrome B5 Reductase Dynamics

68 Density Variation in Additively Manufactured Ti-6Al-4V

Alexandra Beebout, Zhenyu Liu, and Guofeng Wang Laboratory of Dr. Guofeng Wang, Department of Materials Science Alyssa Bell, Adam Straub, and Patrick Thibodeau Department of Bioengineering and Department of Chemical and Petroleum Engineering, Department of Microbiology and Molecular Genetics, and Department of Pharmacology and Chemical Biology

16 A Wearable Sensing System to Estimate Lower Limb State for Drop Foot Correction Levi Burner and Dr. Nitin Sharma Neuromuscular Control and Robotics Laboratory, Department of Mechanical Engineering and Materials Science

21 Determining Through-Culm Wall Properties of Bamboo Using the Flat-Ring Bending Test Chelsea Flower and Kent A. Harries Department of Civil and Environmental Engineering

26 Simulating the Natural Gas Filling Rate of Fuel Tanks Packed with Metal-Organic Framework Adsorbents

Keerthi Gnanavel and Dr. Christopher E. Wilmer Hypothetical Materials Laboratory, Department of Chemical and Petroleum Engineering

33 Binder Jet Additive Manufacturing of Magnetocaloric Foams for High-Efficiency Cooling Katerina Kimes, Amir Mostafaei, Erica Stevens, and Markus Chmielus Department of Mechanical Engineering and Materials Science

39 Silicon Solar Cell 92.4% Solar Spectrum Absorption Achieved Through Nanotexturing And Thin Film Etching Danielle Kline and Paul Leu Laboratory for Advanced Materials in Pittsburgh, Department of Industrial Engineering

43 Deterministic Space Networking and Time-Triggered Ethernet Modeling Joseph R. Kocik, Dr. Alan George, and Christopher Wilson NSF Center for Space, High-performance, and Resilient Computing

48 Induction of Regulatory T Cells for Treatment of Periodontitis: Analysis of the Effect of Tri-Factor Microparticle Treatment on Disease Outcomes

Kayla M. LeMaster, and Ashlee C. Greene Little Lab, Department of Chemical and Petroleum Engineering

53 Combining Metal Nanomeshes and Nanostructured “Hazy” Glass as an Alternative to Transparent Conducting Oxides Maxwell Lindsay, Rafael Rodriguez, Sajad Haghanifar, and Dr. Paul Leu Department of Mechanical Engineering and Materials Science and Department of Industrial Engineering

Kyler R. Madara and Jason E. Shoemaker Department of Chemical and Petroleum Engineering

63 Binder Jet Additive Manufacturing of Dental Material from Cobalt-Chrome Alloy

Pierangeli Rodriguez, Amir Mostafaei and Markus Chmielus Department of Mechanical Engineering and Materials Science Samantha Schloder, Erica Stevens, David Schmidt, Markus Chmielus Advanced Manufacturing and Magnetic Materials Laboratory, Department of Mechanical Engineering and Materials Science

72 Assessing Cytocompatibility of Novel Ultra-High Ductility Magnesium Alloys

Fathima Shabnam, Jingyao Wu, Abhijit Roy, and Prashant N. Kumta Department of Chemical and Petroleum Engineering and Department of Bioengineering

76 Stimulation of Elastic Fiber Proteins by Mesenchymal Stem Cell-Derived Factors

Rachel Sides, Kaori Sugiyama, Aneesh Ramaswamy, David Vorp, Hiromi Yanagisawa, and Justin Weinbaum Department of Bioengineering, Department of Cardiothoracic Surgery, Department of Surgery, McGowan Institute for Regenerative Medicine, Center for Vascular Remodeling and Regeneration, and Department of Chemical and Petroleum Engineering

82 Assessment of Human Stem Cell Retention and Host Cell Invasion in an Implanted Seeded Tubular Scaffold

Abigail M. Snyder, Katherine L. Lorentz, Antonio D’Amore, Justin S. Weinbaum, William R. Wagner, and David A.Vorp Department of Bioengineering1, Department of Surgery3, Department of Cardiothoracic Surgery4, Department of Chemical and Petroleum Engineering5, and McGowan Institute for Regenerative Medicine2, University of Pittsburgh, Pittsburgh, PA, USA

87 The Effect of Zeolite Additives on Li-ion Conductivity of Gel-Polymer Electrolytes

Philip A. Williamson, Pavithra M. Shanthi, Ramalinga Kuruba, Prashanth J. Hanumantha and Prashant N. Kumta Department of Mechanical Engineering and Materials Science, Department of Civil and Environmental Engineering and Department of Bioengineering

91 The Effect of an Osteoarthritis Unloader Brace on Knee Joint Space During Gait Shumeng Yang, Kanto Nagai, William Anderst Department of Bioengineering and Department of Orthopaedic Surgery

95 Trigger Rate Monitoring for the ATLAS Experiment at CERN

Daniel Zheng, Andrew Todd Aukerman, Dr. Tae Min Hong Department of Electrical and Computer Engineering and Department of Physics and Astronomy

Ingenium 2018

A Message from the Associate Dean for Research “Ingenium” is medieval English vernacular for “an ingenious contrivance.” Two descendant words of ingenium in the English language are “engine” and “engineering.” The word “engineer” can be a noun or a verb—a profession/professional or an action.

David A. Vorp, PhD

The University of Pittsburgh Swanson School of Engineering proudly presents the fourth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering, a compilation of reports representing the achievements of selected Swanson School undergraduate students who have demonstrated excellence in our 2017 summer research program. Business magnate Elon Musk has said, “I don’t spend time pontificating about high-concept things; I spend my time solving engineering and manufacturing problems.”

Our students are our legacy; our students are our future. To see them learn how to apply what they learn in the classroom and outside the classroom is very gratifying. But we hope that it does not stop there. We envision and hope that these experiences make a lasting impact on the students and their careers and drive them to achieve the heights of Musk’s vision to change the world and humanity using engineering.

The student authors of the articles contained within this issue of Ingenium studied mostly under the tutelage of a faculty mentor in the Swanson School of Engineering; in some cases, the research took place at other institutions. At the conclusion of the program, students were asked to submit abstracts summarizing the results of their research, which were reviewed by the Ingenium Editorial Board made up of Swanson School graduate student volunteers. Students with the highest-ranking abstracts were invited to submit full manuscripts for consideration for inclusion in Ingenium, and those that were submitted were peer reviewed by the Editorial Board. Therefore, Ingenium serves as more than a record of our undergraduate students’ excellence in research: It also serves as a practical experience for our undergraduate students in scientific writing and the author’s perspective of the peer-review process and for our graduate students in editorial review and the reviewer’s perspective of the peer-review process.

I would like to acknowledge the hard work and dedication of the coeditors in chief of this issue of Ingenium, Amir Mostafaei and Stephanie Wiltman, as well as the production assistance of Melissa Penkrot and the Office of University Communications. This issue also would not have been possible without the hard work of the graduate student volunteers who made up the Ingenium Editorial Board and who are listed by name in this issue. It is also altogether fitting to thank the faculty mentors and other coauthors of each of the reports included in this issue. Finally, we are grateful to the PPG Foundation, which generously supported 12 of our undergraduate students as 2017 PPG Summer Undergraduate Research fellows. Five of those fellows —Alexandra Beebout, Keerthi Gnanavel, Katerina Kimes, Danielle Kline, and Samantha Schloder—have contributed articles in this year’s edition of Ingenium.

On behalf of U.S. Steel Dean of Engineering Gerald Holder and the entire Swanson School of Engineering, I hope that you enjoy reading this fourth issue of Ingenium and that the many talents of our students inspire the engineers of the future!

David A. Vorp, PhD Associate Dean for Research University of Pittsburgh Swanson School of Engineering

Undergraduate Research at the Swanson School of Engineering

Ingenium 2018

A Message from the Co-Editors-in-Chief

Amir Mostafaei

Greetings! We are delighted to bring you the fourth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering. Ingenium was instituted as a platform to showcase peer-reviewed undergraduate research for students, faculty, and friends—at the University of Pittsburgh and beyond. Ingenium illustrates the breadth of diverse research achieved at the University of Pittsburgh, comprising 19 articles across each department of the Swanson School of Engineering. Successful production of this issue has been enabled by the Ingenium team—editorial board, faculty advisors, and of course, the undergraduate students— all of whom contributed their time and effort in conceiving, developing, and polishing the research published here. We take this opportunity to acknowledge each one of you—thank you! As a peer-reviewed publication, all submissions to Ingenium were evaluated using a standard two-step single-blind review process. The extended abstracts and the full manuscripts were comprehensively reviewed by the editorial board consisting of graduate student volunteers from all departments of the Swanson School. This all-round participation of undergraduate and graduate students has been the characteristic spirit of Ingenium as a scientific publication by students.

Stephanie Wiltman

We are grateful for Associate Dean for Research, David Vorp, for his vision and guidance through the editing process. We are especially thankful for Melissa Penkrot, whose support has been invaluable. Furthermore, Marygrace Reder and the team at the Office of University Communications have done a fantastic job developing this issue’s cover and producing this issue in its current, professional, and aesthetic form. Finally, we aspire that this modest exhibit portrays the Swanson School of Engineering as the vibrant and enthusiastic research community it is. And we sincerely hope that you—the reader —enjoy this morsel of exciting research we do at Pitt!

Amir Mostafaei Stephanie Wiltman Co-Editor-in-Chief Co-Editor-in-Chief

Undergraduate Research at the Swanson School of Engineering

Ingenium 2018

Editorial Board Members Ingenium: Undergraduate Research at the Swanson School of Engineering Co-Editors-in-Chief: Amir Mostafaei (Mechanical Engineering and Materials Science) and Stephanie Wiltman (Bioengineering) Name Department Amoabeng, Derrick..................................................................................................................................Chemical and Petroleum Engineering Aucie, Yashar.............................................................................................................................................................................. Bioengineering Bobosky, Matthew................................................................................................................... Mechanical Engineering and Materials Science Cardoza, Alvaro............................................................................................................................................................... Electrical Engineering Chen, Jingming........................................................................................................................................................................... Bioengineering Dean, James.............................................................................................................................................Chemical and Petroleum Engineering Ferrer, Gerald.............................................................................................................................................................................. Bioengineering Fortunato, Ronald.................................................................................................................... Mechanical Engineering and Materials Science Gade, Piyusha............................................................................................................................................................................. Bioengineering Ghadge, Shrinath......................................................................................................................................Chemical and Petroleum Engineering Grigsby, Erinn............................................................................................................................................................................. Bioengineering Gudarzi, Mohammad............................................................................................................... Mechanical Engineering and Materials Science Gujarati, Abhijeet..................................................................................................................... Mechanical Engineering and Materials Science Haghanifar, Sajad............................................................................................................................................................. Industrial Engineering Hasik, Vaclav...........................................................................................................................................Civil and Environmental Engineering Heusser, Michelle ....................................................................................................................................................................... Bioengineering Hughes, Christopher................................................................................................................................................................... Bioengineering Islam, Riazul............................................................................................................................................................................... Bioengineering Jian, Jianan............................................................................................................................................... Electrical and Computer Engineering Khanna, Sanjeev......................................................................................................................................................................... Bioengineering Kher, Rajan..............................................................................................................................................Chemical and Petroleum Engineering Kovalchuck, Matthew.............................................................................................................. Mechanical Engineering and Materials Science Lee, Yoojin.................................................................................................................................................................................. Bioengineering Liu, Monica ................................................................................................................................................................................ Bioengineering Lorentz, Katherine...................................................................................................................................................................... Bioengineering Manderino, Christopher........................................................................................................................... Electrical and Computer Engineering Mohsenian, Kevin....................................................................................................................................................................... Bioengineering Mostafaei, Amir**................................................................................................................... Mechanical Engineering and Materials Science Nasrollahi, Amir ......................................................................................................................................Civil and Environmental Engineering Oborski, Matthew....................................................................................................................................................................... Bioengineering Pafcheck, Brad......................................................................................................................... Mechanical Engineering and Materials Science Peng, Zhaoqiang ..................................................................................................................................... Electrical and Computer Engineering Su, Peng........................................................................................................................................................................... Industrial Engineering Pliner, Erika................................................................................................................................................................................ Bioengineering Ramaswamy, Aneesh ................................................................................................................................................................. Bioengineering Rodriguez, Gianfranco.............................................................................................................................Chemical and Petroleum Engineering Stabryla, Lisa ..........................................................................................................................................Civil and Environmental Engineering Stevens, Erica .......................................................................................................................... Mechanical Engineering and Materials Science Svirsko, Anna ..........................................................................................................................................Civil and Environmental Engineering Taylor, Michael .......................................................................................................................................Chemical and Petroleum Engineering Wadekar, Shardul ....................................................................................................................................Chemical and Petroleum Engineering Wang, Yan ...............................................................................................................................................Civil and Environmental Engineering Wang, Mohan .......................................................................................................................................... Electrical and Computer Engineering Wellman, Steven ........................................................................................................................................................................ Bioengineering Wiltman, Stephanie** ................................................................................................................................................................ Bioengineering ** Co-Editors-in-Chief 2018

Undergraduate Research at the Swanson School of Engineering

Ingenium 2018

Materials Computation of Magnetic Properties of Cobalt Nanoparticles Alexandra Beebout, Zhenyu Liu, and Guofeng Wang

Laboratory of Dr. Guofeng Wang, Department of Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Magnetic nanoparticles are useful in a variety of applications: data storage, biotechnology, medical imaging, magnetic fluids, catalysis, and environmental remediation. Cobalt is of particular interest as it is widely used in hard drives and has potential to be used as a high-temperature coating in solar panels. High-density data storage on hard drives relies on the manipulation of magnetic properties of small particles. Investigating energy and magnetism of ferromagnetic nanoparticles is an important step in using these materials to their fullest extent. Our research explores magnetic and energetic properties of the four observed structures of cobalt nanoclusters. We performed density functional theory (DFT) calculations on the software known as Vienna Ab initio Simulation Package (VASP) using pseudopotential plane wave method. From our calculations we obtained ground-state energy and magnetic moment values for the four observed atomic arrangements of cobalt: FCC, HCP, icosahedral, and epsilon. These values were calculated for three different cluster sizes, N=13, N= (55,57,59), and N=(147,153). Our results indicate that the icosahedral structure is the most stable, as its relaxed structure consistently exhibits the lowest energy. Furthermore, we observe that as the cluster size increases in the relaxed structures, the energy and magnetic moment per atom decrease. Smaller particles therefore impart a much higher magnetic moment and energy per atom. Keywords: Cobalt, nanoparticles, density functional theory

1. Introduction Magnetic nanoparticles are an important research topic because they are useful in a variety of applications, namely, data storage, biotechnology, medical imaging, magnetic fluids, catalysis, and environmental remediation [1]. The study of transition-metal clusters is helpful in understanding the behavior of 3d electrons, which are responsible for ferromagnetism. Cobalt is of particular interest as it is most widely used in hard drives and has potential to be used as a high-temperature coating in

solar panels [2]. Research on the geometric and energetic properties of small cobalt clusters has proven difficult, as the scale in question is too small for diffraction probes and too large for spectroscopic analysis. J. Bucher, D. Douglass, and L. Bloomfield have reported on the complex magnetic behavior of free cobalt clusters (N = 20 â&#x20AC;&#x201C; 200) [5]. Cobalt exhibits at least four distinct crystal structures, these being FCC, HCP, icosahedral, and epsilon-cobalt [3,6]. The first to synthesize and describe epsilon cobalt were D. Dinega and M.G. Bawendi via thermal decomposition of octacarbonyldicobalt in the presence of trioctylphosphane oxide [3]. J. Souto-casares et al. used real-space formalism of pseudopotentials within DFT calculations to investigate the magnetic and geometric properties of cobalt clusters; they concluded that icosahedral and HCP structures were most stable on this scale [4]. Our research explores magnetic and energetic properties of the four observed structures of cobalt clusters. It will fill a gap in current research on magnetic nanoparticles because no one has compared these properties in all cobalt nanostructures. In addition, no other research group has studied the evolution of magnetic moment with respect to particle size in cobalt.

2. Methods Density functional theory (DFT) calculations were performed on the software known as Vienna Ab initio Simulation Package (VASP) using pseudopotential plane wave method. Data files that modelled and described analytically the clusters were produced via Visualization for Electronic and Structural Analysis (VESTA) software. Examples of the structures modelled using this software can be found in Figure1. 2.1 Energy Optimization To begin, energy optimization was applied to clusters of cobalt atoms, N = 13 â&#x20AC;&#x201C; 155, of three known crystal structures. These were FCC (face-centered cubic), HCP (hexagonal close-packed), and epsilon-cobalt. This was

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done to determine geometric and energetic parameters for later calculations. Performing these calculations also allowed us to verify that our methods yielded results similar to those of other research groups. 2.2 Geometry Optimization At this point, geometry optimization calculations were performed using projector augmented wave method. A conjugate-gradient algorithm was used to relax the ions into their instantaneous ground state until all force components decreased to less than -0.01 eV/Ă&#x2026;. Our method used the Perdew-Wang 91 exchange-correlation functional for spin-polarized generalized gradient approximation (GGA) in which the 3d and 4s electrons are treated as valence electrons. The wave functions were expanded in the plane wave basis set with the kinetic energy cutoff of 500 eV. The initial magnetic moment per atom was set to five. [12,13,14,15,16]

Figure 1. VESTA representations of cobalt clusters. (a) FCC n=147, (b) icosahedral n=13, (c) HCP n=153, (d) icosahedral n=55, (e) epsilon-cobalt n=153, (f) icosahedral n=147

3. Results Before relaxation, the atomic diameters of 12 chosen nanoparticles were determined. For the FCC crystal structure, the diameters were: 0.471nm for n=13 particles, 0.995nm for n=55, and 1.493nm for n= 147. In the HCP crystals, they were: 0.474nm for n=13, 0.694nm for n=57, and 1.495nm for n=153. The diameters of the epsilon-Co particles came to be: 0.466nm for n=13, 0.932 for n=59, and 1.435nm for n=153. For the icosahedral pattern, particle diameters were: 0.468nm for n=13, 0.943 for n=55, and 1.420 for n=147.

Figure 2. Calculated values for all four crystal structures. (a) Total energy of the relaxed cluster divided by the number atoms (n) plotted against n. (b) Total magnetic moment of the relaxed cluster divided by the number atoms (n) plotted against n.

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The ground-state energy and magnetic moment values for all known atomic arrangements (FCC, HCP, icosahedral, and epsilon) were obtained from the optimization calculations. These values were calculated for three different cluster sizes: N=13, N= (55,57,59), and N=(147,153). The results are displayed comparatively in Figure 2. 3.1 Crystal Calculations Cobalt exhibits three known crystal structures; these are FCC, HCP, and epsilon-cobalt. FCC signifies facecentered cubic, as it is a cubic structure with consistent distances between atoms. The distance between atoms in a crystal structure is known as the lattice parameter, a. The packing fraction for FCC is .74, which is the volume of atoms in the cell divided by the total volume of the cell. The unit cell, or the smallest repeating unit, of FCC consists of a cube with atoms in all eight corners and six more atoms centered on the face of each of the six sides of the cube. The coordination number of a structure is the number of atoms that each atom is touching. For FCC, the coordination number is 12. This structure is also known as cubic close-packed (CCP). Hexagonal close-packed (HCP) also has a packing fraction of .74 and a coordination number of 12. Its unit cell consists of three layers where the atoms fit tightly around each other. The top and bottom layer both contain seven atoms; six in the shape of a hexagon and one in the center. The middle layer has three atoms in the shape of a triangle. HCP is not cubic, so it has two lattice parameters, a and c. Structure

FCC

HCP

EpsilonCobalt

Value

Our Results

Epsilon-cobalt is a structure unique to cobalt that was first described in 1999. This structure is cubic and a unit cell contains 20 atoms, some with a coordination number of two and others with three. It exhibits threefold symmetry along its main diagonal. [3] In order to perform calculations to determine cohesive energy and magnetic moment, the most energetically favorable lattice parameters for these structures had to be resolved. The relevant literature was reviewed to determine approximate values for the lattice parameters. A set of values was chosen as a starting point and, using structure- and atom-specific parameters, submitted to VASP to perform energy optimization calculations. The process was repeated with more precise values, up to five decimal places, until what appeared to be the most energetically favorable lattice parameters were obtained for each crystal structure. In Table 1, our calculated results are compared with other calculated and experimentally determined results. For FCC, the value a = 3.518 was obtained, which was exactly the same as another value obtained experimentally by de la PeĂąa Oâ&#x20AC;&#x2122;Shea et. al. Our HCP calculation for the lattice parameter a = 2.4918 was very close the DFT calculation of a = 2.5007 obtained by Singal et. al. The ratio that we obtained, c/a = 1.6157, was almost the same as a value, c/a = 1.614, published by the Carnegie Mellon Alloy Database and also obtained from DFT calculations. The optimization for epsilon-cobalt yielded a = 6.05432,

Experimental Results

Other DFT Results

a(angstrom)

3.518

3.54477, 3.5688, 3.5459

3.518(GGA)9, 3.548(GGA+U)9, 3.480610, 3.5411

1.6089

1.75617

1.64(GGA)9, 1.87(GGA+U)9

E (eV/atom)

-6.9698752

-3.5446

-4.35, -5.48, -3.79

a(angstrom)

2.4918

2.50718

2.47610, 2.500719

c/a

1.6157

1.623718

1.6429, 1.61410, 1.62219

1.55885

1.714, 1.7220

1.61(GGA)9, 1.83(GGA+U)9

E (eV/atom)

-6.9899775

-4.39

-4.72, -5.49, -3.66

a(angstrom)

6.05432

6.00979

6.057(GGA)9, 6.128(GGA+U)9

1.603845

1.703

1.65(GGA)9, 1.91(GGA+U)9

E (eV/atom)

-6.9433985

-5.45, -3.70

Table 1. Results of preliminary geometry optimization calculations compared with other published results.

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which is very close to a = 6.057, a value determined by de la PeĂąa Oâ&#x20AC;&#x2122;Shea et. al. using DFT calculations. It should be noted that this crystal structure has been studied less extensively than the other two. The consistency of our results with those of other groups demonstrates the replicability and integrity of our computational strategy. 3.2 Nanoparticle Calculations Once the ideal lattice parameters were established, the particles in question had to be modelled. In addition to FCC, HCP, and epsilon-cobalt, a third structure has been observed to occur in cobalt nanoparticles, the icosahedral structure. This structure was also investigated. An icosahedron is a polyhedron with 20 faces, 30 edges, and 12 vertices. A particle of this design has one atom in the middle and an arbitrary number of layers around it. It therefore does not have one single repeating unit cell and cannot be referred to as a crystal structure; instead, it is a liquid crystal. This shape was constructed using 13, 55, and 147 atoms. The FCC crystal was made with 13, 55, and 147 atoms as well. For HCP, 13, 57, and 153 atoms were used. The epsilon-cobalt particles were built with 13, 59, and 153 atoms. In order to construct these nanoparticles, our calculated values were used to describe repetitive crystal lattices of great size, then cut them down to our experimental sizes. Further optimization calculations were performed for all 12 nanoparticles. The same program was used as for the first step, but certain parameters were changed to improve accuracy. The value for EDIFF was changed from 10-4 to 10-6; the process of relaxation is stopped when the total free energy change and the band structure change are both smaller than this value. The program must take smaller steps to get to the final value, making it more accurate. The value for EDIFFG was changed from -.1 to -.01; all forces must be smaller than this value in order for relaxation to be complete. This is a more restrictive criterion for relaxation, ensuring a more precise result. Our results are outlined in Figure 2. For all four atomic structures, cohesive energy per atom decreased with increasing particle size. The icosahedral structure is consistently the most stable of the arrangements. Magnetic moment per atom does not follow so neat a trend, but in general magnetic moment per atom decreases as particle size increases.

4. Discussion Our results indicate that the icosahedral structure is the most stable, as its relaxed structure consistently exhibits the lowest energy of all four atomic arrangements. This is in agreement with results published by J. Souto-casares et. al. [4]. Furthermore, we observe that as the cluster size increases, the energy and magnetic moment per atom decrease. Smaller particles therefore impart a much higher magnetic moment and energy per atom. Analyzing the computational results, we found that the Co atoms at the surface of the particles have higher magnetic moments than the Co atoms in the inner core of the particles. As compared to the core Co atoms, the surface Co atoms have lower coordination numbers. Hence, we believe that the reduced coordination number of the surface Co atoms induce extra magnetic moments on these surface atoms. Since the small nanoparticles have a higher surface to volume ratio than the large ones, the small nanoparticles have the higher magnetic moment per atom owing to a greater contribution from surface magnetism. Thus, we explain well the observed size-dependent magnetic moment of Co nanoparticles.

5. Conclusion In this study, we have performed the first-principles DFT calculations to predict the structural, energetic and magnetic properties of Co nanoparticles. We predict that the icosahedral particle structure, as compared to HCP, FCC, and epsilon-cobalt particle structures, is energetically the most favorable configuration for the Co nanoparticles with number of atoms ranging from 13 to 153. Moreover, we predict that the average atomic magnetic moment of the Co nanoparticles would decrease when the size of the nanoparticles increases. Therefore, our present study demonstrates that the first-principles DFT calculation method is a useful tool for us to gain in-depth understanding about the relation between the structure and properties of advanced materials.

Acknowledgments This research was made possible by the Swanson School of Engineering and by the generosity of PPG Industries, Inc. Special thanks to Guofeng Wang, Zhenyu Liu, and Yisong Wang.

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References [1] A.Lu, E. Salabas, and F. Schüth, (2007), Angewandte Chemie International Edition, 46(8), 1222-1244 (2007). [2] J. Moon , T. K. Kim , B. VanSaders , C. Choi , Z. W. Liu , S. H. Jin , and R. K. Chen , Sol. Energy Mater. Sol. Cells 134, 417 (2015). [3] D. P. Dinega and M. G. Bawendi, Angew. Chem., Int. Ed. Engl. 38, 1788(1999). [4] J. Souto-Casares, M. Sakurai, and J. R. Chelikowsky, Phys. Rev. B 93, 174418 (2016). [5] J. P. Bucher, D. C. Douglass, and L. A. Bloomfield, Phys. Rev. Lett. 66, 3052 (1991).

[12] P. Hohenberg and W.Kohn, Inhomogeneous Electron Gas, Phys. Rev. 136, B864 (1964). [13] W. Kohn and L. J. Sham, Self-Consistent Equations Including Exchange and Correlation Effects, Phys. Rev. 140, 1133 (1965). [14] G. Kresse and J. Hafner, Ab-Initio MolecularDynamics Simulation of the Liquid-Metal AmorphousSemiconductor Transition in Germanium, Phys. Rev. B 49, 14251 (1994). [15] G. Kresse and D. Joubert, From ultrasoft pseudopotentials to the projector augmented-wave method, Phys. Rev. B 59, 1758 (1999).

[6] Q.-M. Ma, Z. Xie, J. Wang, Y. Liu, and Y.-C. Li, Phys. Lett. A 358, 289 2006 .

[16] J. P. Perdew and Y. Wang, Accurate and Simple Analytic Representation of the Electron-Gas CorrelationEnergy, Phys. Rev. B 45, 13244 (1992).

[7] CRC Handbook of Chemistry and Physics, edited by D. R. Lide and W. M. M. Haynes, 90th ed. (CRC, Boca Raton, FL, 2009).

[17] K.H. Hellwege and O. Madelung. 1986. LandoltBörnstein Numerical Data and Functional Relationships in Science and Technology. New York: Springer

[8] W. F. Gale, T. C. Totemeir. 2004. Smithells Metals Reference Book. 8th ed. Burlington, MA: Elsevier Butterworth-Heinemann

[18] W.B.Pearson. 1967. A Handbook of Lattice Spacings and Structure of Metals Alloys. Vol. 2. Oxford, England: Pergamon Press Ltd.

[9] V. A. de la Pẽna O’Shea, I. de P. R. Moreira, A. Roldan, F. Illas. J. Chem. Phys. 133, 024701 (2010).

[19] C.M. Singal, T.P. Das. 1977. Electronic structure of ferromagnetic hcp cobalt. I. Band properties. Phys. Rev. B 16, 5068

[10] ALLOY DATABASE. Mihalkovic, Widom and cowerkers; [updated 10/18/2011; accessed 6/12/17]. http://alloy.phys.cmu.edu/.

[20] C.Kittel. 2005. Introduction to Solid State Physics. New York: Wiley

[11] M. R. LaBrosse, L. Chen, and J. K. Johnson, Modelling Simul. Mater. Sci. Eng. 18, 015008 (2010).

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Ingenium 2018

Computational and Experimental Modeling of Cytochrome B5 Reductase Dynamics Alyssa Bell, Adam Straub, and Patrick Thibodeau

Department of Bioengineering, Swanson School of Engineering, Department of aChemical and Petroleum Engineering, Swanson School of Engineering; b Department of Microbiology and Molecular Genetics, and cDepartment of Pharmacology and Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Human erythrocyte cytochrome b5 reductase 3 plays a vital role in the electron transport chain by converting NADH to NAD+ to convert methaemoglobin to hemoglobin in the blood via an FAD intermediate. A conservative threonine to serine mutation, T117S, in cytochrome b5 reductase 3 is found in ~23% of African Americans and has been correlated with an elevated risk of hypertension and cardiac failure. The impact of this mutation on protein structure, dynamics, and function is currently unknown. To address this, we utilized a combination of biochemical, structural and computational approaches to assess the impact of T117S on CyB5R3. Computational and experimental approaches demonstrated alterations in protein dynamics and early release of FAD in the T117S protein. These data provide evidence that the molecular effects of the T117S mutant result in altered CyB5R3 structural properties and changes in FAD binding energetics. Given the observed changes in protein dynamics and stability, small molecule binding might be a potential therapeutic strategy to revert T117S and restore nativestate properties to the T117S mutant. Keywords: Protein stability, protein dynamics, protein structure, ligand binding Abbreviations: Cytochrome B5 reductase 3 (CyB5R3), flavin adenine dinucleotide (FAD), wildtype protein (WT), threonine-117-serine (T117S), fast protein liquid chromatography (FPLC), molecular dynamics (MD), root mean square fluctuation (RMSF), guanidinium hydochloride (GuHCl), relative centrifugal force (RCF), melting temperature (Tm)

1. Introduction The human cytochrome enzymes play critical roles in maintaining healthy respiration by aiding in electron transport and oxidation-reduction reactions with the use of iron-containing heme groups as catalysts. These

reactions are partially regulated by accessory enzymes, including multiple reductase proteins [1]. Among these, the cytochrome B5 reductase 3 (CyB5R3) is critical for the conversion of methemoglobin to hemoglobin and participates in multiple metabolic pathways [2]. Conversion of NADH to NAD+ ultimately facilitates methemoglobin reduction via electron transfer through a bound FAD intermediate. Recent studies suggest that mutations in the human cytochrome B5 reductase result in an elevated risk of hypertension and a significant increase in mortality due to cardiac failure (unpublished results, Straub Lab, Univ. of Pittsburgh). A conservative substitution of a threonine to a serine (T117S), is correlated with elevated hypertension and cardiovascular disease and is prevalent in African-Americans with an allele frequency of ~0.23 [3]. To date, the thermodynamic effects of the T117S mutations have not been established. The location of the conservative T117S mutation, in a loop near the bound FAD, provides few clues regarding its impact on CyB5R3 structure and function. We hypothesized that T117S alters the thermodynamic stability and native-state dynamics of CyB5R3 by altering FAD binding, which is critical for enzymatic function and serves as a structural co-factor in the folded protein [4]. Understanding the physical basis of CyB5R3 dysfunction and the T117S mutation will increase our understanding of the molecular pathologies of disease and potentially inform efforts for therapeutic development.

2. Methods 2.1 Molecular Dynamics One hundred nanosecond, all-atom simulations on WT CyB5R3 and T117S were performed using GROMACS [5], essentially as described [6], using the 1UMK PDB file [4] and analyzed in MatLab. Local CyB5R3 dynamics were assessed by measuring the distance between the T117/T117S site and neighboring secondary struc-

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ture elements. Global changes in CyB5R3 structure were assessed by evaluating the dynamics of a highlyconserved salt bridge distal to the T117 locus (D100 and K195 residues). 2.2 Protein Expression and Purification The open reading frame for the CyB5R3 was fused, in-frame, with a 6xHis-tag for protein purification. PCRbased mutagenesis was performed to generate variants. The WT and mutant proteins were expressed in E. coli and purified using standard protein chromatographic separation on an AKTA FPLC using Ni-NTA and S300 columns following standard procedures. 2.3 In Vitro Characterization Thermodynamic stability of the purified CyB5R proteins was assessed with the thermofluor protein assay [7]. Denaturation of 5uM protein was monitored as a function of changes in SYPRO Orange dye fluorescence, which increases as the dye binds to exposed hydrophobic regions of the denatured protein. The traces were normalized, and the mid-point of the fluorescence transition (Tm) was calculated using nonlinear regression, assuming a two-state unfolding model [7]. FAD fluorescence was similarly assessed after temperature ramping during thermal denaturation experiments in the absence of SYPRO orange. Release of the dinucloetide into solution increases the intrinsic fluorescence of FAD. The WT and mutant proteins were also chemically denatured using GuHCl as an orthogonal method to assess protein unfolding and FAD release. Fluorescence emission scans were collected and changes in peak protein fluorescence intensity were used to monitor FAD release.

3. Results 3.1 Simulations of T117S in CyB5R3 The CyB5R3 enzyme consists of two small domains: a FAD binding domain and a NADH binding domain (Figure 1) [1,4]. These two domains are connected via a linker loop and are stabilized by the binding of the FAD nucleotide, forming a nucleotide sandwich. As a result, the FAD binds with high affinity to CyB5R3 and co-purifies with the protein. Available crystal structures of CyB5R3 from multiple species have been solved, however no structural, functional or thermodynamic data are available for the identified T117 CyB5R3 variants [1,4]. The T117 site is located in an extended loop in the FAD binding domain (Figure 1). Substitution of serine to threonine, while conservative, may impact the dynamics of this loop, its interactions with the FAD or alter CyB5R3 stability and structure.

To assess changes in the CyB5R3 molecule associated with the T117S variant, molecular dynamics simulations were used to assess protein dynamics using GROMACS. The RMSF values from all-atom simulations showed minimal changes in global protein dynamics between the WT and T117S proteins (Figure 2). Small (0.5-1.0 Ă&#x2026;) increases in local protein dynamics were seen across the FAD binding domain in the T117S protein with the highest fluctuations seen in the T117S containing loop. 3.2 Protein Stability and FAD Binding The WT and T117S CyB5R3 protein were then purified for in vitro analyses. Protein expression and purification were unchanged by the T117S mutation, suggesting that gross protein properties were not severely impacted by the substitution. Protein stability measurements were then performed using the SYPRO orange thermofluor assay. Dye binding to hydrophobic regions of the protein, exposed upon denaturation, results in an increase in fluorescence intensity. The WT and T117S proteins both showed cooperative thermal unfolding with pre-transition baselines and two-state behavior (Figure 3A). The WT protein denatured with a Tm of 43.6 +/- 0.6 C. The Tm and shape of the denaturation transition was altered by the T117S substitution. The T117S Tm was 41.4 +/- 0.7 C and the pre-transition baseline and denaturation transition showed broadening. These changes in fluorescence are consistent with changes in protein dynamics in the native state of T117S. The environmentally sensitive intrinsic fluorescence of the flavin nucleotide was then used as a second monitor of protein structure. For both WT and T117S CyB5R3, FAD showed a temperature-dependent increase in fluorescence intensity. These increases in fluorescence corre-

Figure 1. Structure of CyB5R3. The structure of CyB5R3 is shown in two rotations. The T117 site is shown in red spheres and the FAD is shown as sticks and colored orange. The NADH and FAD binding domains are labelled for reference in the lower panel.

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lated tightly with the measured thermofluor transitions and showed two-state, sigmoidal behavior (Figure 3B). The FAD thermal midpoint for the WT protein sample was 44.0 +/- 0.8 C. The T117S protein FAD fluorescence was 41.9 +/- 1.0 C, consistent with early unfolding and release of the FAD by the T117S CyB5R3 variant. These data were similar to the thermofluor determined Tms and suggest that FAD release is tightly coupled to CyB5R3 unfolding. As FAD is likely both an electron transfer intermediate and a structural co-factor, the impact of super-saturating FAD was assessed in thermal denaturation experiments. Addition of 1 mM FAD to both the WT and T117S CyB5R3 proteins induced a significant shift in the observed Tm. Saturating FAD shifted the WT Tm to 46.7 +/- 0.4 C. The T117S Tm was similarly shifted to 46.8 +/- 0.6 C in the presence of millimolar FAD. These

Figure 2. Molecular dynamics analysis of CyB5R3 WT and T117S. The RMSF calculated by amino acid position is plotted for the WT, black, and T117S, red, proteins. The locations of the T117S loop, FAD and NADH binding domains and protein secondary structure elements (helices, rectangles; strands, arrows) are indicated.

data are consistent with FAD serving as a structural co-factor and providing additional thermodynamic stability for CyB5R3. FAD fluorescence was then monitored in the presence and absence of the chemical denaturant GuHCl. In the absence of GuHCl, the T117S variant showed increased FAD fluorescence compared to WT (Figure 4A). Complete release of FAD was then triggered by the addition of 4M GuHCl to denature the CyB5R3 protein. Both the wildtype and T117S fluorescence intensities were similar in 4M GuHCl, consistent with similar total concentrations of FAD in both samples and release from the flavoprotein complex. These results are consistent with early FAD release and/or changes binding associated with the T117S mutation.

4. Discussion Full human optic nerve axon counts have been reported These results suggest that, in addition to serving as a critical redox chemistry intermediate, FAD is a stabilizing cofactor for CyB5R3. Moreover, these data provide a structural basis for the observed phenotypes associated with mutant CyB5R3 and provide the first molecular phenotype for the T117S substitution. The computational and experimental results suggest that CyB5R3 structural dynamics are impacted by the T117S mutation. The MD data suggest that the T117S mutation causes increased dynamics of the T117 loop region, which may alter FAD binding. These changes likely propagate across the FAD binding domain. Similarly, the thermofluor experiments show a ~2ยบC destabilization of T117S compared to WT, which is consistent with the changes in dynamics associated with the T117S mutant. Additional mutagenic studies

Figure 3. Thermal stability of CyB5R3 and FAD binding. Thermal denaturation experiments were used to assess the stability and FAD binding to WT and T117S CyB5R3 protein in vitro. A, representative unfolding curves are shown for the WT, black, and T117S, red, proteins with SYPRO orange fluorescence plotted against sample temperature. B, FAD fluorescence is shown as a function of sample temperature. C, representative unfolding urges in the presence of 1 mM FAD are shown for WT, black, and T117S, red.

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are warranted to further evaluate the specific structural requirements for the T117S loop and its role in stabilizing the bound FAD. Stabilization of the protein by supersaturating FAD provides evidence that FAD release is coupled to CyB5R3 unfolding and contributes to its native state stability. Stabilization of the CyB5R3 T117S protein, via ligand and small molecules may restore its native-state properties and serve as a therapeutic mechanism. Similar approaches have been utilized for multiple protein folding and stability diseases. The structure of CyB5R3 indicates that multiple sites are available for small molecule binding and could be targeted without disrupting the conserved FAD and NADH binding sites directly. Further characterization and small molecule screening may provide a path to identify therapeutics to benefit those that inherit the T117S mutant.

5. Conclusions Using a combination of computational and experimental tools, these studies demonstrate that the T117S mutant alters protein dynamics and FAD binding to CyB5R3. Molecular dynamics simulations show that the T117S mutant increases local and global protein dynamics across the FAD binding domain. In vitro assays, using purified CyB5R3 protein, demonstrate that the T117S mutant decreases thermodynamic stability and alters FAD binding to the protein. Further analyses are required to characterize these events more explicitly, however these studies suggest native state dynamics are altered and contribute to FAD release and thermal destabilization. These effects can be reversed by super-saturating concentrations of FAD, suggesting that small molecules

could be developed to stabilize the mutant CyB5R3 protein. Further development of these approaches may facilitate the development of therapeutics for patients with the T117S variant in CyB5R3 variant.

References [1] F. Elahian, Z. Sepehrizadeh, B Moghimi, S.A. Mirzaei, Human cytochrome b5 reductase: structure, function and potential applications. Crit Rev Biotechnol 34 (2014) 134-43. [2] M.M Rahaman, et al, Cytochrome b5 reductase 3 modulates soluble guanytate cyclase redox state and cGMP signaling. Circ Res 121 (2017) 137-148. [3] M.M Jenkins and J.T. Prchal, A high-frequency polymorphism of NADH-cytochrome b5 reductase in African-Americans. Hum Genet. 99 (1997) 248-50. [4] S. Bando, T. Takano, T. Yubisui, K. Shirabe, M. Takeshita, A. Nakagawa, Structure of the human erythrocyte NADH-cytochrome b5 reductase. Acta Crystallogr D Biol Crystallogr. 60 (2004) 1929-34. [5] Abraham, et al, GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 1 (2015) 19-25. [6] M. Zhenin, E. Noy, H. Senderowitz, REMD Simulations Reveal the Dynamic Profile and Mechanism of Action of Deleterious, Rescuing, and Stabilizing Perturbations to NBD1 from CFTR. J. Chem. Inf. Model. 55 (2015) 2349-64. [7] J.J. Lavinder, S.B. Hari, B.J. Sullivan, T.J. Magliery, High-throughput thermal scanning: a general, rapid dyebinding thermal shift screen for protein engineering. J. Am. Chem. Soc. 131 (2009) 3794-3795.

Acknowledgments This research was funded by the Swanson School of Engineering and the Office of the Provost. Molecular Dynamics calculations were performed in the Senderowitz Lab in the Department of Chemistry at Bar Ilan University.

Figure 4. FAD release by mutant CyB5R3. The release of FAD was monitored by intrinsic fluorescence of the flavin. FAD fluorescence was measured in the absence, A, and presence, B, of 4M GuHCl. Fluorescence spectra are shown and labeled for the WT and T117S proteins.

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A Wearable Sensing System to Estimate Lower Limb State for Drop Foot Correction Levi Burner and Dr. Nitin Sharma

Neuromuscular Control and Robotics Laboratory, Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract A common side effect of stroke is drop foot. This causes a patient’s foot to drop or drag onto the floor during the swing phase while walking. Drop foot can be corrected by using functional electrical stimulation (FES). This technology often uses a contact sensor to detect gait phases. The contact sensor is not suitable for high fidelity control of FES because it provides a binary feedback (on/off). For accurate control of FES, full limb state information such as joint angles and angular velocities is highly preferred. Therefore, a wearable sensing system that is capable of real time estimation of limb angles was developed and demonstrated. The system uses inertial measurement units to determine limb angles. The system is untethered and wirelessly communicates limb state information to a recording platform. A preliminary test showed the system’s capability to estimate limb angles in real-time. Future studies will focus on programming advanced estimation algorithms for the system. The limb state data provided by the system will be used to reconstruct a user’s gait in real-time and validated against a ground truth such as measurements from a motion capture system. Once validated the wearable sensing system can be potentially integrated with a multi-channel FES system to correct drop foot or used as a wearable sensing system to study gait abnormalities. Keywords: Inertial Measurement Units, Gait Analysis, Functional Electrical Stimulation, Limb Angle Estimation

1. Introduction Over 800,000 strokes are reported annually. A common side effect of stroke is drop foot, which causes a foot to drag or slap on the floor during walking (swing phase). The condition is due to the inability to control ankle muscles that produce dorsiflexion. It affects a patient’s ability to balance and walk at a steady pace. The loss of control also increases the risk of fall from tripping [1].

Current solutions include using single channel functional electrical stimulation (FES) and a ground contact sensor. FES artificially activates muscles via application of external electrical currents. When the ground contact sensor detects the foot has lifted off the ground, the FES system activates and stimulates the peroneal nerve (to elicit dorsiflexion) so that the foot rises and clears the ground. Another sensing solution is to use electromyography to predict gait posture of a subject and apply stimulation to the peroneal nerve at an appropriate time [2]. Recently, another technique has been investigated that uses inertial measurement units (IMU) to measure the gait cycle of a patient and apply corrective FES. In theory, the IMU will allow more accurate gait cycle estimation than the simple contact sensor. Modulated stimulation of the peroneal nerve can be achieved with more comprehensive data. [3] The goal of this research is to develop a wearable, realtime, foot drop correction system that can be attached to an individual. The system uses IMUs attached to the thigh, shank, and foot for predicting limb angles during gait. A commercial stimulator can be used to apply FES. The motivation for a multiple IMU system is so that comprehensive sensory information on lower limb angles can be obtained and thus the ability to provide multi-channel FES. Compared to existing single channel drop-foot systems, this will facilitate high fidelity, multichannel control of multiple muscles that govern gait. Figure 1 illustrates the parts of a typical closed loop control system that the study focused on.

2. Methods Three primary subsystems were designed. The IMU data collection system (IMU Mux), the Control Software, and the FES system. In Figure 2 the overall system architecture is illustrated. The low-cost Invensense MPU9250 was used. It supports sampling of the gyroscope and accelerometer at 1 kHz and is available on a high-quality board from Sparkfun. Testing verified that a single 2 MHz Serial Peripheral

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Interface (SPI) bus could support a 1 kHz sample rate of 6 IMUs. An open-source MPU9250 SPI library was used for communications [4]. The library was modified to fix a startup error in the acceleration and gyro biases. The magnetometer was also disabled. The gyroscopes range was set to 2000 degrees per second to allow capture of quick movements. The accelerometer’s range was set to 19.6 m/s2. All internal MPU9250 low pass filters were disabled. The digital motion processor inside the MPU9250 was not used because it’s algorithms are proprietary. Signal integrity was a concern because SPI is designed for use with lines several centimeters long. In this case, the distance from the IMU Mux to an IMU could be a meter. Category 5 (Cat5) cabling is used for the SPI bus lines. It helps minimize crosstalk between signal lines. The IMUs share bus lines by being connected in series. This reduces the capacitance of the signal lines compared to the capacitance due to running separate cables. To reduce overshoot and prevent reflections, source termination is used on all SPI bus driver outputs. The IMU Mux uses a Teensy 3.2 for the SPI communications and relaying data over USB. A custom protocol was designed to allow the relay of IMU data packets at the required 1 kHz update rate. It is mounted on a custom PCB that is mounted directly to the computer running the control software. A Raspberry Pi 3 is used to run

Figure 1. The sections encircled with dashed lines are the stages of a closed loop controller that the system handles.

the control software. The Pi, IMU Mux, and IMUs are powered from a rechargeable 2 cell, 1 Ah Lithium Polymer battery. The Pi, IMU Mux, and battery are enclosed in a 3D printed case. Figure 3 shows the physical realization of the Pi, IMU Mux, and IMU Modules. To apply FES a RehaStim stimulator (HASOMED, Germany) was used. It includes its own battery power. Communication with the stimulator and the Raspberry Pi was achieved using the RehaStim’s serial interface and HASOMED’s Science Mode protocol. The Sparkfun MPU9250 breakout boards and Cat5 jacks are mounted on a custom PCB inside a 3D printed case. The case includes a slot for a Velcro strap which is used to attach the case to a subject. The RehaStim, Raspberry Pi, and IMU Mux board are held in a toolbelt. This was considered sufficient since the system is designed for use with future research. A picture of a subject wearing the system can be seen in Figure 4. A basic limb angle estimation algorithm was designed using the Madgwick orientation filter [5]. First the IMU data sampled at 1 kHz was sent from the control processor to a powerful PC using UDP packets over WiFi. Six axis fusion was performed using a Python implementation of the Madgwick sensor fusion algorithm [6]. Initial IMU orientations were saved eight seconds after startup. The initial orientations were removed from all measurements thereafter. This was to correct for human error when mounting the IMU modules. Because the magnetometer

Figure 2. High-level overview of major system components.

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was not used the heading of each IMU would drift over time. To correct this, heading was discarded from the orientation after the initial offset was compensated for. A limb angle was then determined as the difference between the processed orientations.

3. Results To verify the signal integrity in the SPI communication lines an oscilloscope was attached to the data input lines

of the IMUs as well as the data input lines of the IMU Mux. The signals were observed while the maximum supported length of Cat5 cabling was attached between each IMU. The oscilloscope measurements from the SPI bus revealed that overshoot, reflections, and crosstalk were within acceptable levels. A test script was run on the Raspberry Pi to control the FES unit. Voltage measurements from the output of the FES unit were obtained with an oscilloscope. The

Figure 3. On the left, the IMU Mux is shown mounted on top of the Raspberry Pi, in the 3D printed enclosure. Right, IMU Module mounted on custom PCB in 3D printed enclosure. Both enclosuresâ&#x20AC;&#x2122; covers are removed.

Figure 4. Control computer, IMU Mux, FES unit, and IMUs attached to a human.

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measurements indicated that the FES unit was applying stimulation as requested by the Raspberry Pi. The toolbelt was observed to not cause discomfort while the subject walked around normally. The toolbelt held all equipment securely to the subject. The mounting straps for the IMU’s required frequent adjustment. Accuracy of the limb angle extraction algorithm was verified qualitatively. The limb angles were used to manipulate a three-dimensional skeletal model which was viewed in real time while a subject walked around while wearing the system. The skeletal model was found to accurately depict the movement of the subject’s legs. Care had to be taken to ensure that IMU’s did not shift while moving. Gimbal lock was observed when a limb angle was more than 90 degrees from the initial orientation. The phenomenon caused the estimated limb’s angle to discontinuously transition from one angle to the other when a limb angle approached +/- 90 degrees. The limb angles were also logged to a file for further analysis. The data during a slow, full step of the right leg was graphed and can be seen in Figure 5. The step took 3.5 seconds to be completed.

4. Discussion While the FES unit was tested as being controllable it was not used in a real-time control scenario. Further work will attempt to create a controller to correct gait abnormality using the FES unit and data from the limb angle estimation algorithm. The processing unit and the stimulator could be mounted to a human subject while remaining reasonably comfortable. However, further work needs to be done to improve the mounting of the IMU’s. They would frequently shift during usage causing inaccurate measurements. It was demonstrated that the sensors used could produce data required for limb angle estimation in real time at a 1 kHz update rate. The algorithm for estimating limb angles may be used in future for gait abnormality detection algorithms. While the inaccuracies due to gimbal lock were concerning, the range of motion typically used for walking does not cause approach orientations impacted by gimbal lock. This can be seen in Figure 5. No discontinuities in the limb angles can be observed as the limb angles stay 15 degrees away from 90 degrees. A limitation of the algorithm is that it does not produce any time based derivates of angular position. Angular

Figure 5. Limb angles versus time during a full step of the right leg.

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velocity estimates would aid the design of controllers attempting to correct gait abnormalities. The data from a full step shown in Figure 5 is very smooth and continuous. This makes it very easy to see each stage of gait during the progression of the full step. This leads the author’s to be very hopeful about the systems usefulness in detecting abnormalities in gait and detecting when correction has been applied successfully. Figure 5 shows that the limb angles did not return to their initial positions after the completion of a full step which was not fully expected. Because the limb angle measurements were verified visually there is no way of knowing if such small variations were accurate measurements or the result of an error. Future work should benchmark the limb angles against those extracted using an optical tracking system. Additionally, the system would benefit from a C/C++ version of the limb angle estimation algorithm that can be run on the Raspberry Pi in real time. The latency of the system would be improved due to the removal of the UDP communications. This reduction in latency could also be verified with the motion capture system.

5. Conclusion The design of the gyroscopic balance system was a A wearable lower-limb angle measurement system using IMU’s for a potential use in a multi-channel FES system for subjects with drop-foot was developed. The ability to perform six axis fusion on six IMU’s at 1 kHz was successfully demonstrated. The resulting data was successfully used to estimate limb angle positions of the hip, knee, and ankle. Future studies will develop a controller that applies multi-channel FES in response to limb angle data. Additionally, future work will focus on improving the robustness of the measured limb angle data and comparing the angles against those produced by an optical tracking system.

References [1] O’Dell, M.W., Dunning, K., Kluding P., Wu S., Feld J., Ginosian J., and McBride K., Response and Prediction of Improvement in Gait Speed From Functional Electrical Stimulation in Persons With Poststroke Drop Foot. PM&R. 6(7): p. 587-601. [2] Dutta, A., Khattar B., and Banerjee A., Nonlinear analysis of electromyogram following gait training with myoelectrically triggered neuromuscular electrical stimulation in stroke survivors. EURASIP Journal on Advances in Signal Processing, 2012. 2012(1): p. 153. [3] Azevedo Coste C., Jovic J., Pissard-Gibollet R., and Froger J., Continuous gait cycle index estimation for electrical stimulation assisted foot drop correction. Journal of NeuroEngineering and Rehabilitation, 2014. 11(1): p. 118. [4] Chen B., Invensense MPU-9250 SPI Library, 2017. <https://github.com/brianc118/MPU9250>. Retrieved August 2017. [5] Madgwick S., An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io. UK: University of Bristol; 2010. p. 1 [6] Böer J., A Python implementation of Madgwick’s IMU and AHRS algorithm, 2015. <https://github.com/ morgil/madgwick_py>. Retrieved August 2017.

Acknowledgments I want to thank Dr. Sharma for advising and supporting this project. I’d also like to thank the Swanson School of Engineering and the Office of the Provost for providing funding for the project.

The system achieved the target goal of obtaining comprehensive limb angle data concerning the hip, knee, and ankle. This data was clearly sufficient for detecting different parts of the gait cycle. This was achieved while remaining in a wearable form factor that remained unobtrusive to the user. In the future, the system will be used as a research platform to develop control methods for applying multi-channel FES to improve gait in persons with drop-foot.

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Determining Through-Culm Wall Properties of Bamboo Using the Flat-Ring Bending Test Chelsea Flower and Kent A. Harries

Department of Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Bamboo is a light-weight, high strength, and sustainable construction material. It demonstrates structural potential, essentially as a naturally occurring fiber-reinforced composite; however it holds the stigma known as “Poorman’s timber”. This is because there is not yet a means to assess the properties and performance of bamboo as there are for conventional materials, such as concrete and steel. This research explores bamboo’s unique, tapered structure by means of a modified flat-ring flexure test. The culms of five different genera were cut into flat specimen, and tested, horizontally in either 3 or 4-point flexure. The modified samples were cut at specific parts of the cross-section to test the effects of the through-culm wall thickness on the modulus of rupture (MOR). This can eventually be connected to the effects of physical properties, such as fiber content. The longitudinal fibers, which account for bamboo’s high-tensile strength are denser towards the outside of the culm. The results of these experiments demonstrate a potential relationship between the longitudinal fibers and failure behavior. Effects of fiber content on MOR were more prominent in the thicker-walled bamboo species. Eventually, based upon these relationships between mechanical and physical properties, a field-accessible characteristic test can be developed. Keywords: Bamboo, Splitting, Test Method, Modulus of Rupture

1. Introduction Years of research have transformed concrete, steel, and timber into reliable materials for which there are universally recognized design and materials standards. Bamboo demonstrates structural and sustainable properties that compete with and, in some cases, surpass those of comparable construction materials such as timber and cold-formed steel, but still lacks reliable methods to assess these properties. In order to reach its potential as a unique yet reliable structural material, engineers must have a standardized means of assessing the properties and

performance of bamboo. Bamboo is known for its high tensile strength, light weight, and fast growth. Its longitudinal strength combined with light weight makes bamboo a potential competitor to light gauge steel in tensile applications. In practice, however, bamboo is primarily seen as an alternative to softwood lumber and, in some cases, hardwood. The light weight is especially advantageous in areas with high seismic activity and makes bamboo convenient for local transportation and construction. Due to its fast growth-rate, bamboo is a rapidly renewable resource. Some species grow up to a meter per day and reach full maturity in three to five years. Once mature, a two or three-year harvest cycle is possible (as opposed to 10 years for softwood and up to 30 years for hardwood). This is useful in areas of rapid development and fastgrowing populations, such as regions in South America, Africa, and South Asia, all of which are ideal environments for growing bamboo [4]. Many of these regions have established bamboo industries, although structural use of bamboo remains limited and it is typically only used in ‘informal’ or vernacular construction. Though commonly described as a hollow cylinder, bamboo demonstrates gradients in diameter, wall thickness, and fiber content along the culm length therefore it should more correctly be seen as a hollow, tapered cone. Bamboo has evolved into a “smart material” in this sense. Toward the top of the culm, as the diameter gets smaller, the fiber density increases – effectively creating a relatively constant stiffness over the length of the culm. The fiber content also becomes denser towards the outside of the culm wall section as shown in Figure 1, which shows two typical fiber volume distributions reported in the literature [1, 2]. The outermost thin layer of the bamboo culm is a protective silica layer, contributing little to the strength properties of the culm. Engineers can both learn from and utilize these physical characteristics in structural design [3]. Bamboo also demonstrates some mechanical disadvantages. The biggest drawback of bamboo is its weakness in the perpendicular direction and its tendency to fail

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due to longitudinal cracks (splitting). Recent and ongoing research on bamboo studies these failures and is aimed at developing ways to predict and mitigate this behavior. Test methods have been created to assess bamboo splitting behavior, but fail to isolate certain failure mechanisms and lack convenience in a field setting [4, 5]. The use of cumbersome lab equipment is not sustainably or financially convenient in developing countries. Current common tests, including longitudinal tensile and compression, “bow-tie” shear, split pin, and edgebearing tests, fail to capture the entire wall gradient of the bamboo culm [5]. Additionally, these tests cannot be practically translated to a field setting. The flat-ring flexure test, developed by Virgo et al, is a relatively new test method with field potential [6]. The flat ring flexure test further explores the tendency of bamboo to fail via longitudinal cracks and requires a full cross-sectional specimen that is L = 0.2D in length. Depending on the specimen diameter, it is subjected to either 4-point (all diameters) or 3-point (permitted for D < 75 mm) flexure as shown in Figure 2. The desired failure for this test occurs in the constant moment region (4-point test) or within a distance L of the applied load (3-point test). The flat ring flexure test gives the apparent MOR of the specimen, fr which, due to specimen geometry, is related to the transverse tension capacity of the bamboo [6]. The MOR is calculated from the test results as: 3Pa fr = (tN + ts)L2 where P = total load applied to specimen a = shear span (a = test span/3 for 4-point flexure, a = test span/2 for 3-point flexure) tN and ts = culm wall thickness at failure locations on either side of the culm L = length of culm section tested (flexural depth of specimen)

Figure 1. Typical fiber volume ratios through culm wall thickness.

A practical test span is found to be approximately 0.85D. Additionally, the flat ring flexure test is easily translated to a field setting, requiring only two loading plates, four pins, and hand weights, rather than a hydraulic press. This paper primarily explores the reliability of a modification to the flat-ring test specimen, in which only portions of the cross-section are isolated and tested. The intent of this modification is to determine the effect of the material property gradient through the culm wall and to connect the mechanical results to the physical characteristics, such as fiber density.

2. Methods Five different bamboo genera were tested in this study, Phyllostachys edulis (Moso), Phyllostachys bambusoides (Madake), Phyllostachys meyeri (Meyeri), Phyllostachys nigra (Henon), and Bambusa stenostycha (Tre Gai). Tre Gai was the only thick-walled species investigated; Phyllostachys are thin-walled. The culms were cut into full cross-sectional segments, approximately 0.2D in length. In order for the tests to be effective and reliable, the cross-sectional planes on each end had to be parallel; some specimens were sanded using a belt sander in order to achieve this. The tests were then labeled according to figure 3, assuming the cross-section was oval-shaped, rather than circular. The East and West labels indicated the ends of the major axis while the North and South labels indicated the ends of the minor axis. The reported diameter D is the diameter associated with the major axis; in this way, the test span is maximized. The North and South quadrants were then cut using an end mill to create the modified flat-ring flexure specimens, shown in Figure 3. The specimens were cut such that β and γ (see Figure 3a) were in increments of either 0.20 or 0.25 of the original thickness, leaving a value of α, also equal to either 0.20t

Figure 2. Flat Ring Flexure Test [10]

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Species

D (mm)

t (mm)

0.16 (0.24)

110.8 (0.03)

9.3 (0.06)

Î˛

Îą

1.00

18.8 (0.17)

0.27 (0.06)

16.2 (0.28)

0.00

0.26 (0.21)

0.50

0.25 (0.02)

0.13 (0.09)

123.0 (0.02)

10.7 (0.06)

8.4 (0.28)

99.4 (0.01)

7.5 (0.05)

19.6 (0.25)

1.23

0.40

0.22

12.50

0.78

P. nigra

P. meyeri

65.2 (0.10)

6.7 (0.19)

81.2 (0.02)

10.3 (0.14)

0.13 (0.01)

72.7 (0.02)

18.7 (0.06)

0.20 (0.00)

30.0

20.9 (0.14)

1.89

1.32

33.6 (0.01)

0.00

0.30 (0.03)

20.1 (0.10)

1.09

0.50

19.1

1.03

0.25

1.00

0.26 (0.03) 0.31

18.5 (0.15) 12.6 (0.25)

2.11 -

0.68

0.75

0.31 (0.01)

15.8 (0.02)

0.00

0.20 (0.05)

23.1 (0.30)

1.71

0.40

12.4

0.92

23.0 (0.27)

1.70

0.20

1.00

16.0 (0.17)

0.80

0.20 (0.02)

0.27 (0.09)

0.5

0.26 (0.13)

0.75

0.25

0.75

0.20

18.7 (0.21)

13.5 (0.12)

0.19 (0.00)

0.25

1.00

0.60

0.27 (0.20) 0.25 (0.04) 1.00

0.27 (0.12)

0.27 (0.14)

1.38 -

19.2 (0.70)

1.20

17.1 (0.76)

1.07

16.7 (0.62) 22.1 (0.92) 20.0 (0.16) 28.0 (0.11)

1.04 1.38 -

1.40

17.5 (0.28)

0.87

19.1 (0.19)

0.95

0.5

0.26 (0.16)

16.1 (0.25)

1.00

9.4 (0.26)

0.26 (0.09)

0.85

0.81 -

0.28 (0.10)

14.4 (0.12)

1.53

0.5

0.28

7.5

0.80

0.25

0.75

0.27 (0.08)

10.3 (0.29)

1.10

0.00

0.20 (0.01)

17.1 (0.08)

1.79

0.40

0.20 (0.02)

8.3 (0.04)

0.86

B. stenostycha

0.60

0.20

0.91

0.20 (0.00)

25 0.14 (0.11)

0.20

1.00

0.80

49 0.14 (0.09)

7.2 (0.16)

0.20 (0.01)

33 93.3 (0.03)

15.9 (0.16)

0.00

0.15 (0.14)

0.62

16 0.14 (0.07)

11.7 (0.20)

17.2 (0.88)

P. bambusoides

0.91

0.86

0.33 (0.21)

11 90.6 (0.03)

17.1 (0.00)

0.75

0.17 (0.16)

0.25

fr/frC

P. edulis

fr (MPa)

n 15

2 2 2

0.20 0.60 0.80

0.27 (0.05)

1.00

0.20 (0.02) 0.20 (0.01) 0.20 (0.01)

8.8 (0.34)

9.6 (0.09)

12.0 (0.04) 8.9 (0.10)

13.8 (0.13)

0.94

1.25 0.92 1.43

Table 1. Summary of experimental results (Coefficient of Variation provided in parentheses)

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or 0.25t as the width of the specimen to be tested. The 0.20t specimens allowed the culm to be segmented into five different strips, whereas the 0.25t specimens allowed only four strips, but could be more easily achieved on thinner-walled samples. The specimens were cut from the inside and outside using a 9.525 mm and 19.05 mm end mill, respectively (Figure 3b). Specimen dimensions are given in Table 1. It is preferred that all specimens, with the exception of the Meyeri specimen, were tested in 4-point flexure [6]. Due to its smaller diameter, the Meyeri specimens were tested in 3-point flexure. Control specimens (denoted C in Table 1) having no cuts (i.e., α = t) were also tested. The results from these represent the gross cross-section properties against which the cutspecimen data is normalized. Alongside the flexure tests, characteristic data was collected for each bamboo sample, including the moisture content (MC) and the area of vascular bundles over the cross-section. Although this data was not directly used in the preceding experiment it provides information relevant to the relationship between physical characteristics and mechanical behavior of bamboo. These eventual connections are imperative to fully understand bamboo as a construction material.

3. Results Methods were used to create a consistent test that fails primarily in the constant or maximum moment regions. The most effective means found to produce the specimens was to use the end mill to cut the inner and outer wall of the specimen. This method produced sufficient samples of different (well-controlled) widths and also consistently resulted in specimens that failed in the constant moment region. A summary of the test results is provided in Table 1. Figure 4 shows a summary of average normal-

ized values of MOR through the (normalized) culm wall thickness (0 is the inner wall and 1 is the outer wall). The value fr/frC = 1 represents the control case in which the entire culm wall thickness is tested. Figure 4 shows that the MOR, fr, varies through the culm wall thickness in a generally parabolic manner: the modulus is greater at both the inner and outer walls and lower in the middle. These results were more prevalent in the thick-walled specie, Tre Gai. This could be due to the fact that a larger culm wall allows for different regions of the cross section to be more effectively isolated.

4. Discussion The observed trend in fr does not match the typically assumed distribution of fiber volume ratio (Figure 1). The presence of the fibers tends to weaken the transverse capacity in most cases [1,2]. Thus we would expect the relationship for fr determined from the flat-ring flexure tests (Figure 4) to be inversely proportional to the fibervolume ratio (Figure 1). While this is generally the case for the inner 70% of the culm wall, it is not the case toward the outer wall. One hypothesis is that the extreme outer layer of silica is contributing significantly to the β = 0 tests. Another interesting observation is that the average of observed moduli through the thickness of the culm wall does not equal the modulus when tested with α = 1 (control specimens). While this observation also requires additional investigation, it is believed that it can be explained by fundamental changes in the failure mode. Specimens having β = 0 include the brittle exterior silica layer. The brittle nature of this layer may drive the failure of all specimens in which it is included. Additional tests will be conducted to investigate this behavior.

Figure 3. Modified Flat-Ring Flexure Specimen: a) specimen plan dimensions; b) example of fiber gradient, SW quadrant; c) example of cutting ( β = 0, α = 0.20t and γ = 0.80t shown)

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5. Conclusions and Future Research The modified flat-ring flexure test for bamboo was demonstrated to provide fundamental data regarding the through culm wall thickness distribution of transverse MOR, fr. This value is surrogate for the transverse tension capacity of the culm. Observed trends in five genera of bamboo tested were similar. The test results highlighted what is believed to be the effect of the extreme outer layers of the culm wall on transverse behavior, however, further experiments are needed to investigate this. This pilot test was limited although shows great promise as a means for relatively simple (and field-appropriate) investigation of bamboo properties. Because access to lab equipment is not always practical in bamboo-growing and using regions, simple field tests that may be accomplished using free weights (such as the flat-ring flexure test) hold promise for enhancing the engineering acceptability of bamboo. More investigation using different means is ongoing and will be synthesized with the present to data to attempt to explain the observed behavior, especially near the outer wall. To investigate this effect, additional specimens will be tested with very small values of β in order to remove the outer silica layer away. The data developed in this test program will be coupled with other geometric and mechanical data from the same specimens to better understand the effects of fiber gradient through the bamboo culm wall. The studies, at this time, are intentionally being kept separate so as to not bias results of the final synthesis of data. Exploring bamboo will allow engineers to discover relationships between physical and mechanical characteristics. With each experiment, bamboo will become

familiarized, closer to becoming standardized, and eventually lose the reputation of being “poor man’s timber”. Overall, the cut flat-ring flexure test provides future opportunity to explore the relationship between bamboo’s physical and mechanical properties, furthering on the path to standardization and advanced bio-inspired design.

Acknowledgements Funding was provided by the Swanson School of Engineering. Mentorship was provided by Dr. Kent Harries. Lab assistance and mentorship was provided by Yusuf Akinbade, Shawn Platt, and Charles Hager.

Works Cited [1] Ghavami, K., Rodriques, C.S., and Paciornik, S., (2003) Bamboo: Functionally Graded Composite Material, Asian Journal of Civil Engineering (Building and Housing), 4(1), 1-10. [2] Dixon, P.G. and Gibson, L.J. (2014) The structure and mechanics of Moso bamboo material, Journal of the Royal Society Interface 11: 20140321. [3] Grosser and Liese (1971) On the anatomy of Asian Bamboos, with Special Reference to their vascular bundles. Wood Science and Technology 5, 290 – 312. [4] Janssen, J., (1981) Bamboo in Building Structures. Doctoral Thesis. Eindhoven University of Technology, Netherlands. [5] Harries, K.A., Sharma, B. and Richard, M.J. (2012) Structural Use of Full Culm Bamboo: The Path to Standardization, International Journal of Architecture, Engineering and Construction 1, 66–75. [6] Virgo, J., Moran, R., Harries, K.A., Garcia, J.J., and Platt, S. (2017) Flat Ring Flexure Test for Full-Culm Bamboo, Proceedings of 17th International Conference on NonConventional Materials and Technologies (17NOCMAT), Yucatán, México, November 2017.

Figure 4. Average normalized moduli of rupture through culm wall thickness

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Simulating the Natural Gas Filling Rate of Fuel Tanks Packed with Metal-Organic Framework Adsorbents Keerthi Gnanavel and Dr. Christopher E. Wilmer

Hypothetical Materials Laboratory, Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract This article describes an application of a class of superporous material called metal-organic framework. The purpose of this study is to support the use of natural gas vehicles, which have gained significant momentum over the past decade. This study describes a fuel tank design which incorporates Cu-BTC, a specific metal-organic framework, to store high density natural gas at relatively low pressures. Cu-BTCâ&#x20AC;&#x2122;s pore size and chemical properties favor the size and nonpolar characteristics of methane gas molecules. This tank design could give rise to much safer gas storage conditions, one of the main bottlenecks that restrict natural gas from being prevalent in the transportation industry. This study uses a MATLAB analysis to simultaneously solve equations of flow between the re-fueling air supply and the fuel tank. As a result, similar gas density in a compressed natural gas tank at 250 bar can be observed in an absorbed natural gas tank at 50 bar or less. This could lead to major breakthroughs in gas storage technologies. Lower pressures would equate to safe, cheaper storage conditions, making this solution much more appealing to transportation companies and to the public. Keywords: Compressed natural gas (CNG), adsorbed natural gas (ANG), metal-organic framework (MOF), Bernoulliâ&#x20AC;&#x2122;s Flow, Ergun, Peng-Robinson.

1. Introduction Alleviating dependence on petroleum for transportation has been a global concern for decades. Achieving this goal would require a more balanced energy system that utilizes solar, wind, water, nuclear, and natural gas technologies. The density of natural gas storage limits compressed natural gas (CNG) from becoming more prevalent in the transportation industry. CNG is conventionally stored at 200-300 bar at ambient temperature with mass densities of 160-210 kg/m3. To make natural gas more attractive to

consumers and industry, high densities must be achieved without resorting to such high pressures. In order to store gas at a higher density, we utilize a specific universal property of all gases: gas molecules will tend to concentrate higher on the surface of objects, when compared to their concentration in air. [9] As a result, high surface-area frameworks provide gas molecules with structures that allow them to concentrate in small spaces without resorting to high pressure. [9] This project seeks to evaluate and improve the storage density of absorbed natural gas (ANG), which use a sandlike bed of metal-organic frameworks (MOF) particles to concentrate the gas at low pressures. MOFs provide the highest surface-area per unit volume of material known to man, far beyond its nearest competitor activated carbon. [9] The U.S. Department of Transportation has set the storage density of methane for ANG to be 180 v(STP)/v (130 kg/m3) at 35 bar to make CNG a competitive alternative to petroleum powered vehicles [1]. As a result, a similar gas density in a CNG tank at 200 bar can be observed in the ANG tank at 100 bar or less [2]. MOFs are built with metal and organic chemical building blocks. These frameworks can be chemically synthesized and resemble grains of fine sand [3]. With crystal sizes ranging from microns to millimeters, MOF grains harbor trillions of nanoscale pores that allow adsorption of gas molecules. Some common MOFs are shown in figure 1.

Figure 1. Nanoscale view of four common MOFs [2]. Cu-BTC was studied by Mason et al. and found to be the optimum MOF for methane capture. [2]

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2. Methods The project consists of a thorough computational analysis of a tank filling process. Flow rate into the tank is given by an industrial, natural gas compressor [4]. As the pressure in the tank rises, the flow from the compressor reduces according to Bernoulli’s flow equation. The filling process is terminated when the desired pressure in the tank is achieved. The variables to record during the process are time, mass delivered, tank pressure, and flow rate, all in SI units. The tank-filling process was first studied in detail without the inclusion of a MOF. This was done to develop an understanding of the mechanics of free-flowing gas into a tank. The equations used in this analysis were the basic mass balance for fluid flow through a pipe, Bernoulli’s mechanical energy balance, and the Peng-Robinson equation of state for non-ideal gases. We simultaneously solve the Bernoulli equation alongside the Peng-Robinson equation-of-state using discrete timesteps. Initially, mass is delivered through the pipe, into the tank, using specifics from Bernoulli’s flow. After the initial mass is delivered to the tank, the tank’s pressure increased, which acts against the pressure of the compressor, slowing the flow of mass. The second iteration of mass delivery was therefore slightly less than the first. This process was repeated until the desired tank pressure was reached. Conventionally, volumetric delivery rates are reported by the manufacturer in cubic foot per minute (CFM) and were converted to SI units before used in any calculations. Instruments such as flow regulators allow users to adjust the delivery rate to fit their application [4]. The tank was modeled after the average 20-gallon fuel tank found in motor vehicles, modified to accommodate CNG. A visualization of this process is pictured in figure 2.

Figure 2. This process diagram shows a simplified representation of a typical CNG tank filling procedure.

After the CNG tank was studied in detail, an inert, packed bed was inserted. The Ergun packed bed equation served as the mathematical tool to analyze the effects of the inclusion of a packed bed. The initial bed was designed to consist of various grades of sand. The specifications of sand were given by the Krumbein Phi Scale and the International Scale (ISO) which outlines grain sizes from millimeters to microns [5]. A visualization of the packed bed tank is shown by figure 3.

Figure 3. This diagram illustrates the practical setup of an ANG tank. The fixed bed can be made of inert particles or MOF particles.

Incorporating the bed structure into the tank was done by designing a fixed, packed bed of particles located at the entrance of the tank. This was done to create a disturbance in pressured flow before the gas fills the remaining tank volume and loses its pressured flow. Adhering to this assumption would then allow us to apply the Ergun equation which describes pressure drop in flow. Modifications in the visual representation were made to visualize the effect of the Ergun equation. Accurately modeling the pressure drop required the adoption of a continuous flow tank for the mathematical model. Packed bed mechanics outlined by the Ergun equation could then be extended to fit the behavior of the MOF bed in the tank. Cu-BTC was chosen to make up the initial MOF bed due to its high affinity for methane [2]. A visualization of this model is shown by figure 4.

Figure 4. This diagram reveals the theoretical representation of the mathematical process. The packed bed is found in a continuous tank to adhere to Ergun dynamics and the size of the storage tank is adjusted according to the bed’s parameters.

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3. Data Acquisition & Implementation MATLAB code was written for convenient analysis. Testing with a MATLAB program allowed for quick and easy adjustments in code as well as convenient memory storage into variables. We used discrete timesteps to solve the Bernoulli equation alongside the Peng-Robinson equation of state. With each progressing time step, mass would be delivered to the tank, pressure would be calculated and adjusted, and a new flow rate would be consequently be applied to the next time step. The specifications for the compressor, which delivers the high-pressure gas, were given by Sauer Compressors USA [4]. Various starting flow rates were tested, as well as a myriad of desired tank pressures and densities. After the free-flow operation had been successfully modeled with sufficient accuracy, inert beds were inserted. The Ergun equation would apply a reduction in pressured flow from the compressor, resulting in a reduced initial flow rate. Multiple bed arrangements consisting of sand with grain sizes from 625 microns to 1 millimeter in radius were tested [6]. After successfully modeling sand beds, a Cu-BTC MOF bed was tested. The data which describes the absolute loading of methane onto Cu-BTC was acquired through a molecular simulation via the computational software called RASPA. It

Figure 5. This graph shows the pressure evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

delivered data at 300K for the volumetric and gravimetric loading that Cu-BTC allows from 1 to 200 bars of pressure. The average absolute loadings for the pressure range were recorded and stored in a data file. The bulk gas densities for methane at each pressure from 1 to 200 bars were also recorded and stored in the same data file. These values were used to add the effective volume allowed for gas adsorption due to the inclusion of MOF. Just as a sponge allows water to absorb into its pores, Cu-BTC would allow gas into its pores.

4. Results The packed bed of particles caused an observable decrease in flow. The bed slowed the flow from the compressor by a degree that was proportional to its pressure drop. Insertion of the Ergun pressure drop was done through a modification of the original driving force given by the compressor. In the expanded general mass flow equation with Bernoulliâ&#x20AC;&#x2122;s flow inserted, the Ergun pressure drop term was represented as the multiplication of the Ergun pressure drop divided by the original compressor subtracted from one. As a result, if the bed produces a high pressure drop, the driving force of tank pressure against compressor pressure will be greatly reduced. If the pressure drop is small, then the driving force will consequently remain closer to the operating pressure of the compressor.

Figure 6. This graph shows the flow rate evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

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Figure 7. This graph shows the density evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

Figure 8. This graph shows the mass evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

For example, a compressor which operates at 206 bars and 3 CFM would take 83.8 seconds to fill a 20-gallon tank from 1 to 200 bar. The inclusion of a Cu-BTC packed bed, occupying 10% of the tank, increased the fill time to 348.8 seconds. This packed bed added about 14.2 effective liters to the volume of the tank, while only occupying around 3 L itself. Inclusion of this MOF improves storage density from 162.4 to 178.9 kg/m3. The results from this simulation can be visualized by figures 5 through 8.

procedures are characteristic of high pressure, high flow rate, and a short fill-time. The inclusion of a MOF bed would increase the fill-time, create a gradual rise in pressure, and slow flow rates, significantly.

The target for gas density achieved through ANG tanks is 130 kg/m3, set by the U.S. Department of Transportation to make compressed gas fuels a competitive alternative against conventional liquid petroleum fuel [1]. The simulations performed above suggest that the addition of MOF can, indeed, elevate gas density inside a gas tank. However, to make the tank design attractive, we must perform tests at significantly lower pressure, 35 bar, while maintaining at least 130 kg/m3. Figures 9 through 12 visualize the results of this simulation. It is observed from the graphics above that the addition of a MOF bed can have a major impact the dynamics of a tank. It will allow the tank to hold more gas. Consequently, it will take longer to fill. Conventional CNG gas filling

One major complication that arises while increasing the amount of MOF inside the tank is its impact on flow from the compressor. As depth of the bed increases, the pressure drop it imparts will also increase. In order to maintain the pressure drop as compatible with compressor delivery pressure, tank dimensions must be altered to increase the area of the bed while keeping its depth at a minimum. Through a trial-and-error optimization process, we found reasonable tank dimensions to be 0.34 meters in length with cross-sectional area to be 0.22 square meters allowing for 95% of the tank to be filled with Cu-BTC MOF. As a result, storage conditions under 35 bar pressure are improved from 24.9 to 147.6 kg/m3. This result surpasses U.S. Department of Transportation standards [1].

5. Discussion The variables which have the most effect on fill-time would be the delivery of the compressor and effective volume in the tank. When inserting a packed bed, it is

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Figure 9. This graph shows the pressure evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

Figure 10. This graph shows the flow rate evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

Figure 11. This graph shows the density evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

Figure 12. This graph shows the mass evolution of a conventional compressed natural gas (CNG) tank compared to a tank which has a packed bed of Cu-BTC MOF for adsorption (ANG).

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important to consider particle size, as smaller particle sizes create a denser bed which causes a greater pressure drop in flow. As inert bed depths increase, less volume is allotted for gas storage. In the case of MOF beds, increased bed depths would increase volume for gas storage, however, the flow rate would still be significantly reduced. Therefore, a balance must be found to determine the highest loading while avoiding significant pressure drop. Several regulatory factors could make the implementation of a MOF bed inside a fuel tank somewhat difficult. MOFs have characteristic heat of adsorption values that raise the temperature of the bulk gas while adsorption is occurring. This could potentially deter government and safety agencies from endorsing the use of MOF beds, as methane is one of many flammable gases used for fuel. Implementing a coolant jacket around the tank as an energy dispersion method would improve attitudes toward ANG tanks. An analysis of more MOF types, like PCN-14 and IRMOF-1, would widen the scope of this analysis [1] . By studying the behaviors of many MOFs, additional information about the forces of attraction between the MOF structure and gas molecules may be incorporated into the simulation [2]. Additional improvements on this project would include heat dynamics between the bulk fluid and MOF framework. A considerable spike in temperature would affect the compressibility of methane, and therefore would need compensation by cooling or a slower filling [8]. Safety regulations would also require precise cooling and insulation to prevent unwanted combustion. The gas temperature could be strictly controlled to be maintained under 60oC (140oF) described by the NFPA-52 regulations that govern natural gas vehicle operation in the United States [9]. If interest and funding were concentrated on this project, a considerable improvement in the transportation industry would be observed and society would be one step closer to improved energy management.

6. Conclusion This analysis is a purely computational representation. Using basic mass transfer equations and a general understanding of a tank-filling process, we fit common parameters found in engineering to the experiment which includes MOF framework as a gas sponge inside of a natural gas storage tank. We found that the storage conditions of compressed natural gas tanks can be dramatically improved with the addition of a MOF bed structure inside of the tank. Pressure can be lowered from 200 to 35 bar while maintaining a gas density of 147 kg/m3 , well above the 130 kg/m3 standard given by the U.S. Department of Transportation to compete with conventional liquid fuels [1]. This result is a pressure reduction of 82.5%. Improvements can also be made to this computational model of a filling process by obtaining an actual CNG tank, the MOF, building the apparatus, and running realtime experiments. By comparing the experimental data to the computational model, a scientist can fine-tune the modelâ&#x20AC;&#x2122;s equations to capture the dynamics of the real system. Regardless of the challenges, the application of MOFs to natural gas storage yields exciting results about how the future can change for the better. This may push the energy and transportation industry to be one step closer in alleviating dependence upon petroleum. Refueling of a natural gas vehicle becomes easier and cheaper. Furthermore, effective gas storage technologies can be implemented in additional applications, such as medical instrumentation or even space-flight. Developing this engineering technology may pave new roads for mankind and open possibilities that were once considered out of reach.

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Acknowledgements This project was only possible with the generous funding and support from the PPG Foundation as well as the University of Pittsburgh’s Swanson School of Engineering.

References [1] Ma S, Sun D, Simmons JM, Collier CD, Yuan D et al. (2007) Metal Organic Framework from an Anthracene Derivative Containing Nanoscopic Cages Exhibiting High Methane Uptake. Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio. [2] Mason J, Veenstra M, Long JR, (2014) Evaluating metal-organic frameworks for natural gas storage. Chem. Sci, 5, 32-46. [3] Kim KJ, Li YJ, Kreider PB, Chang CH, Wannenmacher N, et al. (2013) High-rate synthesis of Cu-BTC MOF, Chemical Communications Cambridge., Oregon State University. [4] https://www.sauerusa.com/compressed-natural-gas-cng/

[5] Krumbein W.C. & Sloss LL, (1963) Stratigraphy and Sedimentation (2ndedn) Freeman, San Francisco. [6] Tovar TM, Zhao J, Nunn TW, Barton HF, Peterson GW et al. (2016) Diffusion of CO2 in large crystals of Cu-BTC MOF. Vanderbilt University, Nashville, Tennessee. [7] Subramanian, R. Shankar (2016) Flow Through Packed Bed and Fluidized Bed. Clarkson University, Potsdam NY [8] Xiao J, Hu M, Benard Pierre, Chahine R. (2013) Simulation of Hydrogen Storage Tank Packed with MetalOrganic Framework. Wuhan University of Technology, Hydrogen Research Institute, China. [9] Wilmer, E. Christopher (2014). “Efficient Gas Storage and Separation” We Solve for X. The Moonshot Factory. Presentation. [10] National Fire Protection Association. Section 52 (2009) (10th edn) Standards Council. Chicago, IL

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Binder Jet Additive Manufacturing of Magnetocaloric Foams for High-Efficiency Cooling Katerina Kimes, Amir Mostafaei, Erica Stevens, and Markus Chmielus

Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Magnetocaloric (MC) materials change temperature when exposed to a magnetic field in an adiabatic environment. The main application for these functional materials is in magnetic refrigeration because it is more efficient and sustainable. As a result, much work has gone into acquiring an alloy composition that exhibits a large temperature change near room temperature. However, no studies have been done on the additive manufacturing of these materials. Using an additive manufacturing process on the material would allow for a higher surface area, increasing effectiveness of the MC effect. In this study, Ni-Mn-CuGa powder was produced, 3D printed, sintered, and characterized in an effort to compare the properties of a MC foam with those of bulk MC materials. It was found that porosity can be systematically controlled by changing the sintering temperature. Additionally, the sintered samples showed thermal functionality with an overlapping phase transformation and Curie temperature between 29 and 37 Â°C. Ultimately, the sintering temperature had an effect on Mn evaporation, causing a slight variability in saturation magnetization and phase transformation temperatures. It was concluded that functional MC foams produced via additive manufacturing are attainable near room temperature.

and energy savings of up to 30% motivate the drive to advance magnetic cooling technology [2,3]. As seen in Fig. 1, the positive MCE arises due to magnetostructural coupling caused by an overlap of two contributions. When the first-order structural transformation from pseudo-tetragonal martensite to cubic austenite overlaps with the second-order magnetic transformation of magnetic spin alignment overlap, a giant magnetic entropy change occurs. Therefore, by modifying the composition to align the Curie temperature (TC) with the phase transformation temperature, a magnetocaloric (MC) material (MCM) can be tuned to exhibit large temperature changes in moderate magnetic fields [4].

Key Words: Magnetocaloric, Additive Manufacturing, Binder Jet, Magnetic Refrigeration

1. Introduction The magnetocaloric effect (MCE) is a phenomenon that occurs in many conventional magnetic materials where upon adiabatic magnetization, the material heats up and upon adiabatic demagnetization, it cools down [1]. The MCE was first discovered in the early 1900â&#x20AC;&#x2122;s and since then has been widely studied in an attempt to develop alloys that exhibit a large temperature change near room temperature (RT) in an effort to find a viable material for magnetic refrigeration (Figure 1). Environmental benefits

Figure 1. The magnetic refrigeration cycle due to the MCE (left) is a result of two field-induced contributions (right).

Off-stochiometric Ni2MnGa Heusler alloys are known to exhibit large magnetic field induced strains as high as 9.7% [5]. However, these alloys are very brittle. By replacing some Mn with Cu, the martensitic transformation temperature is raised and the Curie temperature is lowered, causing the magnetostructural coupling to occur around room temperature, inducing large magnetostrain,

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magnetoresistance, and MCE. Additionally, the Cu addition increases atomic ordering, allowing the alloy to favor the MCE and drastically increase plasticity [4,6]. Previous studies have been done to gather temperature and entropy data for many Ni-Mn-Cu-Ga MC alloys. Although progress has been made in finding compositions that exhibit a large MCE near room temperature and in low magnetic fields, there have not been any studies regarding the additive manufacturing (AM), or 3D printing, of Ni-based MCM and how an AM process effects functional behavior. Figure 3. Part manufactured traditionally with no internal voids that has a limited surface area (left) compared to a part manufactured with BJP that has many voids resulting in an increased surface area (right).

This interconnected porosity is of great interest for magnetocaloric materials since fluid can pass through the channels for example in a heat exchanger, increasing the contact area with the fluid, as seen in Fig. 4 [8,9]. Additionally, a porous structure will reduce grain boundary constraints, preventing cracking. It could also lead to a possible reduction in magnetic properties that could result after many field-induced cycles [9]. Figure 2. Schematic of a binder jet 3D printer.

This study focuses on the AM method of powder bed binder jet (PBBJ) printing. In PBBJ printing methods, a metal powder is spread out one layer at a time and selectively joined with a binder after each layer (Fig. 2) [7]. After printing, the â&#x20AC;&#x153;green partâ&#x20AC;? is very fragile because the metal powders have not yet joined to form a solid part. To solidify the printed green part, a sintering process is carried out to burn off the binder and densify the sample. PBBJ printing allows for the production of parts with internal porosity due to either the designed structure, such as a lattice structure (Fig. 3), or a part with interconnected porosity. Interconnected porosity can be achieved by controlling sintering effectiveness. The sintering conditions for a PBBJ printed sample can be tailored to control the density of the part, making it possible to increase the surface area within the material by allowing interconnected porosity to remain.

Figure 4. Optimized cooling using magnetocaloric material with a porous or lattice structure.

It has been shown that bulk Ni50Mn18.75Cu6.25Ga25 [10,11,12,13] Heusler alloy shows a large magnetic entropy change around 12 J/kg*K in a 2 T magnetic field and near room temperature [14]. In this study, a powder of the same composition was produced, printed, and sintered at different temperatures. The densification, microstructure, magnetic properties, and thermal properties of the printed

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and sintered parts were characterized and compared to bulk polycrystalline magnetocaloric materials.

2. Methods 2.1 Powder Production High-purity elemental Ni, Mn, Ga, and Cu were cast into ingots using an induction melting process with a target atomic composition of Ni50Mn18.75Cu6.25Ga25. The actual composition of the ingots was determined using a Zeiss Sigma 500 FESEM equipped with EDS. These ingots were broken into smaller pieces and mechanically ground into a powder using a Retsch PM100 ball mill. The powder was sieved to less than 106 µm.

3. Results 3.1 Powder Composition EDS average composition of eight ingots is Ni46.4±0.5Mn16.0±0.6 Cu8.3±0.8Ga29.3±0.8. Ingot compositions were homogeneous but varied nonuniformly from target composition. Error was greatest in the Cu, reaching almost 10%. All other elements had errors of less than 4%. 3.2 Sintering Density A linear trend was seen in the Archimedes density measurements (Fig. 5). The melting point of the material was reached between 1080 °C and 1090 °C.

2.2 Additive Manufacturing and Sintering The powder from Section 2.1 was used to produce cylindrical coupons with target dimensions of 5 mm tall by 10 mm in diameter using an ExOne Lab PBBJ printer. Samples were individually encapsulated in a quartz tube under an argon-purged vacuum atmosphere with a Ti getter and sintered for 2 hours at the following temperatures: 1000 °C, 1020 °C, 1040 °C, 1050 °C, 1070 °C, 1080 °C, and 1090 °C. All samples were air cooled. 2.3 Characterization Density was determined using the Archimedes density calculation before samples were cut. The 1000 °C sample was cut in half and mounted before imaging on a Keyence VHX-600 optical microscope. Analysis using ImageJ [15] was done to verify that the density measurement was accurate. Imaging was also done on a Nikon Optiphot differential interference contrast (DIC) microscope, and a Zeiss Sigma 500 FESEM to see twinning. Uniform rectangular prisms were cut from the center of all samples for thermal and magnetic property measurements. Thermal and magnetic properties were measured using a Pyris 6 differential scanning calorimeter (DSC) and Lakeshore 7407 vibrating sample magnetometer (VSM). Lastly, structure was analyzed using a Bruker B8 Discover X-ray diffractometer (XRD) with a Cu-kα source. For XRD and room temperature VSM, only three samples were characterized (1000 °C, 1040 °C, 1070 °C) due to time restrictions and unforeseen instrument service. These samples were selected based on density distributions to acquire representative data for the whole set.

Figure 5. Calculated densities of sintered samples.

The Archimedes density calculations were confirmed by analyzing the density of the optical image shown in Fig. 6. The void in the middle is due to loose powder that was dislodged during mechanical polishing, and was therefore

Figure 6. Optical micrograph of sintered 1000 °C sample used for density calculations.

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not analyzed for density. ImageJ analysis resulted in a density of 52.7% for the sample sintered at 1000 °C. All sintered samples were strong and did not easily scratch. Surface melting was seen starting at 1080 °C and the 1090 °C sample fully melted. Interestingly, there were visibly more pores in the cross-section of the 1090 °C sample compared to the 1080 °C sample, confirming the unexpected density results. 3.3 Thermal, Magnetic, and Structural Characterization The DSC plot in Fig. 7 shows clear phase transformation peaks near room temperature as the material goes from martensite to austenite upon heating and austenite to martensite upon cooling. There is no TC in the plot, suggesting that the TC overlaps the phase transformation temperature. The phase transformation temperatures are also slightly different for the various sintering temperatures, where samples with an increasing sintering temperature through 1050 °C show an increasing phase transformation temperature at which point it begins to decrease through 1090 °C.

Figure 8. DIC image of 1000 °C sample.

The representative high-temperature VSM results for all samples (Figure 9) shows the demagnetization temperature where the magnetization dramatically drops and approached zero. This demagnetization between 29 °C and 37 °C confirms that the TC overlaps with the phase transformation temperature.

Figure 9. High temperature VSM results for all samples show demagnetization near RT.

Figure 7. DSC plot for all samples.

The presence of room temperature martensite is verified in DIC (Fig. 8). In the DIC image, twinning can be seen as the horizontal lines across the sample. The twinning was visible throughout the sample and is characteristic of the martensitic phase.

Saturation magnetization results (Figure 10) are comparable to values of bulk Ni-Mn-Cu-Ga of similar composition [16]. The same trend as in the DSC results is also observed where the saturation magnetization of the 1070 °C and 1000 °C samples are very close and are both less than the 1040 °C sample.

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study show the same trend, suggesting that Mn evaporation is occurring during sintering. The Mn loss can be accounted for by increasing the concentration of Mn in the initial ingots or wrapping the samples in Mn during sintering. The loss in Mn can be verified by performing EDS elemental analysis on the samples. Twinning in the sintered sample is indicative of martensite in the material which is observed in the DIC micrograph. Evidence of room temperature martensite is also seen in the XRD plots due to peak splitting. The presence of room temperature martensite is promising for alloy functionality.

Figure 10. Room temperature VSM results for the selected samples.

4. Discussion Although ingots were each homogeneous, the composition variation from ingot to ingot is due to Mn evaporation during melting. This can be accounted for by increasing the mass of elemental Mn before melting. The functionality of Heusler alloys are very sensitive to composition and a slight variation from the target composition could affect the strength of the MCE. The results from the Archimedes density measurements show that increasing the sintering temperature will cause an increase in density. It is expected that the density will level off near melting temperature. However, due to the unknown melting temperature, the sintering temperature will have to be increased until melting is achieved to obtain a full sintering curve. The DSC, HT VSM, and RT VSM results all show the same trend where transformation temperatures increase until the sintering temperatures reach 1050 Â°C, at which point, the transformation temperatures begin to decrease despite the increasing sintering temperature. This curious trend is also seen in Sarkar et al. when Cu concentration is varied [11]. In this case, near 6.5 at% Cu, the Curie temperature is at a minimum. This result suggests that as Mn concentration decreases, the TC decreases and then increases, since Cu replaces Mn. The results in this

DSC and high temperature VSM results confirm the functionality of the material. Phase transformation peaks in DSC and TC in VSM both are indicative of magnetostructural coupling. The VSM results can be used in a Maxwell relation to determine the magnetic entropy of the samples. The entropy can be compared to bulk materials for a comparison of the MCE in porous and bulk MCM.

5. Conclusion The sintering study showed that porosity can be controlled by changing sintering conditions. However, it is important to note the effect of sintering conditions on composition due to Mn evaporation. As MCM are very sensitive to composition, Mn loss must be taken into account to achieve optimal functionality. It was confirmed through various characterization techniques that the additively manufactured samples had similar functionality to that of bulk material. The presence of room temperature martensite seen in XRD and DIC shows that the AM method did not have an effect on the formation of this phase at room temperature. Additionally, functionality was achieved in the DSC and VSM results. In the future, sintering temperatures will be expanded until the melting point is reached and EDS will be performed on sintered samples to determine Mn loss. Magnetic entropy will also be determined using the high temperature VSM results in conjunction with a Maxwell relation.

Acknowledgements Funding by PPG Foundation and SSOE Undergraduate Research Program. Jakub Toman, for assistance in sample prep and VSM testing.

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References [1] Liu et al. Nat. Mater. 11. 620–26, 2012. [2] Smith. The Euro. Phys. Jour. H. 38. 507–17, 2013. [3] Cherechukin et al. Phys. Lett. Sect. A Gen. At. Solid State Phys. 326. 146–51, 2004. [4] X. Zhang et al. Appl Phys Let. 108. 2016. [5] P. Müllner et al. Jour of Appl Phys. 95. 1531–1536, 2004. [6] P.-P. Li et al. Chinese Phys B. 20. 2011. [7] Mostafaei, et al. Mat. & Des. 108. 126-35, 2016. [8] J. Lozano et al. Jour of Magnetism & Mag. Mat. 320. 2008.

[9] C.P. Sasso et al. Intermetallics. 19. 952–956, 2011. [10] J. Ferenc et al. Acta Phys. Pol. A. 128. 111–115, 2015. [11] J.F. Duan et al. Jour of Appl Phys. 103. 2008. [12] T. Kanomata et al, Metals. 3. 114–122, 2013. [13] I. Dubenko et al. Jour of Magnetism and Mag. Mat. 401. 1145–1149, 2016. [14] K. Sielicki, et al. Acta Phys. Pol. A. 127. 644–646, 2015. [15] C. a Schneider et al. Nat. Methods. 9. 2012. [16] S.K. Sarkar et al. Jour. of Alloys & Comp. 670. 281–288, 2016.

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Silicon Solar Cell 92.4% Solar Spectrum Absorption Achieved Through Nanotexturing And Thin Film Etching Danielle Kline and Paul Leu

Laboratory for Advanced Materials in Pittsburgh, Department of Industrial Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Photovoltaics, the technology responsible for capturing and converting solar energy into usable electricity, is still a rapidly developing area within renewable energy research. The commercial panels commonly seen on roofs and in large-scale field arrays are far from ideal in terms of energy conversion efficiency and manufacturing cost. To remedy this, methods such as wet chemical thin film etching and reactive ion nanotexturing have been researched to reduce reflection and increase light trapping within the absorbing layer. This aims to simultaneously lower material usage and replace the antireflection coating. In this study, the effect of combining the nanotexturing technique with material reduction was observed. We predicted that thinning the silicon would increase light transmission, as modeled by our Lumerical simulation, but the transformation of planar to black silicon could potentially fix this through increased light scattering. To measure the effect, integrated absorption was calculated for both planar thin film silicon and black thin film silicon. As anticipated, solar spectrum absorption was observed at 92.4% for the black silicon, as opposed to only 54.2% for the planar silicon, indicating that the incorporation of reactive ion nanotexturing to ordinary thin film silicon provides absorption gains and significant solar cell efficiency improvement potential. Key Words: photovoltaics, black silicon, thin film, simulation Abbreviations: DRIE (deep reactive ion etching), FDTD (finite-difference time-domain), SSP (single-sided polished), DSP (double-sided polished), c-Si (crystalline silicon), SEM (scanning electron microscope)

1. Introduction The function of light trapping, one of the key efficiency enhancements explored in this study, is implied in the name: maximizing the amount of incoming light trapped within the absorbing layer by decreasing both reflection and transmission.

Figure 1 shows the basic structure of a solar cell. The absorber, in this case, is crystalline silicon, performing the task of solar to electrical energy conversion. As the light hits the surface of planar, untextured silicon, a percentage of the light is reflected, a percentage is completely transmitted, and a percentage is absorbed and converted to usable energy.

Figure 1. The basic structure of a solar cell [1]

To increase the proportion of light absorbed, the length of the lightâ&#x20AC;&#x2122;s path through the absorber should be increased, providing a longer time for the light to create electronhole pairs. In thinning down the silicon, the opposite effect occurs and transmission increases, as we modeled in this study with a Lumerical simulation. Adding nanotexturing aims to correct for this by inducing light scattering, or allowing the light to enter the absorber at a decreased angle. This light will inherently travel a longer distance within the absorber and thus facilitate more energy conversion, as shown in Figure 2.

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Although there exist a variety of methods to facilitate black silicon fabrication, DRIE is a steadfast technique. In the process, SF6 and O2 plasmas are alternated with C4F8 plasma for etching and passivation, respectively, to perform the following reaction [4]: O2 + SF6 → O* + SF5*(or SF4*) + F* (2)

Figure 2. The light path lengthening effects of nanotexturing. The length of the path, Lopt, increases from 2L to 4n2L, where n is the refractive index of the absorber, and L is the thickness of the absorber [1].

Bulk silicon can be etched down via wet chemical etching to achieve thicknesses just a few microns long. The thinned-down wafer boasts desirable properties such as flexibility, in addition to being inherently made of less material. A common etchant used is potassium hydroxide, or KOH. By adjusting the etchant bath temperature and concentration, the etch rate of the following reaction can vary immensely [2]: Si + 2OH- + H2O → SiO32- + 2H2 (1) Black silicon is an effect caused by the chemical reactions of a passivation agent and an etchant on a silicon substrate. The resulting surface appears optically, uniformly black due to the light scattering effects of the grass-like columnar nanostructures and seen in Figure 3, and efficiencies as high as 22.1% have been reported in solar cells with black silicon absorbers [2].

Variables such as gas flow rate, power, and pressure can be altered to fine tune structure geometries and consequently maximize absorber light scattering abilities. In this study, we showed that the absorber becomes exponentially more transmissive as it is thinned down, but the addition of black silicon nanotexturing compensates for the absorption losses. Combining these two fabrication methods, we were able to create a thin film black silicon sample capable of absorbing 92.4% of the solar spectrum across the silicon bandgap.

2. Methods A simulation was created on Lumerical’s FDTD and Device software to model a basic solar cell with varying absorber thickness and observe corresponding solar cell efficiencies. The three-dimensional models built in FDTD and Device can be seen in Figure 4a and 4b, respectively. The absorber, pictured in Figure 4 in red, was observed at 1-10, 15, and 20 μm.

Figure 4a-b. Lumerical FDTD (a) and Device (b) solar cell simulation models.

Figure 3a-b. Black silicon surface images at the nanoscale (a) and the macroscopic scale (b). SEM center tilt 20°.

The deep reactive ion etching was performed on an STS ASPECT Cluster Reactive Ion Etching system at Carnegie Mellon University. A 500 μm <100> SSP c-Si wafer was cleaned with acetone, methanol, and isopropanol and dried with nitrogen gas. The wafer was then loaded into the DRIE and etched according to the Bosch process [4]. A combination of SF6 and O2 were used for etching while C4F8 was used for passivation. Gas flow

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rate, power, and pressure were carefully selected to yield optimal geometries. The wafer was transported back to the University of Pittsburgh for further processing and cleaned again using the same procedure as previously described. An AMMT single series wet etching wafer holder was cleaned and loaded with the black silicon wafer, un-nanotextured side up. A VWR water bath heated 5.36 M potassium hydroxide solution to approximately 60 ± 5 °C. The wafer was submerged in the KOH for about 20 total hours. During this time, the wafer was periodically removed to be cleaned and measured with a micrometer to determine etch rate and visually inspect for defects. The wafer finished at a final thickness of 50 ± 2 μm. A second 500 μm <100> SSP c-Si wafer was also wet etched, lapped side up, using the same procedure. The final thickness of this planar thin film wafer was also 50 ± 2 μm.

Figure 5. Solar cell thickness versus total solar cell efficiency, as simulated by Lumerical.

The results from spectrophotometer processing can be seen in Figure 6.

Images were captured by a Zeiss Sigma500 VP SEM. Wafer thickness was measured by a Fowler digital counter micrometer. The planar thin film and black silicon thin film reflection and transmission were each recorded using a PerkinElmer Lambda 750 UV/Vis/NIR across a 280 1100 nm wavelength range.

3. Results Resulting efficiencies from the Lumerical solar cell simulations can be found in Table 1, and Figure 5 represents these findings graphically. Absorber Thickness (μm)

Efficiency (%)

6.38585

1 3 4 5 6 7 8 9

4.84407 7.33295 7.7378

8.16966

8.47468 8.28747

8.86597

8.88228 8.86311

9.33005 9.35656

Table 1. Lumerical absorber thicknesses and corresponding solar cell efficiencies.

Figure 6. Absorption for planar and bSi thin film across the silicon bandgap spectrum.

The planar thin film sample absorbed 54.2% of the solar spectrum while the sample with nanotexturing showed a significant improvement, absorbing 92.4% across the same wavelengths (280 – 1100 nm, also known as the silicon bandgap).

4. Discussion The logarithmic relationship found between absorber thickness and solar cell efficiency supports our previous hypothesis that planar thin film silicon will provide a shorter light path and increase transmission. Achieving ultrathin absorbing layers will come at a significant cost to the overall efficiency of the solar cell, as demonstrated by the simulation results, unless scattering of incoming light can be effectively enhanced. Applying nanotexturing to the thin film could be enough to provide this light scattering and make thin film a viable

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option, as shown in our experimental results. The addition of nanostructures to planar silicon provided a 38.2% increase in total integrated absorption across the silicon bandgap, a significant amount of which came from reduced reflection and the rest coming from decreased transmission. Figure 7a and 7b graphically shows the proportions for which reflection and transmission properties were improved.

To improve these results, alternative fabrication methods can be explored to reduce waste and scale up production for industrial settings. For example, an exfoliation method has been developed to create multiple thin film samples from a single bulk wafer [5]. KOH etching creates thin wafers relatively well, but the remainder of the bulk material is wasted, and uneven heating and bath concentration can lead to defective surfaces. Substituting exfoliation for wet chemical etching could alleviate both concerns. Another means of reducing cost, especially on an industrial scale, would be to replace DRIE with an HF/AgNO3 etch to fabricate black silicon [6]. A wet chemical etch would likely be more scalable, and therefore more practical for mass production purposes. Our lab is currently investigating this method and plans to continue its development.

Acknowledgements Special thanks to the PPG Foundation for funding my participation in this project. I would also like to thank the University of Pittsburgh Swanson School of Engineering, the Peterson Institute of Nanoscience and Engineering, the cleanroom staff at Carnegie Mellon University, Dr. Paul Leu, and Bradley Pafchek for providing me with the resources and guidance necessary to complete this research.

References Figure 7a-b. Percentage of light reflected and absorbed for black silicon thin film and planar silicon.

As seen in Figure 7, the majority of the absorption boost came from decreased reflection. To make thin film more lucrative, a larger improvement in transmission might be required, especially in the ultrathin region; however, adding nanotexturing is a good first step.

5. Conclusions By combining the light trapping effects of black silicon with thin film, we were able to create an absorber with 92.4% spectrum absorption, which proved to be a significant improvement on planar thin film for both the reflective and transmissive properties. A more significant transmission reduction may be required to make thin film a marketable concept, though.

[1] Leu et al. “2017_10_29_Swanson.” Oct. 29 2017, Microsoft PowerPoint file. [2] Wang, Y. (2016, May) KOH Etching of Silicon. New Jersey Institute of Technology. [3] Savin et al. (2015, July), Black Silicon Solar Cells with Interdigitated Back-Contacts Achieve 22.1% Efficiency. Nat. Nano. Vol. 10, No.7 pp. 624-628. [4] Jansen et al. (1996) J. Micromech. Microeng. 6 pp. 14–28. [5] Saha et al. (2013) Single heterojunction solar cells on exfoliated flexible ~25 µm thick mono-crystalline silicon substrates. Appl. Phys. Lett. 102, 163904. [6] Liu et al. (2012) Nanostructure Formation and Passivation of Large-Area Black Silicon for Solar Cell Applications. Small, 8, No. 9 1392-1397.

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Deterministic Space Networking and Time-Triggered Ethernet Modeling Joseph R. Kocik, Dr. Alan George, and Christopher Wilson

National Science Foundation Center for Space, High-performance, and Resilient Computing (SHREC) University of Pittsburgh, Pittsburgh, PA, USA

Abstract Space-computing systems rely on deterministic networking to precisely control sensors and perform maneuvers. These systems require a deterministic networking protocol that is both robust and fault-tolerant, to be usable in the harsh environment of space. Time-triggered Ethernet (TTE) is an emerging protocol that meets these requirements. Designers favor TTE because it is compatible with traditional Ethernet devices and adds additional traffic classes to ensure guaranteed and speedy message delivery. To verify the suitability of TTE on a future space missions, simulations of TTE systems were created to observe its behavior, under a variety of parameters (e.g. throughput, base period, system configurations). The latency and deviation of the system were then observed to appraise the simulated systemâ&#x20AC;&#x2122;s effectiveness under the various parameters. These tests were then compared to specifications of the protocol to determine the strengths and limitations of a TTE-compliant system. The results also exhibited potential methods to optimize the configuration and scheduling of TTE systems specifically with regards to throughput and clocking (base period). Key Words: space computing, networking, Ethernet

1. Introduction Computing is a crucial task on space missions. Numerous computing platforms need precise control and up-to-date information. To be suitable for space, a networking protocol needs to service the diverse needs of space platforms in a reliable and expedient fashion. Traffic must also always arrive with consistent timing, so that computers can act rapidly to address safety-critical situations. In networking, this concept of predictable timing is known as determinism and is a key addition to modern space networks [1]. Deterministic networking protocols are designed so that when packets are sent from one point to another, they should follow the same path at a constant latency, with minimal deviation (low jitter). A space network will be used to transmit critical messages involving the control and operation of a spacecraft, so

the possibility of failure must be minimized, through resilient and fault-tolerant computing and networking platforms. Resiliency and fault tolerance are important because space computing devices must deal with radiation which may cause devices to act erratically or cease working altogether [2]. Time-triggered Ethernet (TTE) addresses these concerns by extending traditional IEEE 802.3 Ethernet. Ethernet is commonly used, and most hardware can connect to Ethernet either natively or through an inexpensive adapter, allowing inclusion of varied devices without extensive costs or development overhead [3]. In traditional Ethernet, traffic is best-effort by nature, meaning there is no guarantee when a packet will be delivered or even that the packet is delivered at all [4]. Ethernet traffic is also event-driven, with packets being sent as events occur [3]. This is unsuitable for the environment of space. TTE improves traditional Ethernet through its system of three traffic classes which add determinism and fault tolerance. Time-triggered (TT) traffic holds the highest priority in a TTE-compliant system. TT packets transmit according to a schedule with an adjustable period. The schedule is predetermined and distributed to both bridges and end-systems. Scheduling guarantees delivery, minimizes latency, and achieves a near constant jitter, ensuring determinism [3]. These guarantees ensure reliable transmission suitable for critical messages, such as navigation, sensor status data, or warnings in the case system failure. The intermediate priority is rate-constrained (RC) traffic. RC traffic is event-driven and cannot be pre-scheduled [4]. It is allocated a set bandwidth, and has reduced latency and jitter, though not as low as TT traffic. It also supports guaranteed delivery. RC packets may still collide with one another, introducing higher variability in latency (jitter) of traffic [3]. The lowest-priority traffic class is best-effort (BE). BE traffic follows the model of classical Ethernet with no guarantees in latency, jitter, or arrival. Best-effort traffic is also event-driven and lacks predetermined scheduling.

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BE traffic allows for interfacing with traditional Ethernet and is only suitable for non-critical packets, occupying bandwidth unused by TT and RC packets. Through these traffic classes, high-priority messages are guaranteed low-latency delivery with minimal jitter. TTE is of interest to designers because it is compatible with traditional Ethernet devices through extensions or best-effort traffic. The relative ease of interoperability between diverse hardware reduces costs. The allowance of multiple traffic classes to coexist under one protocol also allows for diverse traffic to be passed using one network. Multiple traffic classes are useful in space systems, and supports the concept of Integrated Modular Avionics, allowing shared hardware to perform multiple distinct functions, which has become increasingly popular [5]. Before TTE can be integrated into a space mission, testing and experience with the technology are necessary. Research into TTE allows for comparison with existing and emerging technologies to determine the optimal network for a given situation. Simulations of a TTE-compliant system were constructed to evaluate capabilities and limitations of TTE, while also investigating suitability for future space missions and preparing for future experiments on a hardware realization of TTE. 1.1 Networking Terminology Networks consist of interconnected computers, with connections governed by layers of networking protocols [1]. These computers, known as nodes or end-systems, connect to one another via bridges which are responsible for forwarding traffic from a source node to a destination node. The amount of time it takes a packet to arrive at a destination from a source is the latency [3]. The difference between maximum and minimum latency is known as the jitter [6].

2. Methods 2.1 VisualSim Simulations of a TTE-compliant simulation were created using VisualSim Architect, a commercial, discrete-event, systems simulation software developed by Mirabilis Design. VisualSim is also outfitted with a TTE-compliant library. This library includes nodes, traffic generators, bridges. Nodes and traffic generators were combined to create end-systems. A special TTE statistics generator was used to create outputs relevant to a system using the TTE protocol.

2.2 Hardware Target System This research targeted the TTE Development System, a hardware testbed produced by TTTech Computertechnik AG. The TTE Development System consists of four endsystems that may be both the source and the destination of traffic. It contains two bridges for routing traffic between the end-systems.

Figure 1. Two default configurations of a hardware testbed, dual-channel (top) and multihop (bottom).

There are two default configurations for the TTE Development System (Figure 1). The multihop configuration, as the name suggests, requires hopping across multiple bridges for packets to pass between the two pairs of nodes. The dual-channel configuration has fully duplicated connections between the bridges, adding fault tolerance via dual-modular redundancy. 2.3 TTE Development System Model The model (Figure 2), created in VisualSim, was designed to emulate the TTE Development system. It contains the four end-systems and two bridges. The stats blocks were used to output graphics showing the latency of the system. Another version was used for collecting results, in which the stats blocks were replaced with customized script blocks that allowed for batch data collection. The system configuration could be modified to either dual-channel or multihop. The traffic tables were edited to modify traffic topography. The traffic classes, destinations, and sources could all be set, as well as the intended throughput. In the simulations, all three traffic classes were used. The end-systems were paired with Node1 sending traffic to Node4, Node2 sending to Node3, and vice versa. Each node had two streams of RC traffic, and one stream of BE traffic each sending packets with a target throughput

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Figure 2. VisualSim model to mimic a hardware testbed. A node block, traffic block, and stats block together make up one end-system.

of 0.5 Mbit/s (unless otherwise specified). Each sent TT traffic with a throughput scaled inversely with base period according to the following equation: (10-6×64)÷Base Period. 64 bytes was the size of one TT packet for this set of simulations. The scaling throughput was used to reduce high latency observed in simulations combining high throughput with a long period. The simulation was run at a series of logarithmically scaled, base periods ranging from 10 µs to 10 ms. Both configurations were simulated for comparison. Average latency was used to holistically represent latency, and standard deviation of latency was used as an analog for jitter. The simulation was also run at high levels of throughput for rate-constrained and time-triggered traffic (exceeding 100 Mbit/s with other values remaining the same), to measure the ability of higher-priority traffic to displace that of lower priorities. Graphics were produced reflecting these results using the Bokeh Python visualization library, as presented in the next section.

3. Results Average latency and standard deviation of latency were the primary results used to investigate the system. These results were obtained through the customized VisualSim statistics script blocks, which returned the average and standard deviation of latency based on the stream of packets it received. The results obtained from the system showed that on average dual-channel had lower latency (Figure 3). This observation was true across all traffic classes and across the tested range of periods. The results analyzed included only measurements of traffic between

Figure 3. Average end-to-end latency (log) in seconds of traffic sent from Node1 to Node4 for dual-channel (squares) and multihop (circles) configuration across a base period (log) with a range of 10 µs to 10 ms.

Node1 and Node4, but were the same regardless of source and destination. The average end-to-end latency was reasonably consistent across rate-constrained and best-effort traffic, with very slight decreases in latency as base period grew (Figure 3).

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Conversely, TT traffic’s latency grew with the base period (Figure 3). The average latency was consistently higher in multihop configuration compared to dual-channel configuration. This higher average latency is most evident in time-triggered traffic, where the latency is approximately 1 base period higher in multihop then in standard deviation for all tested values (Table 1).. The standard deviation of latency was inconsistent in TT traffic. Neither dual-channel nor multihop had a consistently higher standard deviation. Both were low, except for multihop at a base period of 10 µs with a standard deviation of 4.305 µs (Table 1). RC and BE traffic consistently had lower standard deviation in dual-channel then in multihop (Figure 4). This lowered standard deviation was observed, at varying magnitudes, across all tested periods and appears more pronounced in RC traffic than in BE traffic. Figure 4. Standard deviation of end-to-end latency (log) in seconds across base period (log) for traffic sent from Node1 to Node4. Legend and parameters are the same as Figure 3.

Base Period

10.0 µs

100.0 µs

1.0 ms

10.0 ms

Base Period

10.0 µs

100.0 µs

1.0 ms

10.0 ms

Dual-channel

Mean Latency

16.188 µs

106.239 µs

1.006 ms

10.006 ms Multihop

Mean Latency

28.568 µs

206.158 µs

2.006 ms

20.006 ms

4. Discussion SD of Latency

436.871 ns

706.094 ns

284.973 ns

0.0 ps

SD of Latency

4.305 µs

183.214 ns

0.0 ps

153.109 ns

Table 1. Mean and standard deviation (SD) of latency for traffic traveling from Node1 to Node4 in the dualchannel (top) and multihop configuration (bottom).

When simulations were run with TT traffic set to high throughput (100 Mbit/s), RC and BE throughput decreased to zero message transmission. When RC traffic was set to high throughput, TT traffic still transmitted with higher standard deviation. Once again, no BE traffic was transmitted. The TTE Development System model’s performance was in line with expectations of the TTE protocol. The models exercised determinism and reliability when sending packets. The system prioritized TT traffic, followed by RC, with BE packets using leftover bandwidth. This prioritization was evidenced by high-throughput, TT traffic being able to throttle RC and BE traffic and highthroughput RC traffic throttling BE. There was a slight deviation from the protocol in that RC traffic at high throughput would increase the deviation in TT traffic. This departure from the protocol is most likely due to the system running at a throughput so large that attempts to service RC traffic will interfere, even with higher-priority traffic. Under nominal conditions all traffic had low latency and standard deviation, which is especially important for TT traffic, as the TTE protocol dictates that latency and jitter (indicated by standard deviation) are required to be kept low. The average latency increased with the base period for TT traffic. This increase is consistent with expectations, as a TTE traffic is periodically scheduled and transmission will coincide with the clock signal provided by base period. This additional latency derived from

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periodic scheduling also explains the higher latency in multihop compared to dual-channel, as each bridge will have to wait for a clock signal to send a packet forward. Thus, the multihop configuration would require an extra cycle for end-to-end transmission. This extra cycle could indicate a potential to reduce latency in TT traffic by minimizing the number of bridge hops.

Acknowledgements

In TT traffic, standard deviation appeared to be independent of system configuration, as neither dual-channel nor multihop had consistently lower standard deviation. This independence is most likely because rigid scheduling should mitigate conflicts regardless of configuration. RC and BE traffic’s lower standard deviation for dualchannel then multihop is likely due to the ability to use either bridge in a dual-channel configuration, reducing congestion and allowing for consistent latency.

References

5. Conclusions and Future Work

[3] A. Loveless, “On TTEthernet for Integrated FaultTolerant Spacecraft Networks,” AIAA SPACE 2015 Conf. and Exposition, 2015.

Time-triggered Ethernet is an appealing option, with its deterministic message transmission and support for diverse computing operations. However, before use in space, more experience and evaluation of the protocol was required. This experience was achieved through research of the protocol, comparison with existing protocols, and simulations of a TTE system. A simulation of the TTE Development System showed the benefits of the protocol. The simulation performed as expected with a variety of parameters. The determinism, low latency, and low standard deviation display the potential of TTE to meet the strict requirements of space computing. The ability to accommodate diverse traffic also underlines the potential of using TTE to support the Integrated Modular Avionics model, where more computing tasks can be condensed onto one platform. This research enables future investigations using the TTE Development System as a hardware testbed. The model results, while promising, will be verified in future research using a real TTE system.

The authors would like to thank SHREC students who assisted in this research. The authors wish to acknowledge SHREC members, the Swanson School of Engineering, and the Office of the Provost for funding this research, and Mirabilis Design for free use of their VisualSim tool. [1] D. A. Briscoe and J. M. Gwaltney, “Comparison of Communication Architectures for Spacecraft Modular Avionics Systems,” NASA Center for AeroSpace Information , 2006. [2] C. Wilson, A. George and K. Ben, “A Methodology for Estimating Reliability of SmallSat Computers in Radiation Environments,” 2016 IEEE Aerospace Conference, pp. 1-12, 2016.

[4] Aerospace Standard SAE AS6802, 2016. [5] P. J. Prisaznuk, “Integrated modular avionics,” Proceedings of the IEEE 1992 National Aerospace and Electronics Conference, vol. 1, pp. 39-45, 1992. [6] H. Kopetz, A. Ademaj and P. Grillinger, “The Time-Triggered Ethernet (TTE) design,” Eighth IEEE Int. Symp. on Object-Oriented Real-Time Distributed Computing (ISORC’05), pp. 22-33, 2005. [7] D. Law, D. Dove, J. D’Ambrosia, M. Hajduczenia, M. Laubach and S. Carlson, “Evolution of Ethernet Standards in the IEEE 802.3 Working Group,” IEEE Communications Magazine, vol. 51, no. 8, pp. 88-96, 2013.

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Induction of Regulatory T Cells for Treatment of Periodontitis: Analysis of the Effect of Tri-Factor Microparticle Treatment on Disease Outcomes Kayla M. LeMaster and Ashlee C. Greene

Little Lab, Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, PA, USA

Abstract Periodontitis is an advanced form of gingivitis where the tissue around the teeth becomes inflamed and the associated alveolar bone decays. Although initiated by bacteria in the mouth, the disease is propagated by the body’s inflammatory immune response. Regulatory T cells (Tregs) are immune cells that can combat the destructive effects of the inflammatory response by restoring immunological homeostasis in the periodontal environment when present in sufficient numbers. For this reason, we have developed degradable polymer-based microparticles for the controlled drug delivery of three factors (TGF-β, IL-2, and rapamycin) that are effective in the local induction of Tregs. These tri-factor microparticles are administered in a model of murine periodontitis. In this study, the three factors are separately encapsulated using a single or double emulsion fabrication technique and administered to male mice. After 30 days, the mice are sacrificed and the maxillae are dissected and defleshed, imaged microscopically and analyzed via ImageJ software. In our preliminary results, on average we observed trends suggesting disease amelioration from the tri-factor microparticle treatment, although bone loss in the treated mice was not determined to be statistically significant (as per a one-way anova test with p-value 0.5626) to that of non-diseased control mice. Data suggest that further experimental tests would aid in concluding whether or not there is a statistically significant difference between the treated mice and/or the control mice versus the nontreated mice. Key Words: Regulatory T cells, periodontitis, microparticles, immune response

1. Introduction Periodontal disease is currently the most pressing oral health concern by the American Dental Association, affecting 64 million adults in the United States alone [1]. Periodontitis is a disease initiated by the buildup of

bacteria, plaque and tartar on and around the gingival tissue. When this buildup is present, the body’s immune system fights the bacteria but results in an adverse inflammatory response that causes destruction of the alveolar bone and of the surrounding tissue. However, current management of the disease focuses on disease symptoms rather than targeting the underlying unregulated immune response. For example, nonsurgical treatments include the mechanical removal of plaque to reduce the bacterial buildup, the use of antibiotics to kill the bacteria, and meticulous personal oral care. In advanced cases, surgical treatments include bone and gum grafting: a procedure to build new gum and bone in areas damaged due to the disease [2]. Meanwhile, in the literature, treatments for periodontal disease have focused on drug delivery using gels [3], implants [4], and microparticles [5] containing antibiotics such as minocycline [3] and doxycycline [4, 5]. Unfortunately, these treatments are inhibited by inherent downfalls; these treatments often result in poor delivery to the site of bacterial infection or are ineffective due to bacterial antibiotic resistance [6]. We have proposed a different approach through the release of a mixture of anti-inflammatory immunosuppressants and cytokines rather than antimicrobial drugs, thus targeting the primary cause of disease exacerbation: the underlying inflammatory immune response. Recently, regulatory T cells (Tregs), a type of immune cell, have been shown to be able to reduce periodontal inflammation by bringing the oral environment to immunological homeostasis [7]. To take advantage of this effect, we have identified the co-delivery of three factors (TGF-β, IL-2, and rapamycin) as an effective approach to produce a local environment favorable for the induction of Tregs [8]. It is known that TGF-β is a Treg differentiation factor, IL-2 is a lymphocyte proliferation factor, and rapamycin guides T cells to mature into Tregs [8]. When administered in vivo, they work together to induce Tregs. A steady, local presence of the three factors in the periodontal environment is desired to combat the destructive inflammation

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that propagates the disease. Thus, a controlled drug delivery vehicle composed of biodegradable poly(lactic-coglycolic acid) (PLGA) was chosen. In this study, TGF-β, IL-2, and rapamycin are encapsulated separately during microparticle (MP) fabrication and later administered to mice (TGFβMP, IL-2MP, and rapaMP, respectively). The overall aim of this research is to examine the effects of the tri-factor microparticles on disease outcomes, and alveolar bone loss in a murine model.

2. Methods Microparticles were fabricated as follows: RapaMP were produced via the single emulsion-evaporation technique due to the molecule’s low solubility in water; the drug is added to an oil phase, where it is stable, before being added to an outer aqueous phase. This technique uses a homogenizer at 3000 rpm. Fifty (50) μl of rapamycin (Alfa Aesar, Haverhill, MA) was added to a solution of polylactic-co-glycolic acid (PLGA; RG502H, Sigma Aldrich, St. Louis, MO) in dichloromethane (DCM) then homogenized in 2% polyvinyl alcohol (PVA) to create the oil-in-water emulsion. In comparison, due to the proteins’ high solubility in water, TGFβMP and IL-2MP were formulated using the water-in-oil-in-water double emulsion-evaporation technique. Herein, the proteins are added to an aqueous phase where they are stable, then this aqueous layer is surrounded by an oil phase and an outer aqueous phase to form the microparticles. This required the use of a sonicator at 25% amplitude for the initial emulsion and a homogenizer at 3000 rpm for the second emulsion. Specifically for the TGFβMP, 50 μl of recombinant human TGF-β (Peprotech, Rocky Hill, NJ) was added to a solution of polymers RG502 and mPEG-PLGA dissolved in DCM. The mixture was sonicated for 10 seconds then homogenized in 2% PVA. For the IL-2MP, 50 μl of recombinant mouse IL-2 (R&D Systems, Minneapolis, MN) was added to a solution of PLGA in DCM. The mixture was sonicated for 10 seconds then homogenized in a salt solution of 2% PVA containing 51.6 mmol NaCl. After homogenization, all batches were mixed into separate beakers of 1% PVA and stirred for 3 hours on ice. The particles were then isolated by centrifugation, washed with Milli-Q water, frozen in liquid nitrogen, and lyophilized. Between analyses, they were stored in a -20 °C freezer. Analysis of the fabricated microparticles was a multistep process: the morphology of the microparticles was characterized using scanning electron microscopy (SEM) and the size of the microparticles was measured

using a volume impedance method via Coulter Counter. Microparticles were also characterized for their release of drug over time; they were rotated in an incubator at 37 °C and the release profiles were obtained. To evaluate the effects of the tri-factor microparticle treatment, male mice maxillae were dissected, defleshed and stained with methylene blue. The teeth were photographed under a microscope and measurements were performed using ImageJ software. As displayed in Figure 1, the area between the cemento-enamel junction (CEJ) and the alveolar bone crest (ABC) was traced and measured for each set of teeth.

Figure 1. Quantifying periodontal disease. The area between the alveolar bone crest (ABC) and the cemento enamel junction (CEJ) is measured.

3. Results Sizing and imaging results are shown in Figure 2. On average, rapaMP, TGFβMP, and IL-2MP were 12.89 μm, 17.14 μm, and 25.78 μm in diameter, respectively. Also, each type of microparticle varied in morphology; rapaMP were spherical without pores, TGFβMP were non-spherical without pores, and IL-2MP were spherical with pores due to differing fabrication conditions. The IL-2MP used PVA containing 51.6 mmol NaCl, forming a porous structure as the osmolarity difference pushed water into the spheres. The TGFβMP were produced with two polymers: RG502 and mPEG-PLGA, causing the microparticles to form in a non-spherical fashion. In contrast, the rapaMP did not require any additions to polymer or PVA, explaining the smooth, spherical surface structure. Next, cumulative release profiles were obtained after 23 days for rapaMP or 30 days for TGFβMP and IL-2MP and are shown in Figure 3. The particles were formulated for a 30 day release to last the length of the disease model that is based on previous studies in literature where disease analysis was performed 30 days after bacterial

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Figure 2. SEM images and Coulter Counter size distribution of (A) rapaMP, (B) TGFÎ˛MP and (C) IL-2MP.

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Figure 4. ABC-CEJ area measurements obtained through ImageJ analysis of microscopic images.

Shown in Figure 4 are average area measurements of the studied area of male maxilla for three separate groups. Group A was bacteria-induced diseased mice with no treatment, group B was bacteria-induced diseased mice with tri-factor microparticle treatment, and group C was age control mice – mice that did not receive bacteria or treatment. The disease untreated mice have the largest average of alveolar bone loss while the disease treated and the control mice have about the same average area. The treated, diseased mice showed a decrease in average ABC-CEJ area (only 337870.8246 square µm) relative to the untreated, diseased mice (369557.1248 square µm). This preliminary data suggests the tri-factor microparticle treatment may have potential for reversing the progression of the disease.

4. Discussion Figure 3. Release profiles of microparticles.

introduction [9-11].. Thirty days of rapaMP samples are available but 23 days were tested because our previous studies have indicated no change in the plot’s plateau after about 20 days [8]. The rapamycin plot shows a steady increase before leveling off; around the 20-daytimepoint, the breakdown of the polymer caused a slight increase in the release, but based on previous studies, it is expected to plateau [8]. The IL-2 output shows a slight burst phase, as desired, since IL-2 is a proliferation factor – the initial burst phase indicates that enough drug will be present for cells to begin multiplying. The TGF-β plot shows a level release after the first day.

In order to examine the results of the tri-factor microparticle treatment, average area measurements for three separate groups were compared. It was observed in Figure 4 that the disease treated mice showed a quantitative decrease in area compared to the disease untreated mice. In periodontitis, the area between the CEJ and ABC, shown in Figure 1, increases as the disease progresses and the inflammatory environment initiated by bacteria destroys the associated bone and tissue. Thus, a smaller area measurement indicates less bone and tissue loss; the disease treated mice underwent less destruction in comparison to their disease untreated counterparts. Furthermore, the disease treated and the control mice showed matched area measurements. An initial one-way anova test (p-value 0.5626) suggested there is no statistically significant difference between the area measure-

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ments of the disease treated and the control mice. In comparison to the disease untreated mice, trends in these preliminary results suggest that the treatment successfully combatted the destructive effects of the inflammatory immune response, thus decreasing the amount of tissue and bone decay that occurred in the disease treated mice to the same level as that of the age control mice. Rather than attempting to reduce the amounts of bacteria, as other groups have [3-5], we instead focused on the underlying immune response. Using these preliminary results as a foundation, we plan to further our analysis of the effects of the tri-factor microparticle delivery system by continuing to investigate the progression of disease by quantifying bacteria levels via a swab test and PCR.

evaluation of its effect on experimental periodontitis models. Drug Delivery, 23, 525-531, 2016. EBSCOhost, doi:10.3109/10717544.2014.929195.

5. Conclusions

[6] Xiong, M., et al. Delivery of antibiotics with polymeric particles. Advanced Drug Delivery Reviews 78, 63-76, 2014.

Overall, the results of this initial male mice study suggest that the tri-factor microparticle treatment may prevent the severity of disease symptoms by producing less alveolar bone loss in our murine model, determined by the decrease in average area from the untreated to treated mice from 369557.1248 square µm to 337870.8246 square µm, respectively. A one-way anova test between the treated and control groups suggests that the difference in area between these two groups is not statistically significant. Moving forward, the effects on both male and female mice will be studied as current literature suggests that when compared to their male counterparts, female mice’s sex hormones advance the pathology of periodontitis [12]. To explore this idea in further studies, the above methods will be reproduced to investigate any differences on disease outcome that may be due to the presence of the female hormonal environment.

Acknowledgements Funding was jointly provided by the Swanson School of Engineering at the University of Pittsburgh, and the Office of the Provost.

References [1] Eke, P.I. et al. Prevalence of periodontitis in adults in the United States: 2009 and 2010. J Dent Res 91, 914-920 (2012). [2] Periodontal (gum) disease. NIH Publication No. 13-1142. National Institute of Dental and Craniofacial Research (2013). [3] Ruan, H. et al. Preparation and characteristics of thermoresponsive gel of minocycline hydrochloride and

[4] Do, M. et al.In situ forming implants for periodontitis treatment with improved adhesive properties. European Journal of Pharmaceutics and Biopharmaceutics 88, 342-350, 2014. [5] Mundargi, R. et al. Development and evaluation of novel biodegradable microspheres based on poly(d,llactide-co-glycolide) and poly(ε-caprolactone) for controlled delivery of doxycycline in the treatment of human periodontal pocket: In vitro and in vivo studies. J Controlled Release 119, 59-68, 2007.

[7] Glowacki, A.J. et al. Prevention of inflammationmediated bone loss in murine and canine periodontal disease via recruitment of regulatory lymphocytes. Proc Natl Acad Sci U S A 110, 18525-18530 (2013). [8] Jhunjhunwala, S. et al. Controlled release formulations of IL-2, TGF-β1 and rapamycin for the induction of regulatory T cells. J Controlled Release 159, 78–84, 2012. [9] Garlet, G. et al. Cytokine pattern determines the progression of experimental periodontal disease induced by Actinobacillus actinomycetemcomitans through the modulation of MMPs, RANKL, and their physiological inhibitors. Oral Microbiology Immunology 21, 12–20, 2006. [10] Garlet, G. et al. Actinobacillus actinomycetemcomitans-induced periodontal disease in mice: patterns of cytokine, chemokine, and chemokine receptor expression and leukocyte migration. Microbes and Infection 7, 738–747, 2005. [11] Repeke, C. et al. Evidences of the cooperative role of the chemokines CCL3, CCL4 and CCL5 and its receptors CCR1+ and CCR5+ in RANKL+ cell migration throughout experimental periodontitis in mice. Bone 46, 1122-1130, 2010. [12] Shusterman, A. et al. Genotype is an important determinant factor of host susceptibility to periodontitis in the Collaborative Cross and inbred mouse populations. BMC Genet 14, 68, 2013.

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Combining Metal Nanomeshes and Nanostructured “Hazy” Glass as an Alternative to Transparent Conducting Oxides Maxwell Lindsaya, Rafael Rodrigueza, Sajad Haghanifarb, and Dr. Paul Leub

Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, b Department of Industrial Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Transparent conductive oxides (TCOs) are an important component in thin film solar cells, as they provide excellent transmission and haze factor (> 80%) over the visible spectrum and low sheet resistance (< 100 Ω/sq). However, they introduce complexity and high cost to the thin film solar cell manufacturing process. This work proposes an alternative to common TCOs by combining the concepts of nanostructured glass and metal nanomeshes. We demonstrate sheet resistances as low as 3.3-11.8 Ω/sq, haze factors of > 80%, and total transmittance values of 58%-70% for the entire visible spectrum. Key Words: Thin-film Solar cells, Transparent Conductive Oxides, Nanostructured Glass, Metal Nanomesh

1. Introduction Thin film solar cells based on hydrogenated amorphous (a-Si:H) or microcrystalline silicon (µc-Si:H) still remain among the most promising technologies to advance the photovoltaic market. Substantial reductions in manufacturing costs, material usage, and processing time are the primary advantages that thin film solar cells, and especially a-Si:H, offer. However, these cells have yet to exceed 10% efficiency, which is the main obstacle of this technology[1][2]. Different approaches have been developed in order to boost thin film solar cell efficiency. The two main methods have been the use of other materials like GaAs or CdS/ CdTe rather than a-Si:H and the concept of enhancing light scattering and absorption. For the second approach, a fundamental component is the transparent conductive oxide (TCO) layer depicted in Figure 1[2][3]. Increasing the length of the light beam that reaches the absorption layer is necessary for improving the photovoltaic effect of transforming light into electricity. Therefore, it is essential to have a surface with high haze that scatters the light before it reaches the silicon absorber layer. Also, decreasing optical losses due to reflection at

Figure 1. Tandem Structure of a thin film a-Si:H solar cell[2]

the TCO/absorber interface is critical in order to achieve maximum light collection; thus, a surface that reduces reflection at this contact is suitable. Moreover, since all of the light is not absorbed by the silicon layer at the first moment the light reaches it, it is important to trap the light and not allow it to escape so that maximal light absorption may be guaranteed[2]. These three conditions plus the necessary transparency (more than 80% in total transmission for the visible spectrum) and the necessary sheet resistance (less than 100 Ω/sq) are met by using surface textured TCOs. Most common TCOs are different dopant versions of tin oxide (SnO2)[4] and zinc oxide (ZnO)[5]. The three most widely used are indium-doped tin oxide (ITO), fluorinedoped tin oxide (FTO) and aluminum-doped zinc oxide (AZO). Several investigations in the last fifteen years

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have analyzed the ability of these TCOs to improve the efficiency of thin-film solar cells by increasing light scattering and trapping[3][4][6]. Most common reported values of haze factor and total transmission results range between 80-90%[5][7]. These investigations also addressed the role of different morphologies of textured surfaces, looking for the most efficient texturing pattern to further enhance the aforementioned optical properties[6]; Ashaby-U, a W-textured and a cauliflower-type are some of the most popular subwavelength structures used to texture the TCOs. Even though the reported results are promising, textured TCOs represent another layer of complexity and extra cost for thin film solar cells. Therefore, alternative methods that are capable of mimicking the same outstanding optical and electrical characteristics should be developed. The use of less expensive materials that are already present in solar cells and possess characteristics similar to textured TCOs represents a more sustainable, cost efficient approach for enhancing thin film solar cell efficiency. In this regard, glass represents the very first option when looking for similar high quality optical properties, especially since it is already commonly used for the encapsulation layer of solar cells. Glass, but in particular, fused silica, has an almost constant value of 93% total transmission over the entire visible spectrum. Depending on its purity, the index of refraction ranges between 1.5-1.9. However, plain fused silica surface does not scatter light (its haze factor is less than 1%), and it is an insulator (ρ > 1010 Ω cm). •

Nevertheless, recent investigations have addressed different optical properties of surface textured glass[8]. Thus, further investigation into textured glass as an approach to obtain high haze and high transmission surfaces is justified. Similarly, there are some investigations that have developed the concept of a metal nanomesh (NM), especially those made of cheap metals like copper, as a solution that is able to produce conductive surfaces while maintaining high values of light transmission[9][10]. Therefore, the purpose of this project was to develop a device that incorporates both metal NMs and nanostructured glass [11] in order to achieve the same characteristics as common surface textured TCOs.

2. Experimental Settings Approximately 1 in2 square samples are cut from fused silica wafers. The samples are then cleaned thoroughly with acetone, methanol, and isopropanol and dried with nitrogen gas. Next, the samples are transferred to a TRION Phantom III Reactive Ion Etcher (RIE). A process gas flows into the RIE chamber, and the resulting RF plasma etches the surface of the samples. The physical ion bombardment physical and chemical reaction between silica and fluorine-based gases develop subwavelength needle-type structures on the surface through a common dry etching process. These structures effectively scatter light and cause the samples to look hazy to the naked eye. Then these “hazy glass” samples are placed with the needle-like structures facing down in a clean, 6 in glass Petri dish to begin the copper NM fabrication process, a schematic of which is shown in Figure 2. The Petri dish is filled with deionized water until the samples are 1-2 mm under the surface of the water. A syringe is used to create a monolayer of polystyrene (PS) microspheres at the air/water interface. With the addition of a drop of 1 wt% surfactant (sodium dodecyl sulfate) solution, the PS microspheres assemble into a hexagonally close-packed (hcp) arrangement. Excess water is drained out using a syringe, depositing the microspheres onto the substrates at the bottom of the petri dish. Next, the diameter of PS microspheres is reduced through RIE with oxygen plasma. Electron beam evaporation at approximately 10-8 Torr is then used to deposit a 50 nm thick layer of Cu onto the substrates using a Pascal Technologies UHV Dual E-Beam Evaporator. Lastly, the PS microspheres are lifted off of the substrate through ultrasonication in ethanol at 40 °C. The resulting samples have the hazy glass structure on one side and a NM of copper on the opposite side. This metal NM processing method can be scaled up by increasing the size of the vessel in which the hcp PS sphere monolayer is created. The NM metal thickness, pitch, and width can be altered for specific applications by varying the evaporation time, micro/ nanosphere size and etching time, respectively. Transmittance data was collected using a Perkin Elmer Lambda 750 spectrophotometer, and sheet resistance data was collected using an Everbeing International Corp. four-point probe station equipped with gold plated probe tips. A Zeiss Sigma 500VP SEM was used to take SEM images.

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Figure 2. Schematic of the fabrication process of a metal nanomesh on a fused silica substrate with hazy glass nanostructures.

3. Results and Discussion Through the NM fabrication process in Figure 2, Cu NMs were consistently produced with sheet resistances in the range of 3-12 Ω/sq and with total transmittances of about 80% (at a wavelength of λ = 550 nm). It should be noted that, solely from geometric considerations, the maximum transmittance of such a nanomesh structure is 90.7% [10]. Plasmonic effects can be neglected, as the only hole diameter included in this investigation is 1.4 µm, which is greater than any wavelength included in the transmittance spectra. This indicates that only a ~10% loss in transparency is caused by imperfections in the NM lattice. The optical image of a Cu NM on an unetched fused silica sample in Figure 3a illustrates the high transparency of the NM, and the SEM images in Figure 3b-c illustrate its high degree of ordering. The optical properties of the hazy glass alone were evaluated before combining the two nanostructures. Figure 4a depicts the evolution of haze factor results as a function of etching time. Starting with a 0 min etching process and reaching a 180 min etching process, the haze

factor constantly increases from less than 1% (corresponding to plain fused silica) up to a constant value of 98% through the entire spectrum. Figure 4b illustrates the effects of the same increasing etching times on the total transmission of the samples. Starting at a constant 93% total transmission that corresponds to plain fused silica (0 min etching), the total transmission slightly increases with the first two increments on etching time, reaching values slightly above 95%. However, further etching starts to deform the constant curve, enhancing the transmission on the visible spectrum but reducing it in the UV and NIR regimes. Etching for more than 60 min negatively affects the transmission in the entire spectrum, but even the 180 min etched sample did not show values below 80% total transmission within the visible spectrum. Figures 5a-b show SEM images of the textured surfaces of the 10 min and 180 min etching samples, respectively. Needle-type structures were developed during the etching process with heights ranging between 0.8 µm to 15 µm, depending on the etching time (etching rate: ~0.08µm/ min). It is important to note that the data indicate a

Figure 3. Optical (a) and SEM (b) and (c) of a Cu NM created on an unetched fused silica substrate with polystyrene microspheres of diameter 1.4 µm and Cu thickness of 50 nm.

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Figure 4a-b. a) Haze Factor results for different etching times; b) Total Transmission results for different etching times

Figure 5a-c. a) SEM image of the needle-type structures (~0.8µm in height) developed after 10 min etching; b) SEM image of the needle-type structures (~15µm in height) developed after 180 min etching.; c) Optical picture where the tradeoff between Haze Factor and Total transmission can be observed.

correlation between the height of these subwavelength structures and the haze factor results; the longer the etching time, the taller these structures are and the higher the values of haze that are obtained, especially in the long wavelength regime. A fully developed theory to explain this phenomenon is still not available. However, there are some existing theories that need to be investigated further [12].Therefore, this topic should be one of the priorities to look at in further investigations regarding this subject.

Table I details the efficiency of the Cu NM for achieving outstanding results of low sheet resistance, outperforming values reported for common TCOs.

Lastly, Figure 5c is an optical picture that illustrates the tradeoff between haze factor and total transmission with increased etching time that Figures 4a-b depict. Next, we must combine the nanostructured glass and the metal NM. Table I and Figure 6 describe the performance of fused silica samples created with the Cu NM on one side and etched with the hazy glass structure on the other.

Sample etch time

Sheet Resistance (Ω/sq)

10 min

3.30

0 min

30 min

60 min

90 min

180 min

3.28 5.07 3.78

11.78 5.50

Table 1. Combined Results. Sheet Resistances

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In this case, the NM hinders diffuse transmission, and the haze value decreases. Nonetheless, it is important to point out that for the 90 min and 180 min samples, the haze is still above 80% for the entire visible spectrum, matching the haze characteristics of TCOs. Finally, Figure 6b depicts the total transmission values for the same samples. Regardless of the transmittance results due to the hazy structure alone (samples without NM), adding the NM caused the transmittance of all the samples to drop to between 58 and 70%. This represents a ~30% drop in transmission due to the metal NM. This exceeds the approximate 20% loss expected based on the transmittance limitations of the fused silica and metal NM geometry. The similarity between the curves also supports the conclusion that the final transmission will be primarily determined by the transmittance behavior of the NM, not the hazy glass structure. Unfortunately, these transmittance results do not match the performance of common TCOs. As understanding of the interaction between these two structures increases, improvements in total transmission are expected.

4. Conclusion

Figure 6a-b. Haze Factor (a) and Total Transmission (b) results of hazy glass combined with Cu NM.

Figure 6a describes the haze factor results of samples etched between 0 min and 180 min. The graph shows that for samples with low haze, the Cu NM actually increases the haze factor. For the case of 0 min etching the haze increases from nearly 0% to between 10% and 20%. Similarly, for the 10 min etching the haze again increase from less than 1% to between 25% and 35%. On the contrary, for high haze samples, the Cu NM hinders the haze, reducing it by about 10% from its original value. This dual behavior can be explained by considering that the NM will hinder greatly the transmission of the type of light transmittance which predominates. Thus, for low haze samples, specular (direct) light predominates, and the addition Cu NM hinders its transmission, thus increasing the haze value. The opposite is true for samples with high haze, where scattered (diffuse) light predominates.

A needle-type subwavelength structure was effectively developed on fused silica substrates (etching rate: ~0.08µm/min) which strongly enhances the substrate’s optical properties. A constant haze factor value of 98% for the entire spectrum (250 nm-1200 nm) and a total transmission above 80% for the visible spectrum were achieved. Scalable and efficient processing techniques were used to create large area, highly ordered Cu NMs with superior conductivity (Rs ~ 3-12 Ω/sq) and transmittance (~80% at λ = 550 nm). After 180 min of etching, combining both the nanostructured “hazy” glass and the metal NM, a value of sheet resistance as low as 5.50 Ω/ sq and a haze factor of above 80% for the entire spectrum (250nm-1200nm) are observed. These properties match the reported values of TCOs. However, a maximum of 68% in total transmission was obtained, falling short of the required 80%. Future work will focus on other substrate materials that do not suffer from the tradeoff between haze factor and transmission to obtain optimal optical properties.

Acknowledgements The authors would like to thank the Mascaro Center for Sustainable Innovation (MCSI) for providing the funding to conduct this investigation.

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References [1] J. Krc et al., “Potential of thin-film silicon solar cells by using high haze TCO superstrates,” Thin Solid Films, vol. 518, no. 11, pp. 3054–3058, 2010. [2] W. Beyer, J. Hupkes, and H. Stiebig, “Transparent conducting oxide films for thin film silicon photovoltaics,” Thin Solid Films, vol. 516, no. 2–4, pp. 147–154, 2007. [3] J. Müller, B. Rech, J. Springer, and M. Vanecek, “TCO and light trapping in silicon thin film solar cells,” Sol. Energy, vol. 77, no. 6, pp. 917–930, 2004. [4] Y. Zhao, S. Miyajima, Y. Ide, A. Yamada, and M. Konagai, “Microcrystalline Silicon Films and Solar Cells Prepared by Photochemical Vapor Deposition on Textured SnO 2 with High Haze Factors,” Jpn. J. Appl. Phys., vol. 41, no. Part 1, No. 11A, pp. 6417–6420, 2002. [5] A. Hongsingthong, T. Krajangsang, A. Limmanee, K. Sriprapha, J. Sritharathikhun, and M. Konagai, “Development of textured ZnO-coated low-cost glass substrate with very high haze ratio for silicon-based thin film solar cells,” Thin Solid Films, vol. 537, pp. 291–295, 2013. [6] Y. Nasuno and M. Kondo, “Effects of substrate surface morphology on microcrystalline silicon solar cells,” J. Appl. physics. Pt. 2, vol. 40, no. 4, pp. 303–305, 2001.

[7] S. Q. Hussain et al., “Light trapping scheme of ICP-RIE glass texturing by SF6/Ar plasma for high haze ratio,” Vacuum, vol. 94, pp. 87–91, 2013. [8] C. Xu, L. Wang, and D. Li, “Maskless fabrication of broadband antire fl ection nanostructures on glass surfaces.” [9] P. W. Gao, T., Wang, B., Ding, B., Lee, J. K., & Leu, “Uniform and ordered copper nanomeshes by microsphere lithography for transparent electrodes,” Nano Lett., vol. 14, no. 4, pp. 2105–2010, 2014. [10] C. Theuring, M., Steenhoff, V., Geibendörfer, S., Vehse, M., von Maydell, K., & Agert, “Laser perforated ultrathin metal films for transparent electrode applications.,” Opt. Express, vol. 23, no. 7, 2015. [11] S. Haghanifar, T.Gao, R. T. R. D. Vecchis, B. Pafcheck, T. D. B. Jacobs, and P. W. Leu, “Ultrahightransparency, ultrahigh-haze nanograss glass with fluid-induced switchable haze,”Optica, vol. 4, no. 12, pp. 1522-1525, 2017. [12] K. Jäger, O. Isabella, L. Zhao, and M. Zeman, “Light scattering properties of surface-textured substrates,” Phys. Status Solidi Curr. Top. Solid State Phys., vol. 7, no. 3–4, pp. 945–948, 2010.

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Modeling Interferon Response in Pandemic H1N1 Influenza Virus Infected Mice Using Gene Expression Data Kyler R. Madara and Jason E. Shoemaker

Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Innate immunity is essential for viral clearance and tissue repair during infection. However, there are drawbacks to this response. Inflammation induced by the immune response can cause adverse and sometimes deadly effects. Interferon is a cytokine essential to activating the immune response. Developing an engineering model of the interferon response dynamics can help researchers understand the potential drawbacks of a powerful immune response. The purpose of this project is to model the inflammatory interferon response to pandemic H1N1 (pH1N1) influenza virus infection in mice using gene expression data. Not only does the model capture experimental dynamics, but the parameters and equations adhere to basic biological and mass-action principles. Introducing target and infected cell fractions into the model allows simulated interferon production to stop after the virus is destroyed, with a sum-of-squares error value of 5.0791. Changing the parameter associated with interferon production (ki,prod) suggests an aggressive relationship between parameter changes and model responses; interferon production spikes with small changes in viral titer. This suggests that regulating interferon production to decrease viral titer is ill-advised due to a more inflamed host response. Overall, this model reveals insights into emergent systems properties associated with interferon production. This model could be used to identify targets and treatment protocols for future drugs and vaccines. Key Words: Modeling, Innate Immunity, Interferon, Systems Biology

1. Introduction Each year, 5-20% of the United States population is inflected with some strain of influenza. When complications arise, 200,000 Americas on average will have to visit a hospital for treatment, where approximately 25% of those patients will succumb to this virus [1]. In 2009, a strain of the flu, colloquially coined as swine flu (pH1N1), swept the U.S. The Center for Disease Control

(CDC) confirmed about 60.8 million cases (20% of the U.S. population) during that flu season. This pandemic resulted in 274,304 hospitalizations, 4.5% of which resulted in patient death [2]. One of the major factors in patient death due to any strain of influenza virus is the human bodyâ&#x20AC;&#x2122;s natural response to viral infection. Innate immunity is essential for viral clearance during infection. However, there are drawbacks to this response. Inflammation induced by cytokines, such as interferon, can cause adverse and sometimes deadly effects [3]. To attempt to understand this powerful immune response, an engineering model of the interferon response dynamics can be developed. Many models incorporate innate immune response, but there is not a current model that focuses strictly on interferon production rate changes. One of the models that focuses the most on interferon in the Saenz model from 2010, which concluded that increased interferon production in horses infected with influenza resulted in increased nasal discharge and coughing [4]. A modern model would aid in quickly identifying potential drug targets around interferon. If a parameter can be changed to produce an ideal response (fast viral clearance) with no deadly interferon spikes, then this parameter could become a potential drug target. The purpose of this project is to model the inflammatory interferon response to pH1N1 influenza virus infection in mice using gene expression data.

2. Methods 2.1 Genetic Clustering and Ontology Gene expression data and experimental conditions were previously reported by Shoemaker et al [5]. To cluster the genes, weighted correlation network analysis (WGCNA) was performed in R [6]. The algorithm was developed by Langfelder and Horvath. It clusters genes together by generating a matrix of correlation coefficients that is n*n in size, where n is the count of genes available for analysis. In this case, n was 16,062. Genes that are highly corre-

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Table 1. Model Equations, Descriptions, and Initial Conditions

lated with each other will be placed in the same module, or cluster. In other words, it identifies co-regulated genes by clustering genes based on the scaled correlations of their gene expression. From these clusters, the algorithm develops a “module eigengene” for each cluster, a scaled representation of the gene expression of the entire cluster. This eigengene is the first principle component of the gene expression matrix. After clustering, DAVID Gene Ontology Tool was used to determine which clusters are associated with interferon production. The eigengene was used to describe gene expression dynamics.

Table 2. Model Parameters

the eigengene and Titer designates the viral titer. The subscript “sim” denotes the simulated eigengene or titer. Error=(E–Esim)2+(Titer–Titersim)2 (Eq. 1) The fitted model parameters and resulting dynamics are shown in Table 2 and Figure 1, respectively.

3. Results 3.1 Model Validity DAVID determined that Module (cluster) 4 was the most enriched in interferon production, inflammation, and

2.2 Model Development and Error Matlab was used to generate the model shown in Tables 1 and 2. The model consists of 4 ordinary differential equations (ODEs) and 9 parameters. Each equation adheres to biological principles and mass-action kinetics. The 4 species in the model are target cell fraction (T), infected cell fraction (c), viral load (v), and interferon (i). Target cells decrease as they are infected by the virus and also decrease as they naturally die. Infected cells will increase at the same rate that target cells become infected, but will then decrease as they lyse and release more virus. The virus concentration will increase as the infected cells release more viral copies, but will decrease as interferon concentration increases and as the virus naturally dies. Cells will recognize foreign viral RNA and respond by producing interferon at a rate described by Hill kinetics. The model was parameterized using FMINCON to minimize the simulation error found by comparing the simulation to the WGCNA-generated eigengene as well as the observed viral titers. This sum-of-squares error calculation is given by Equation 1, where E designates

Figure 1. The experimental (blue) and simulated profiles (black). The error for the simulation was 5.0791. (Time Bounds: 0 to 168 hours post infection).

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Figure 2. “Shut-off” dynamics of the model against time. (Time Bounds: 0 to 500 hours post infection).

Figure 3. Interferon Expression against Viral Titer for Iterations of Interferon Production (k_(i,prod)). The thick black line represents the original simulation, with interferon production at the value indicated in Table 2. The red line represents the original parameter reduced 50%, while the purple line represents the original parameter increased 50%. (Time Bounds: 0 to 1000 hours post infection).

immune response (p-value = 10^(-3.8)). The WGCNA module eigengene from Module 4 was used as the experimental data for model fitting.

feron production over time, increasing inflammation and the potential for death.

The model was fit to the experimental data, not trained. As can be seen from Figure 1, the model simulates the experimental results well. The error of this simulation was 5.0791. This value is a sum-of-squares absolute error, as indicated by Equation 1. Not only does the model follow the experimental results, but the inclusion of target cells and infected cells allows the model to “shut off”. This means that interferon response in the model approaches zero as time increases, thereby shutting off interferon production. Figures 2 and 3 display this behavior. As the virus dies off over a longer period of time, the model stops interferon production, which adheres to biological principles of homeostasis. This is an important aspect of the model because if it did not return to homeostasis, there would be a prolonged innate immune response. This would result in more inter

3.2 Sensitivity Figure 3 explores the possibilities of changing interferon production rate, ki,prod in Table 2. This parameter was increased and decreased by 50% of the original simulation’s value. The red path in Figure 3 represents 50% reduction in the parameter, and the blue path represents a 50% increase. Each path in between is a 10% iteration in parameter value, where the black path represents the original simulation. Figure 3 suggests an aggressive interferon response to viral titer as interferon production rate changes; viral titer decreases at a slower rate than interferon production increases. This result would not have been found without mathematical modeling.

4. Discussion An ideal path on the phase plane in Figure 3 for the virus to take would be one that minimizes both viral titer and

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interferon production. In this â&#x20AC;&#x153;best-caseâ&#x20AC;? scenario, innate immune response is powerful, yet efficient. Minimizing inflammation and viral spread is the ultimate goal in effective viral clearance. Given the aggressive relationship between inflammation and viral load, it may not be beneficial to regulate interferon production to limit virus replication. Figure 3 shows that changing the rate at which interferon is produced creates a heightened inflammatory response to viral titer. If the goal is to decrease inflammation, regulating interferon response would not work. As interferon production increases by 50%, maximum viral titer decreases by 3.75%, but maximum inflammation increases by 34.9%, as indicated by the blue path. Therefore, the decrease in viral titer is not worth the possibility of a more inflamed response, which could result in host death.

4. Conclusion This study demonstrates that mathematical modeling is an effective tool in deciphering the complicated innate immune response. Without mathematical modeling, it would be difficult, if not impossible, to directly assess parameters related to immune response. In this case, it was found that tampering interferon production rate, one of the parameters listed in Table 2, would reduce viral titer at the expense of a highly inflamed host response. This suggests that increasing interferon concentrations in a viral host can cause deadly inflammation.

Acknowledgements I would like to thank Dr. Jason E. Shoemaker for allowing me to work on this project during the Summer 2017 term. I would also like to thank the Swanson School of Engineering and the Office of the Provost for funding my project.

References [1] WebMD. What Are Your Odds of Getting the Flu? 2017. [2] SS. Shrestha et al. Estimating the burden of 2009 pandemic influenza A (H1N1) in the United States (April 2009-April 2010). Clin Infect Dis. 52 (2010) 75-82. [3] A. Billiau. Interferons and Inflammation. Journal of Interferon Research. 7 (2009) 559-567. [4] R. A. Saenz et al. Dynamics of Influenza Virus Infection and Pathology. J. Virol. 84 (2010) 3974-83. [5] J.E. Shoemaker et al. An Ultrasensitive Mechanism Regulates Influenza Virus-Induced Inflammation. PLoS Pathogens. 11 (2015) 1-25. [6] P. Langfelder and S. Horvath. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 9 (2008).

Future works for this model include sensitivity and bifurcation analysis to see what aspects of cellular dynamics effect viral infection the most. More sensitive parameters can be baselines for future drug targets and vaccines to prevent future outbreaks. If viral infections can be cleared more efficiently, the death toll from pandemics such as pH1N1 would drastically decrease.

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Binder Jet Additive Manufacturing of Dental Material from Cobalt-Chrome Alloy Pierangeli Rodriguez, Amir Mostafaei and Markus Chmielus

Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract Binder jet printing (BJP) holds distinctive promises among additive manufacturing technologies due to its fast, low-cost manufacturing, and stress-free structures with complex geometries. Interest in Co-Cr-Mo, as a biocompatible alloy with high specific strength, corrosion and heat resistance, rises to replace Ni-Cr dental materials that release toxic Ni ions. The objective of this research was to understand how different sintering temperatures affect the porosity of BJP Co-Cr-Mo parts to improve microstructure and mechanical properties. After curing, parts were sintered at temperatures ranging from 1240 °C to 1380 °C, with 20 °C increments, for 2 h. Samples were cross-sectioned, mounted, ground and polished for optical observations. Density and dimension changes were measured using Archimedes principle and ImageJ analysis of optical micrographs. Results showed that a nearly fully-densified part (~99%) was achieved at 1380 °C, as practically no pores remained, and grain boundaries were filled with precipitate. Pore interconnection decreased when sintered at or above 1340 °C, and pore size increased at 1360 °C due to pore coarsening. Vickers microhardness tests on cross-sections revealed an increasing trend as the sintering temperature increased, reaching a maximum of 295.2 HV0.1 at 1380 °C. Key Words: Binder jet printing; Sintering; Densification; Gas atomized powder.

1. Introduction Additive manufacturing (AM), also known as threedimensional (3D) printing, is the process of selectively adding material, usually binding one layer to the next, to create an object according to the model data. AM allows to directly manufacture parts with complex internal and external geometries out of a wide variety of materials (metal, polymer, ceramic, sand and glass) while reducing material and chemical waste and increasing control of part size and shape [1]. Among the AM techniques, Binder Jet Printing (BJP) stands out as a fast, low-cost manufacturing method [2]

that exploits all the advantages of AM applied to metals without the introduction of thermally induced residual stresses that are common in laser or electron beam AM methods. Co-Cr-Mo is a commonly used alloy in fields requiring high heat and corrosion resistance as well as in medical and dental applications, because of its advantageous properties (resistance to corrosion, high specific strength, heat resistance, and biocompatibility) [3]. Thus, this alloy is used in heavy machinery and aircraft jet engines, body replacement parts like prosthetic implants and dental frameworks. Increasing interest in this compound surges with regards to the last application: the urge to replace Ni-Cr alloybased dental frameworks, whose Ni-ions can be toxic when released in the oral cavity [4]. This need led to an interest in studying 3D printed Co-Cr-Mo and its potential applications in dentistry. The objective of this project is to learn how different sintering temperatures affect the porosity reduction of the printed Co-Cr-Mo part to improve its mechanical properties. Initially, literature was consulted to better choose sintering conditions [5]. Porosity distribution and density of the as printed and sintered samples were identified. Microstructural observations, compositional analyses and phase formation were studied with regards to different heat treatments to show that properly sintered BJP parts have similar properties to cast alloy. By doing so, 3D-printed Co Cr Mo parts are shown to be reliable for industrial and medical applications.

2. Materials and Methods For this project, parts were printed using gas-atomized Co-Cr-Mo (supplied by Carpenter Technology Corporation) that had the chemical composition given in Table 1. The powder’s morphology and elemental composition analyses were conducted with a JEOL (JSM 6510) scanning electron microscope (SEM), X-ray diffraction (XRD) was performed on a PANanalytical EMPY-

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REAN diffractometer and the particle size distribution by volume and number was analyzed with a Microtrac S3500 tri laser diffraction particle analyzer. Powder particle was determined from optical micrographs using ImageJ (image analysis software [6]). Phase transformation temperatures (solidus, solvus) were also measured by heating powder samples to 1450 °C at 10 °C/min in ultra-high purity Ar (100 mL/min). Element Co

Manufacturer analysis

Element

Bal.

6.07

0.17

0.02

0.03

27.72

0.12

0.71

Mo N

0.7

Manufacturer analysis <0.001

0.009

3. Results and Discussion 3.1 Powder Feedstock Characterization

0.001

Vickers microhardness tests were also performed on the cross sectioned samples with a Leco LM 800 microhardness tester (100 gf for 10s). Rate controlled tensile tests at 5 mm/min were performed using an MTS 880. Dimensions of the as printed sample were 60 mm long, 7 mm wide, 5 mm thick, and gage length of 26 mm.

0.02

<0.01

<0.001

<0.001 <0.01

Table 1. Chemical composition of Co-Cr-Mo alloy as received, in weight percent [wt.%] Figure 1. SEM micrograph of the Co-Cr powder.

The parts were manufactured using an ExOne X1-Lab binder jet printer (BJP). Printed parts were cured at 200 °C in a Carbolite oven (type PF30) and then sintered in a Lindberg tube furnace (three samples per sintering temperature, submerged in an alumina powder bed under vacuum) with the following heating profile: heating by 5 °C/min from room temperature to 1000 °C, 2.5 °C/min to the holding temperature (from 1240 to 1380 °C, with interval of 20 °C), holding for 2 h and then cooling by 1 °C/min to 1250 °C, by 5 °C/min to 500 °C and finally cooling down to room temperature. Density and dimension changes (shrinkage) of the printed and sintered samples were measured according to Archimedes principle with an OHAUS AX324 precision balance (0.1 mg resolution). For sintered samples, density was also determined from optical micrographs of samples’ cross sections using ImageJ (image analysis software). For microstructural examination of the sintered coupons, cross sections were cut from the specimens, cold-mounted, ground and polished (Struers Tegramin-25 automatic system). Optical micrographs were taken with a Keyence digital optical microscope (OM) (dark field Z20 lens and multi-diffused adapter).

Optical micrographs (Figure 1) of the powder (spread over a glass slide) were analyzed with ImageJ. Results showed that the roundness of the powder particles varied from 54 % to 99 %, with an average of 89 ± 1 %. Regarding the size of the particles, the diameter (circular approximation) of the powder particles was 72.5 ± 21.2 µm on average, perimeter values ranged from 20.86 µm to 116.0 µm, with an average of 66.4 ± 22.3 µm, and area measurements went from 29.7 µm2 to 848.5 µm2, with an average of 312.1 ± 191.9 µm2, confirming the expected area of the particles and the absence of unwanted larger pieces. 3.2 Densification and Microstructural Observations After polishing the samples, optical micrographs were taken for each sintering temperature (Figure 2) to observe the pore size and distribution. Micrographs show that sintering temperatures from 1240 °C to 1320 °C had a large amount of irregular interconnected pores, for the 1340 °C sample pores were fewer, as well as for the 1360 °C sample, in which case pores were also much bigger. Size of the pores for the lower temperatures were complex to calculate as they were mostly interconnected with one another. For 1300 °C, pores had an average size

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Density of both, the as-printed and sintered samples were measured. Relative density results (by Archimedes method and OM of cross-sections) for all sintered samples appear in Figure 3.

Figure 3. Relative densities of samples at each sintering temperature from 1240 °C to 1380 °C, according to Archimedes method and ImageJ image analysis.

Figure 2. Optical micrographs (300X) taken from cross-section of the Cr-Co-Mo samples sintered at: a.1240 °C, b.1260 °C, c. 1280 °C, d. 1300 °C, e. 1320 °C, f. 1340 °C, g. 1360 °C, h. 1380 °C.

of 148 µm2, while for 1360 °C, pores had an average size of 2040 µm2. This increase in the size of pores is explained by Dourandish et al.: “The pore coarsening and its effects on density during final-stage sintering is a well established phenomenon. Differences in the pore curvature lead to growth of the large pores at the expense of the smaller stable pores” [5]. The 1380 °C sample had a very small number of minor pores, being fully densified; at this sintering temperature, pores were also more spherical (which is beneficial as they don’t have such a great impact in mechanical properties as irregular pores [1]). Roundness of pores of the lower temperatures remained mostly uniform around 55%, and for 1380 °C, it increased to 63%.

With increasing sintering temperature, the density of the samples increased, and the volume decreased, as they are inversely proportional. The average green density (density of the original powder-compacted sample, as printed) was 44.1%. Then, by water displacement (Archimedes) method, the relative density increased from 52.9% to 64.4%, from 1240 °C to 1320 °C, with a change of about 3% every 20 °C increment in the sintering temperature. The highest density (96.7%) was reached at 1380 °C. ImageJ results, were mostly coherent with the water displacement data, density for the 1240 °C sample was of 56.5%, and then, it increased from 64.3% (sintered at 1260 °C) to 87.1% (sintered at 1340 °C), with an increase of approximately 3% every 20 °C increment in the sintering temperature (very similar to the water displacement method). Density values increased up to 90.5% at 1360 °C, and to 99.9% when sintered at 1380 °C. This behavior was expected because even though pore size increased in the 1360 °C sample, when sintered at 1380 °C, the alloy reached its optimal densification temperature as practically no pores remained and the grain boundaries were filled with precipitate, as liquid phase formed during the sintering process (crossing a phase boundary) [1], for which SEM imaging should be done to confirm the presence of other elements that probably reacted with the alloying elements to form this precipitate.

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Moreover, other samples were sintered at 1380 °C without immersing them in alumina powder, which resulted in a higher Archimedes density measurement of 97.2% and ImageJ analysis of 99.9% densification. This points out, that the difference of results between the image analysis and the water displacement method is not an inconsistency itself, but is occasioned by some imbedded alumina powder particles that affected the Archimedes method measurements, by being of much lower density than Cr-Co-Mo and by blocking the penetration of water in the sample [1]. 3.3 Mechanical Properties Figure 4 is a summary of Micro-hardness measurements showing a hardness increase from 92.7±35.3 HV0.1 (at 1240 °C) to 292.1±16.0 HV0.1 (at 1380 °C). There was a low peak at 1320 °C of 81.6±23.9 HV0.1. The hardness of the sample should increase with an increase in density, that is, with an increasing sintering temperature. However, the relation might not be linear, because “with increasing sintering temperature and time, grain growth or coarsening and segregation of alloying elements to the grain boundary has an opposite effect on the hardness. Thus, varying contributions to the hardness are dominant at different sintering conditions and are visible in the density measurements and OM observations” [5].

4. Conclusion Additively manufactured samples were successfully printed by the powder bed binder jet printing using Co-Cr-Mo alloy. The effect of the sintering temperature on density, microstructure, porosity and microhardness was investigated. It can be concluded that even though an increase in the pore size could be appreciated with increasing temperature, once the optimal sintering conditions (1380 °C for 2 h) are reached it is possible to achieve fully densified BJP Co Cr-Mo parts, and practically no pores remain, which proves that high compact dental frameworks can be produced. For mechanical properties, the hardness values increased with increasing sintering temperature, as expected, reaching a maximum value of 295.2 HV0.1 at 1380 °C, which will prevent dental frameworks from breaking or scratching. These results provide basic confirmation of the potential of Co-Cr-Mo alloy for dental applications, to be assessed with further tests, as a better substitute for current Ni-Cr dental frames.

Acknowledgements This work was funded by the Swanson School of Engineering and the Office of the Provost. I will also like to thank all the members of the Chmielus Lab, especially Amir Mostafaei. We want to thank Carpenter Technologies for supplying Cr-Co-Mo powder to us.

References [1] A. Mostafaei, E.L. Stevens, E.T. Hughes, S.D. Biery, C. Hilla, M. Chmielus, Powder bed binder jet printed alloy 625 : Densi fi cation , microstructure and mechanical properties, J. Mater. Des. 108 (2016) 126–135. [2] A. Mostafaei, J. Toman, E.L. Stevens, E.T. Hughes, Y.L. Krimer, M. Chmielus, Microstructural evolution and mechanical properties of differently heat-treated binder jet printed samples from gas- and water-atomized alloy 625 powders, Acta Mater. 124 (2017) 280–289. doi:10.1016/j.actamat.2016.11.021. Figure 4. Microhardness results for each sintering temperature ranging from 1240 °C to 1380 °C.

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[3] C. Qian, X. Wu, F. Zhang, W. Yu, Electrochemical impedance investigation of Ni-free Co-Cr-Mo and Co-Cr-Mo-Ni dental casting alloy for partial removable dental prosthesis frameworks, J. Prosthet. Dent. 116 (2016) 112–118. doi:10.1016/j.prosdent.2015.12.001. [4] W. Yu, C. Qian, W. Weng, S. Zhang, Effects of lipopolysaccharides on the corrosion behavior of Ni-Cr and Co-Cr alloys, J. Prosthet. Dent. 116 (2016) 286–291. doi:10.1016/j.prosdent.2016.01.002.

[5] M. Dourandish, D. Godlinski, A. Simchi, V. Firouzdor, Sintering of biocompatible P / M Co – Cr – Mo alloy ( F-75 ) for fabrication of porosity-graded composite structures, 472 (2008) 338–346. doi:10.1016/j.msea.2007.03.043. [6] C.T. Rueden, J. Schindelin, M.C. Hiner, B.E. Dezonia, A.E. Walter, E.T. Arena, K.W. Eliceiri, ImageJ2 : ImageJ for the next generation of scientific image data, (2017) 1–26. doi:10.1186/s12859-017-1934-z.

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Density Variation in Additively Manufactured Ti-6Al-4V Samantha Schloder, Erica Stevens, David Schmidt, Markus Chmielus

Advanced Manufacturing and Magnetic Materials Laboratory Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract The process of additive manufacturing has recently been witness to a huge increase of interest and exploration. Additive manufacturing affords new ways to process materials such as metals, plastics, and glass that were previously thought to be unattainable or time costly. Powder bed binder jet printing is one example of additive manufacturing that can create complex shapes as seen in this research study. One trend that was found in these complex shapes, was the presence of less dense areas along the outside edges, with density values in the 40%-70% range. The second trend was how the regions along the curve seemed to be the densest with density value rarely ever falling below 70%. Both trends point towards being a result of the printing process, which influences all properties of the post manufacturing product. To finally determine the reason for the localized density changes, an additional study on the local green density and binder distribution will be needed. Through further research and discussion, the reasons for local density variation and, thus, mechanical properties will need to be investigated in detail. Key Words: Binder jetting, Density, Ti-6Al-4V.

1. Introduction Additive manufacturing (AM) is another term for the 3D printing of materials such as metals. One example of metal AM processing is powder bed binder jet printing, where products are manufactured from three-dimensional computerized models. During this process of powder bed binder jet printing, a thin layer of metal powder is repeatedly added to the powder bed, where with each stacked layer a binder is

added according to the three-dimensionally modeled design [1, 2]. The powder bed is then cured so that any powder particles that underwent binder penetration will stick together to generate a green part, while the extra powder that was untouched by the binder is removed and reused [3]. This process makes a part that is loosely held together by binding materials and requires further processing such as sintering to increase density and strength. How a green part is bound together before postprocessing will greatly affect the microstructure of the finished product and, thus, the mechanical properties as well [4]. Binder jet printing has been used already for a variety of materials including alloy 625 and functional magnetic materials (Ni-Mn-Ga) [5-7]. Historically, titanium products have been machined from forged titanium blanks due to properties, such as reactivity [8]. Binder jet printing is an important AM method for titanium alloys, because unlike laser-based methods, reactivity is less of a concern as there is limited heating. Additionally, by utilizing binder jet printing, waste can be reduced; the titanium that is initially bounded together by binder is all the material used, thus, less scrap is created as waste when compared to machining from forged titanium blanks [3]. In this study, Ti-6Al-4V, which is the most commonly used titanium alloy, was used to test the capabilities of powder bed binder jet printing. Ti-6Al-4V is often used to test additive manufacturing methods, due to its wide use and predictability. In this study, we received a Ti-6Al-4V sample that was 3D-printed into a complex barrel shape by use of powder bed binder jet printing and then sintered in a post-processing step [9]. In this study, the uniformity of the densification is examined over the entire cross section.

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2. Materials and Methods The original sintered sample shown in Figure 1 was cut with a bandsaw, to produce two slices of material. The two slices were then further cut into eight pieces with a metallographic saw. Each piece was then individually hot mounted and polished. Samples were imaged on a Keyence VHX 600 at 100x magnification. Images were reassembled to resemble the

Figure 1. Complex shape manufactured by Carpenter Technologies out of Ti-6Al-4V. (a) shows a side view of the original sample. (b) shows a top view, indicating where the two slices were cut out. (c) shows how each of the two slices were further cut into eight pieces.

original two slices as much as possible, even though a small amount of material was lost due to cutting on the metallographic saw. The reassembled slices were individually split up into a 7x2 grid of sub-images for separate ImageJ analysis [10]. Each of these fourteen pictures was then individually thresholded and a batch macro was written that systematically split up each of these pictures into a 20x20 grid of images. The resulting grid of four hundred images from the first slice, each had dimensions of 617x505 μm2, while the second slice had four hundred images, each with dimensions of 856x701 μm2. ImageJ was then used to calculate the amount of material present per 400 resulting pictures. This process can be visualized in Figure 2.

Figure 2. Process used to split up the reassembled slices for separate ImageJ analysis. (a) shows a 7x2 grid where each section was individually run through a batch macro that further split up each image as shown in (b) that resulted in 400 images such as the one seen in (c).

The batch macro was then used again to spilt up the 7x2 group of sub-images that appeared to be completely dense and measured the area of the sample that was manually solidified for each of the 400 resulting pictures. The dimensions for each of these 400 resulting pictures were the same on both slices, 617x507 μm2 for slice one and 856x701 μm2 for slice two. Since the dimensions of the resulting pictures for the manually solidified sample were respectively the same to the original thresholded sample, the amount of material present in the manually solidified sample could be directly compared to the mate-

After the batch macro was used to process the original thresholded 7x2 group of sub-images, the same group of sub-images underwent a process that would make them appear to be completely dense. This was done, so that an accurate measurement of density could be calculated for any image that contained part of an edge. The procedure used the commands fill holes and dilate, done i times until the part was solid, then using the command erode i times until the edges of the piece were back to their original state. When eroding was done, the edges of the sample that fall along the image’s boundaries were reduced i times and manually corrected for.

Figure 3. (a) and (b) show an example of how each picture changed when manually solidified. While (a) shows the edge of a sample,(b) shows an area from the center of the sample.

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rial present in the original thresholded sample. This was especially important along the surface of the slices where the edge of the sample can be seen. Figure 3 shows two examples of how the original thresholded image changed when manually solidified for both along the edge and in the center of the sample. Finally, the variation in density along the original part was found using the equation density = (area of solid sample in the original thresholded image)/ (area of solid sample in the manually solidified image). By mapping out the resulting density values, a representation of density distribution along the part can be visualized.

3. Results Density measurements on slice 1 ranged from a low of 19.5% to a high of 100.0%. The low value of 19.5% was a localized minimum, and pertained to such a small area that it was excluded from the mapping in Figure 4, so that the variations on the slice could be more defined. Across the slice, the mean density was 81.9 Âą 11.1% and the median density was 84.2%. The most visible difference along the slice would be the changes in color seen when comparing the flat edges to the curved section. Along the flat edges values are seen to be in the 40%-70% range, while along the curve, values rarely ever fall below 70%. There was a consistent trend of lower densities along the outside edge of slice 1.

Figure 4. Mapping of the density values of a crosssectional area of the sintered Ti-6Al-4V slice 1, with printing axis shown in respect to the piece. Values near 20% were considered outliers and excluded from the map to improve visibilities of other variations. Some material was lost during cutting, resulting in some discontinuities.

Density measurements for slice 2 ranged from a low of 27.6% to a high of 99.6%. The mean density was 87.8 Âą 10.3% and the median density was 91.9%. Density variations in slice 2 can be seen in Figure 5. Like slice 1, slice 2 also shows some discontinuities where material was lost during the cutting process. Likewise, along the edges

of this sample there are also less dense areas seen, with the center curve containing relatively high-density values like sample 1.

Figure 5. Mapping of the density values of a crosssectional area of slice 2 of the Ti-6Al-4V piece with printing axis shown in respect to the sample. Some material was lost during cutting, resulting in some discontinuities.

4. Discussion There are three potential reasons for the variation in density in the sintered part: (1) a variation in sintering temperature and, therefore, densification kinetics during sintering, (2) variations in the powder spreading, and (3) variation in the binder density. The low-density areas at the right and left end of the samples are likely due to a lower temperature in the sintering furnace in these regions, resulting in a slowed densification. Excluding these two areas of localized minimums, there are two clear trends: one trend is the less dense area found on the outside edges, where density values ranged from 40%-70%, while the second trend is how the curve seems to be the densest with density values rarely ever falling below 70%. Both trends point towards being a result of the printing process which influences all properties of the green and sintered part as reported by Chen and Zhao [11]. Although research on binder penetration in powder bed binder jet printing is scarce, the process can be related to the process of wet granulation found in the pharmaceutical industry. Wet granulation is done by adding a liquid solution onto a powdery material so that the particles will stick together similarly to binder jet printing. Studies have found that two major mechanisms exist between drop and powder, which are spreading and infiltration [12]. Spreading is how much area the liquid binder encounters when added to the powder particles, while infiltration refers to how much binder enters the powder particles. When spreading occurs, there would be more area that is coated in binder that is not necessar-

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ily desired. If spreading were to occur, there would be less binder in that layer available to infiltrate the powder particles, resulting in this layer not being as held together as possible. These mechanisms could be causing the lower densities seen along the edges of the sample. To finally determine the reason for the local density changes, an additional study on the local green density and binder distribution will be needed.

5. Conclusion Metal additive manufacturing is a technological advancement that has initiated a change in the powder metal industry. The future of additive manufacturing looks promising as the interest in the field grows. By improving upon the printing processes, such as binder jet printing, a greater diversity of products can be made from a variety materials. The density distribution across a binder jet printed and then sintered Ti-6Al-4V curved sample in this research study was not seen to be uniform, but varied between different cross-sectional areas. To fully understand why there are density variations across the sample, another study would have to investigate the density variation within the green part and binder penetration during the printing process.

Acknowledgements The authors would like to thank Carpenter Technologies for materials, the PPG Foundation for funding and SSOE for supporting undergraduate research.

References [1] W. E. Frazier, J. Mater. Eng. Perform., 23, June, pp. 1917–1928, 2014. [2] L.E. Murr and W. Johnson, Journal of Materials Research and Technology, 206, 1, pp. 77-89, March 2017. [3] Shrestha, S. & Manogharan, G. JOM (2017) 69:491. [4] K. V Wong and A. Hernandez, ISRN Mech. Eng., 2012, pp. 1–10, 2012. [5] A. Mostafaei et al., Acta Materialia, 131 (2017) 482-490. [6] A. Mostafaei et al., Acta Materialia, 124 (2017) 280-289. [7] A. Mostafaei et al., Materials & Design, 108 (2016) 126-135. [8] Qian, M., et al., MRS Bulletin 41 (2016) 775-784. [9] M. Donachie, 2nd edition, ASM International, 2000. [10] C. A. Schneider et al., Nat. Methods, vol. 9, pp. 671–675, 2012. [11] H. Chen et al., Rapid Prototyp. J., 22, 3, pp. 527–538, Apr. 2016. [12] H. R. Charles-Williams et al., Powder Technol., 206, 1–2, pp. 63–71, 2011.

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Assessing Cytocompatibility of Novel Ultra-High Ductility Magnesium Alloys Fathima Shabnam1, Jingyao Wu2, Abhijit Roy2, and Prashant N. Kumta2

Department of Chemical and Petroleum Engineering, Swanson School of Engineering, Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh2, Pittsburgh, PA, USA

Abstract Magnesium-based alloys have been on the forefront of biodegradable metals research. This is primarily due to the non-toxic nature of the corrosion products. Currently, there are no metal-based biodegradable stents used for the trachea. Common tracheal stents are made of silicone or stainless steel, or nitinol, all of which have their respective disadvantages. This paper aims to assess the cytocompatibility of novel proprietary magnesium alloys on human bronchial epithelial cells (BEAS-2B). Lithium, Aluminum, and Zinc were present at different compositions in the novel magnesium alloys indicated as alloy 1, 2 and 3. MTT and DAPI & F-Actin studies were conducted and the results suggest that alloys 1 and 2 are promising due to the cell viability and the morphology being similar to the commercial AZ31 alloy. This study, combined with the ongoing in vivo experimentation, will be conclusive in determining the suitability of these novel alloys for tracheal stent application. Key Words: magnesium, alloys, stent, biodegradable, human bronchial epithelial cells

1. Introduction Magnesium-based alloys have attracted biomedical researchers for decades due to their ability to degrade in vivo, favorable biocompatibility and suitable mechanical properties. Extensive potential applications as orthopedic implant, sutures and vascular stents have been explored. These new devices have the potential to reduce the number of second surgeries as well as long-term adverse effects due to perennial existence of the device. Tracheal stents are used as a treatment for many conditions: airway compression due to tumors or lymph nodes, benign stenosis, vascular compression, and stabilization of collapsing airways in tracheobronchomalacia. Currently, common tracheal stents include silicone, nitinol, and stainless steel stents. The demerits of these materials range from migration of the non-degradable stents as well as granulation after placement in the desired region requiring further surgery. These disadvantages accentu-

ate the need for use of biodegradable stents [1]. The ideality of biodegradable stents is that the stent can be placed in the region of interest and would degrade over time as the tissues heal, omitting the need for further surgery. Although during degradation, magnesium alloys do not produce any toxic corrosion products, its rapid, uncontrolled degradation rates pose a concern; this is because the rapid rate of product formation negatively affects the surrounding tissues by increasing the pH and resultant alkalinity. This rapid degradation also causes the stent to prematurely lose its mechanical properties likely fracture [2,3]. Currently, there is no study conducted to evaluate the cytotoxicity of magnesium-based alloys on airway epithelium cells. The aim of this study is to assess in vitro cytotoxicity of our proprietary ultra-high ductility (UHD) magnesium alloys for tracheal stent application. In this study, MTT test and DAPI & F-actin staining were performed to determine the effects of the degradation products of these magnesium alloys with different ratios of lithium, aluminum, and zinc on the human bronchial epithelial cell line (BEAS-2B). These results demonstrated comparable cytotoxicity of our proprietary magnesium alloys to commercial pure magnesium and AZ31 alloys.

2. Methods 2.1 BEAS-2B Cell Culture The bronchial epithelial cells (BEAS-2B cell line) were cultured in Bronchial Epithelial Cell Growth Medium (BEGM). The T75 flasks were pre-coated with a mixture of fibronectin, bovine collagen type I, bovine serum albumin (BSA) in culture medium. Cells were cultured in BEGM medium in the cell culture incubator. 2.2 MTT Assay The MTT assay was used to assess the cell metabolic activity and the cytotoxicity of the extracts. Surface areas of the three alloy samples from each group were calculated. The samples were manufactured by Xiâ&#x20AC;&#x2122;an Sifang Advanced Material Inc. The samples were polished using

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Figure 1. In vitro cytotoxicity of Mg-Li-Zn-Al alloy. (a) Mg and Li ion concentration in BEGM and extract; (b) BEAS-2B cell viability after culturing in different concentration extract for 1 day; (c) BEAS-2B cell viability after culturing in different concentration extract for 3 days; *denotes a significant difference between alloy groups (p <0.05, n=3).

SiC abrasive paper of 1200 grit, cleaned ultrasonically in acetone solution, and placed in a closed hood with ultraviolet light for 30 min on each side. The samples were then immersed in BEGM with a surface area-toextraction ratio of 1.25 cm2/mL for 72 h. The extract of the samples was then added at 10%, 25%, 50%, and 75% concentrations to the 8000cells/well seeded in a 96-well plate for 1 and 3 days, respectively. The positive control was pure medium with no extract, and the negative control was 10% dimethyl sulfoxide (DMSO) culture medium. The cytotoxicity of the extract was then tested using the Vybrant MTT Cell Proliferation Assay Kit. Phenol red free media and MTT were added, and the plate was left to incubate for 4 h. After adding sodium dodecyl sulfate (SDS) solution, the plate was left to incubate for 16 h before reading at 570nm absorbance using a plate reader. To evaluate the ion concentration, 100Âľl of each extract was added to 9.9mL of distilled water. Two standards (high and low) were used to calibrate. Inductive Coupled Plasma Optical Emission Spectroscopy (ICP-OES) quantified the presence of even trace amounts of metal ion concentrations. 2.3 DAPI & F-Actin Staining DAPI & F-Actin staining stains the nucleus and the cytoskeleton of the cell, enabling the cell morphological changes to be visible. The extract from the cell culture plates were fixed with 4% paraformaldehyde (PFA) for 10 min. Triton X was then added before washing with phosphate-buffered saline (PBS). Phalloidin and DAPI were added for staining. Fluorescent images were then taken to image any changes in the cell morphology and configurations.

3. Statistical Analysis For the MTT tests, the average and standard deviation of the three samples for each group was considered and determined. Two-way ANOVA was performed for MTT test with Bonferroni procedure as post hoc test. Statistical significance was defined as p < 0.05. Statistical analysis was performed utilizing the IBM SPSS Statistics 23 package for Windows.

4. Results The results are compared to Mg and AZ31 since those are accepted standards of control used in the current literature in this research area. The effects of pure Mg have been widely researched so it is beneficial to use it as a control for comparison. AZ31, on the other hand, is a commercial alloy that is accepted as the current ideal alloy with excellent corrosion resistance and acceptable mechanical properties among research groups. Figure 1(a) shows the increase in magnesium and lithium ions concentrations after immersing the alloys in culture medium for 72 h. As seen in Figure 1, there is a steep rise in both ion concentrations, with AZ31 alloy having the highest magnesium ion concentration. Since pure Mg and AZ31 alloys are devoid of any lithium, the concentrations for Li are correspondingly zero. Figure 1(b) and 1(c) show the results of the MTT study indicating the cell viability of the BEAS-2B cells at day 1 and day 3. At day 1, the cell viability of the novel alloys were significantly impacted compared to AZ31 and the pure Mg. On day 3, however, the viability appears to have noticeably increased and is similar to that of AZ31 and pure Mg.

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Figure 2. Images of the BEAS-2B cells day 1 and day 3 of DAPI & F-Actin staining. (scale bar = 50Âľm)

Figure 2 compares the morphology of the BEAS-2B cells between day 1 and day 3. With DAPI & F-Actin staining, the red color is the cytoskeleton of the cells, and the blue color indicates the nucleus. On day 1 and day 3, the cells in the different groups appear similar retaining the elongated cell shape of the BEAS-2B cells, indicating that the alloys did not have any adverse effect on the cell morphology. In the control group on day 3, the cells appear to have aggregated which is not seen in any of the other samples.

presence of magnesium ions prevents aggregation of the cells (Figure 1(a)) [5]. This effect has been experimented on platelet cells to test the effectiveness of magnesium treatment for hypertension. Although we hypothesize that this will not affect the treatment and compatibility of the stent in the body, we will be observing any effect of this during the on-going in vivo studies. The nucleus becoming more visible on day 3 for alloy 1 may indicate that the cells were locked in the synthesis phase (which can be seen in the cell cycle study not shown here).

5. Discussion

In summary, based on the MTT study and the DAPI & F-Actin studies, the results suggest that alloys 1 and 2 are promising, as they have similar effects to AZ31. Although alloy 3 results improved on day 3, compared to the other alloys, it is the least positive. However, these results are incomplete as the in vitro experiments conducted herein indicate the effects of the magnesium alloys on the BEAS-2B cells that could be visualized under the microscope. This, combined with the in vivo experimentation that is currently proceeding, will further elucidate the biocompatibility of each alloy. The results of this study will help determine the suitability of the alloys for tracheal stent application.

The trends seen in the ICP-OES results are crucial in understanding the results of other experiments. This is because the effect of the alloys on the cells is primarily due to its composition, and determining the ion concentration explains the exact affect. Since the ICP-OES data showed lithium to be the main difference in ion concentrations between the extracts, it can be concluded that the lithium ion concentration is the reason why the cell viability was impacted on day 1 of the MTT study. This is further supported by literature that lithium is shown to be toxic at high concentrations [4]. However since the levels had risen close to pure magnesium control on day 3, the impact is not as concerning and likely to have no significant affect and the lithium ion concentration is below the toxic limit. From the qualitative results of the DAPI & F-Actin staining, it can be seen that the alloys did not exhibit any negative impact on the morphology, and the cells correspondingly, retained their elongated shape. One reasoning behind the aggregation seen only in the control group compared to the other groups could be that the

Acknowledgements This research was funded by Dr. Kumta via the Edward R. Weidlein Chair Professorship funds and the Center for Complex Engineered Multifunctional Materials (CCEMM), the Swanson School of Engineering, jointly with the Office of the Provost. I would like to especially thank Dr. Kumta, Dr. Roy, Mr. Wu, and my family for all the support and encouragement.

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References [1] “Airway Stents.” Uptodate, www.uptodate.com/contents/ airway-stents [2] Myrissa, Anastasia, et al. “In vitro and in vivo comparison of binary Mg alloys and pure Mg.” Materials Science and Engineering: C, vol. 61, 2016, pp. 865–874 [3] Chou, Da-Tren, et al. “In vitro and in vivo corrosion, cytocompatibility and mechanical properties of biodegradable Mg–Y–Ca–Zr alloys as implant materials.” Acta Biomaterialia, vol. 9, no. 10, 2013, pp. 8518–8533., doi:10.1016/j.actbio.2013.06.025.

[4] Gu, Xue-Nan, and Yu-Feng Zheng. “A review on magnesium alloys as biodegradable materials.” Frontiers of Materials Science in China, vol. 4, no. 2, Apr. 2010, pp. 111–115., doi:10.1007/s11706-010-0024-1. [5] Kisters, Klaus, et al. “Effect of oral magnesium supplementation on blood pressure, platelet aggregation and calcium handling in deoxycorticosterone acetateInduced hypertension in rats.” Journal of Hypertension, vol. 19, no. 1, 2001, pp. 161–162., doi:10.1097/00004872200101000-00023.

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Stimulation of Elastic Fiber Proteins by Mesenchymal Stem Cell-Derived Factors Rachel E. Sidesa, Kaori Sugiyamag, Aneesh K. Ramaswamya, David A. Vorpa,b,c,d,e,f, Hiromi Yanagisawag, and Justin S. Weinbauma,d

Department of Bioengineering, Swanson School of Engineering, bDepartment of Cardiothoracic Surgery, School of Medicine, cDepartment of Surgery, School of Medicine, d McGowan Institute for Regenerative Medicine, e Center for Vascular Remodeling and Regeneration, and fDepartment of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA, and g Life Science Center of Tsukuba Advanced Research Alliance, University of Tsukuba, Japan a

Abstract Elastic fibers in the aortic wall enable recoil in response to the pulsatile pressure of the native circulation. Unsurprisingly, mutation or degradation of elastic fiber components can lead to arterial wall dissection and aneurysm. Our group has shown in previous work that mesenchymal stem cell delivery preserved elastic fibers in the context of murine abdominal aortic aneurysm. In this study, three-dimensional fibrin-based tissue constructs were used to determine if elastic fiber production is elevated in response to mesenchymal stem cell-derived secreted factors (MSCSF). After 20 days of culture, smooth muscle cell-based tissue constructs exhibited evidence of elastin, fibrillin-1, and fibulin-5 by indirect immunofluorescence; a qualitative increase in elastin was observed in the MSCSF-stimulated group. Quantitative PCR analysis demonstrated that MSCSF quantifiably increased expression of both fibulin5 and the elastin crosslinking protein lysyl oxidase. Lastly, insoluble elastin quantity as determined by ninhydrin assay was significantly greater (p = 0.01) in constructs treated with MSCSF. Taken together, this preliminary data shows MSCSF is capable of upregulating multiple proteins involved in mature elastic fiber formation. Key Words: elastogenesis, mesenchymal stem cell secreted factors, 3D fibrin constructs Abbreviations: extracellular matrix (ECM), mesenchymal stem cells (MSCs), smooth muscle cells (SMCs), MSC-derived secreted factors (MSCSF), aminocaproic acid (ACA), non-treated (NT), quantitative polymerase chain reaction (qPCR)

1. Introduction Cardiovascular disease has been the leading cause of death for nearly a century [1]. One of the many conditions

that fall under this category are aortic aneurysms. Aortic aneurysms occur when the major artery leading oxygenated blood away from the heart to the body â&#x20AC;&#x201C; the aorta â&#x20AC;&#x201C; expands under the pressure of the native circulation. This expansion is a result of numerous factors, including age, genetic predisposition, and disease. Aortic aneurysms, of all origins, are the result of degradation or malformations in the extracellular matrix (ECM) and, more specifically, in proteins involved in elastic fiber assembly [2, 3]. Elastogenesis is a complicated and incompletely characterized process that involves numerous structural and organizational proteins. Elastic fibers are generated during development and induce recoil properties in tissues such as the aorta [4]. Initial formation involves the organization of fibrillin-1 into the elastic fiber backbone known as the microfibril and the intracellular production of the soluble elastin precursor, tropoelastin [5]. Tropoelastin is secreted extracellularly where the matricellular proteins fibulin-4 and fibulin-5 are able to organize and facilitate deposition of tropoelastin bundles onto the microfibrils [6]. Specifically, fibulin-4 facilitates the interaction between tropoelastin bundles and lysyl oxidase (LOX), an enzyme responsible for crosslinking tropoelastin bundles into mature, insoluble elastin [7] while fibulin-5 facilitates the formation of structural crosslinks between elastic fibers and the cell surfaces [8]. Disruption of any of these unique organizational or structural proteins leads to ineffective elastic fiber formation and consequent aortic aneurysms as exhibited in genetic conditions like Williams syndrome (deletion of the elastin gene), and Marfan syndrome (mutation of the elastic fiber protein fibrillin-1) [9]. One potential therapy for aortic aneurysm involves adipose-derived mesenchymal stem cell (MSC) delivery. The selection of MSCs as a potential therapy is due to their ability to release various growth factors and differentiate

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into smooth muscle cells (SMCs) with both behaviors being linked to elastogenesis [10]. Additionally, previous work conducted by our lab has shown that periadventitial delivery of adipose-derived MSCs preserves elastic fibers in a murine elastase-induced abdominal aortic aneurysm model [11]. This finding opens a new possible avenue for non-surgical treatment of elastic fiber disorders. This study sought evidence for a paracrine mechanism in MSC-mediated preservation of aortic elastin. In short, we investigated whether the secreted factors from MSCs could induce that elastin-preservative effect on their own. We utilized a three-dimensional in vitro fibrin gel culture to investigate the effect of MSC secreted factors (MSCSF) on SMC elastic fiber formation. We hypothesized that application of MSCSF would upregulate elastic fiber components (tropoelastin, fibrillin-1, and fibulin-5) in healthy aortic SMCs, yielding a higher quantity of mature elastic fibers.

2. Methods

every 48-72 hours. All constructs were cultured in the presence of a lysinemimicking fibrinolysis inhibitor, aminocaproic acid (ACA) at 15 mM. “MSCSF” treatment refers to a 1:1 ratio of SMC growth-supplemented medium and collected MSC secreted factors, plus 15mM ACA. “Non-Treated (NT)” refers to SMC growth-supplemented medium, plus 15mM ACA. 2.3 Real-Time Quantitative Polymerase Chain Reaction (qPCR) Constructs (NT n = 2, MSCSF n = 2) were frozen in liquid nitrogen and then crushed. RNA was extracted using an RNAeasy Mini Kit (Qiagen). cDNA was synthesized by priming for 5 minutes at 25 °C, reverse transcription for 20 minutes at 46 °C, and reverse transcriptase inactivation for 1 minute at 95 °C. Tropoelastin, fibulin-4, fibulin-5, and lysyl oxidase (LOX) were amplified using purchased primers (Sigma, Table 1). GAPDH was the reference gene to which all samples were normalized.

2.1 Cell Culture Human aortic SMCs were purchased from ATCC (adult human aortic SMC, #PCS-100-012), and cultured in Cell Applications’ Human Smooth Muscle Cell Growth Medium (#311K-500). MSCs were collected from the adipose tissue of nondiabetic, non-smoking female patients under the age of 45 (provided by Dr. J. Peter Rubin, Department of Plastic Surgery, UPMC). MSC were cultured using a 50/50 mixture of DMEM (ThermoFisher Scientific, #12100046) and DMEM + F12 media (ThermoFisher Scientific, #12500096), supplemented with 10% fetal bovine serium, 1% fungizone, 1% penicillin/streptomycin, and 0.2mM dexamethasone. To collect conditioned media, MSC were grown to 60% confluence, given fresh media, which was collected after 24-72 hours of conditioning. All MSC were used between passages 0 and 2. Conditioned media was stored at -80 °C, and thawed immediately before treatments. 2.2 Fibrin-Based Tissue Constructs Three-dimensional fibrin-based tissue constructs were prepared as previously described from a solution of fibrinogen (33.3 mg/ml, Sigma), thrombin (25 U/ml, Sigma), and the cells of interest (1x105 cells/construct) [12]. Gelation of constructs occurred within sterile 5/16” (7.94mm) heat-stamped circles on tissue culture plastic. Constructs were cultured for 20 days (37 °C, 5% CO2) under each treatment condition, with media changes

Table 1. Forward and reverse primers for targeted genes in qPCR. All primers are reported 5' to 3'.

2.4 Indirect Immunofluorescence Constructs (NT n = 8, MSCSF n = 8) were fixed using ice-cold methanol and blocked in a 0.1% Tween-20 (Sigma) and 0.1% cold water fish gelatin solution (Aurion, #900-033). Target antigens were elastin (polyclonal rabbit anti-human, Elastin Products Company, #PR533, 1:1000), fibrillin-1 (rabbit polyclonal antiserum against human fibrillin-1, #PaB9543, 1:100), and fibulin-5 (monoclonal mouse anti-human, R&D Systems #MAB3095, 1:1000). Alexa Fluor secondary antibodies were targeted to elastin (mouse anti-rabbit 546) and the target matricellular protein (rabbit anti-mouse 488) (ThermoFisher). Nuclei were stained with Hoechst dye (Sigma). Mounted constructs were imaged as z-stacks using a Zeiss LSM 700 confocal microscope with a 20x oil-immersion lens. Final images are brightest pixel projections of z-stacks as produced by ImageJ (NIH) software. 2.5 Insoluble Elastin Ninhydrin Assay Insoluble elastin content as a fraction of total protein in each construct was determined (NT n = 8, MSCSF n

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Figure 1. qPCR Analysis of Response to MSCSF by Elastin and Other Matricellular Proteins Tropoelastin, fibulin-4, fibulin-5, and LOX expression in human SMCs cultured for 20 days with (yellow stripe, n = 2) or without (grey dot, n = 2) MSCSF as measured by qPCR normalized to GAPDH. Error bars indicate intra-sample variability.

Figure 2. Indirect Immunofluorescent Characterization of Elastic Fibers Elastic fiber formation in human SMCs cultured for 20 days without (A, B, n = 8) or with (C, D, n = 8) MSCSF. Nuclei are blue, elastin is red, and fibrillin-1 (A, C) or fibulin-5 (B, D) is green.

= 8) using a modified ninhydrin assay [13]. Constructs were heated to 98 °C in 0.5mL 0.1M NaOH for 1 hour to solubilize all proteins except insoluble elastin. Non-insoluble elastin proteins were separated from insoluble elastin via centrifugation, dried in a speedvacuum, and both protein fragments were heated to 110 °C in 0.5mL 6M HCl for 20-24 hours. Fragments were dried using a speed-vacuum, resuspended, incubated with a ninhydrin-based reagent, and quantified for protein content using a plate reader at 570nm.

3. Results To provide evidence of the mechanism by which MSCSF potentially affected elastogenesis, constructs were

analyzed for the presence of three different stages in the elastic fiber maturation process—transcription, elastic fiber assembly, and mature insoluble elastin. 3.1 Transcription of Elastic Fiber Genes The earliest stage of elastic fiber development, transcription, was evaluated through real time qPCR analysis, which quantified the gene expression of tropoelastin, fibulin-4, fibulin-5, and LOX in treated and non-treated constructs. Although significance was untestable due to low sample size, every target trended towards increased expression with MSCSF treatment (Figure 1). Fibulin-4 experienced more than a three-fold increase in expression while fibulin-5 and LOX both experienced beyond a five-

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4. Discussion

Figure 3. Insoluble Elastin Quantity as a Fraction of Total Protein Content Fraction of mature, insoluble elastin with respect to total protein content of fibrin gel constructs cultured for 20 days with (n = 8) or without (n = 8) MSCSF. * Statistically significant difference (p = 0.01)

fold increase in expression with application of MSCSF. Tropoelastin expression also increased with MSCSF treatment, but to a much lesser degree. 3.2 Elastic Fiber Assembly The second stage of elastic fiber development, elastic fiber assembly, was qualitatively analyzed through indirect immunofluorescence. Constructs were stained for elastin (red, Figure 2) and one of two matricellular proteins: fibrillin-1 (green in Figure 2A & 2C) or fibulin-5 (green in Figure 2B & 2D). Expression of both elastin and the selected matricellular protein provides evidence that the elastic fiber formation pathway has been activated. Nontreated constructs presented elastin with visual confirmation of fibrillin-1 deposition as indicated by Figure 2A, and minimal fibulin-5 (Figure 2B). MSCSF constructs expressed qualitatively greater elastin, fibrillin-1, and fibulin-5 signal than non-treated constructs. Additionally, fibrillin-1 deposition was confirmed, with observed fibers in a parallel configuration (Figure 2C), and fibulin-5 was localized to cell body perimeters. 3.3 Insoluble Elastin The analysis of the final stage of elastic fiber development quantified the amount of insoluble elastin as a fraction of total protein content through ninhydrin assay. Insoluble elastin corresponds to mature, crosslinked elastin and serves as evidence that elastic fiber formation has neared completion. When analyzed, all constructs contained insoluble elastin, however MSCSF treated constructs presented a significantly (p = 0.01) greater quantity of insoluble elastin than non-treated constructs (Figure 3).

Concerning qPCR analysis, no statistical conclusion could be reached as a result of the small sample size of this preliminary study. However, the results demonstrated the trend that, with the application of MSCSF, gene expression for elastin and the three targeted matricellular proteins increased. Notably, LOX has the two-fold function of crosslinking both tropoelastin and collagen. Since no investigation of collagen synthesis in MSCSF treated constructs was performed, the increased LOX expression cannot be uniquely attributed to effects on the elastogenic pathway. However, all three elastin organizational proteins saw a distinct uptick, which does suggest the upregulation of elastogenesis. Additionally, this provides an explanation for the increase in insoluble elastin that was observed through the ninhydrin assay. When MSCSF stimulates SMCs, they produce higher quantities of crosslinking proteins like fibulin-4, fibulin-5, and LOX. The abundance of these proteins then functions to increase the crosslinking between tropoelastin bundles thus increasing the quantity of insoluble elastin. The behavior seen in qPCR analysis was effectively mimicked by the behavior seen in the immunofluorescent data. The presence of elastin, fibrillin-1, and fibulin-5 in all constructs, as detected by indirect immunofluorescence, indicates that elastic fiber formation was initiated. As with the qPCR data, immunofluorescence was also able to demonstrate the qualitative effects of MSCSF on elastogenesis. First, MSCSF treatment stimulated organization of fibrillin-1 into microfibrils. Given that microfibril formation is one of the first steps in the elastic fiber assembly process, the viewed fibrillin-1 organization demonstrates that elastogenesis is encouraged and that fiber quality will be improved compared with the non-treated control. Next, fibulin-5 expression was localized to the cell periphery and was largely increased by MSCSF treatment. As with fibrillin-1, the fibulin-5 indicates a pro-elastogenic environment. Additionally, the patterned localization indicates improved fiber organization since one of the roles of fibulin-5 is to facilitate crosslinking between the extracellular elastic fibers and cell membranes. Lastly, the overall visual increase in elastin once more suggests MSCSF treatment as a means to increase elastogenesis. Overall, adult human SMCs proved viable in 3D fibrinbased tissue constructs and elastogenesis was initiated. Results from qPCR, indirect immunofluorescence, and ninhydrin assay all suggest that MSCSF treatment is

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capable of increasing the quantity and quality of elastic fiber assembly in vitro. However, all presented results are derived from healthy SMCs rather than SMCs with dysfunctional elastogenic mechanisms. Therefore, a future goal will be to investigate the effects of MSCSF in mice models of various genetic elastic fiber disorders. To herald such mouse trials, wild-type mouse SMCs were cultured in parallel to the healthy human SMCs (data not shown). Although elastic fiber formation was not observed using elastin and fibulin-5 fluorescence markers, qualitatively, the mouse SMCs exhibited much lower fibrin tissue construct degradation than the human SMC constructs. Previous reports have suggested that fibrin degradation products generated by the break-down of fibrin tissue constructs assist with ECM production by SMCs [14]. Thus, the lower fibrin degradation rate that was observed in the mouse SMCs may be contributing to the absence of fiber formation. Future studies will look at the effect of varying ACA concentrations for culture of primary mouse SMCs. Overall, this study was limited by the small sample size and by the number of target proteins analyzed. Possible improvements to this experiment include increasing the number of elastic fiber proteins analyzed by both indirect immunofluorescence and qPCR and increasing the number of fibrin tissue constructs. Possible new target proteins for immunofluorescence could include LOX and fibulin-4 so as to match the existing qPCR data. Possible new qPCR targets could include fibrillin-1 and lysyl oxidase like 1 â&#x20AC;&#x201C; a matricellular protein similar to LOX but that interacts with fibulin-5 rather than fibulin-4.

5. Conclusions Culturing human SMCs in three-dimensional fibrin tissue constructs allows for proper cell growth and elastic fiber formation. This behavior was successfully visualized through indirect immunofluorescence and quantified through qPCR and ninhydrin assay analysis. The results of these techniques verified our hypothesis that formation of mature elastic fibers is enhanced both in quantity and in quality by the presence of MSCSF.

Acknowledgements The authors would like to acknowledge funding from the International Studies Fund, the Swanson School of Engineering, and the Office of the Provost at the University of Pittsburgh.

References [1],Ward, J.W. and C. Warren, Silent victories : the history and practice of public health in twentieth-century America. 2007, Oxford ; New York: Oxford University Press. xxi, 484 p. [2],Yamashiro, Y. and H. Yanagisawa, Crossing Bridges between Extra- and Intra-Cellular Events in Thoracic Aortic Aneurysms. Journal of Atherosclerosis and Thrombosis, 2017. advpub. [3],Brangsch, J., et al., Molecular Imaging of Abdominal Aortic Aneurysms. Trends in Molecular Medicine, 2017. 23(2): p. 150-164. [4],Sherratt, M.J., Tissue elasticity and the ageing elastic fibre. AGE, 2009. 31(4): p. 305-325.

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[5],Lin, G., et al., Homo- and heterotypic fibrillin-1 and -2 interactions constitute the basis for the assembly of microfibrils. J Biol Chem, 2002. 277(52): p. 50795-804. [6],Svenja, H., et al., In vitro elastogenesis: instructing human vascular smooth muscle cells to generate an elastic fiber-containing extracellular matrix scaffold. Biomedical Materials, 2015. 10(3): p. 034102. [7],Papke, C.L. and H. Yanagisawa, Fibulin-4 and fibulin-5 in elastogenesis and beyond: Insights from mouse and human studies. Matrix Biology, 2014. 37(Supplement C): p. 142-149. [8],Yanagisawa, H., et al., Fibulin-5 is an elastin-binding protein essential for elastic fibre development in vivo. 2002. 415: p. 168.

[11],Blose, K.J., et al., Periadventitial adipose-derived stem cell treatment halts elastase-induced abdominal aortic aneurysm progression. Regenerative medicine, 2014. 9(6): p. 733-741. [12],Grassl, E.D., T.R. Oegema, and R.T. Tranquillo, A fibrin-based arterial media equivalent. Journal of Biomedical Materials Research Part A, 2003. 66A(3): p. 550-561. [13],Long, J.L. and R.T. Tranquillo, Elastic fiber production in cardiovascular tissue-equivalents. Matrix Biology, 2003. 22(4): p. 339-350. [14],Ahmann, K.A., et al., Fibrin degradation enhances vascular smooth muscle cell proliferation and matrix deposition in fibrin-based tissue constructs fabricated in vitro. Tissue Eng Part A, 2010. 16(10): p. 3261-70.

[9],Zarate, Y.A., et al., Aortic dilation, genetic testing, and associated diagnoses. Genet Med, 2016. 18(4): p. 356-363. [10],Swaminathan, G., et al., Pro-elastogenic effects of bone marrow mesenchymal stem cell-derived smooth muscle cells on cultured aneurysmal smooth muscle cells. Journal of Tissue Engineering and Regenerative Medicine, 2017. 11(3): p. 679-693.

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Assessment of Human Stem Cell Retention and Host Cell Invasion in an Implanted Seeded Tubular Scaffold Abigail M. Snyder1, Katherine L. Lorentz1, Antonio Dâ&#x20AC;&#x2122;Amore1,3, Justin S. Weinbaum1,2, William R. Wagner1,2,3,5, and David A. Vorp1,2,3,4,5 Department of Bioengineering1, Swanson School of Engineering, Department of Surgery3, School of Medicine, Department of Cardiothoracic Surgery4, School of Medicine, and Department of Chemical and Petroleum Engineering5, Swanson School of Engineering, and McGowan Institute for Regenerative Medicine2, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

1. Introduction

As the prevalence and mortality rates of atherosclerosis and/or narrowing of the arteries in the heart and lower limbs increases each year, there is a growing need for advancements in small diameter (<6mm) revascularization conduits. Current grafting options often result in an artery/vein mismatch and/or thrombosis leading to graft failure after a few years. Our lab has developed a clinically-viable small diameter tissue engineered vascular graft (TEVG) based on seeding human mesenchymal stem cells (MSCs) into a biodegradable, biomimetic scaffold. After 8 weeks in vivo, the seeded scaffold remodels into a native-like conduit that lacks seeded MSCs, but contains host vascular cells. The specific time frame in which the seeded MSCs leave the graft is largely unknown, halting the potential for a clinical transition of the graft until this data is obtained. MSC-seeded scaffolds were implanted into the abdominal aorta of Lewis rats. After 1 or 4 weeks in vivo, the grafts were explanted then examined using immunofluorescent chemistry. A qualitative decrease in human nuclear antigen (from ~10 to 2.5%) (indicating MSCs) was observed from 1 to 4 weeks, while calponin (contractile smooth muscle cells) increased (from 0 to 21.1%). Alpha-smooth muscle actin (smooth muscle cells) showed an apparent decrease (from 13.1 to 9.4%), while von Willebrand Factor (endothelial cells) was present and discontinuous at both time points. These results reveal host recellularization of the TEVG at both the 1 and 4-week time points, corresponding with the loss of seeded MSCs. After collection and analysis of additional explants we will have a more robust idea of the cellular migration, ultimately leading to the clinical transition of our TEVG.

Cardiovascular disease is currently the primary cause of death worldwide. Mortality numbers continue to rise each year, amounting to an alarming 23.6 million predicted deaths worldwide by 2030 [1]. Revascularization is a potential treatment for an insufficient blood supply to the heart or lower limbs. Approximately 600,000 surgeries utilizing revascularization techniques are performed annually in the US for conditions including arterial hyperplasia and atherosclerosis of the coronary arteries [2]. The most common revascularization procedure is a coronary artery bypass surgery which uses an autologous graft as the bypass conduit.

Key Words: Tissue engineered vascular graft, mesenchymal stem cells, immunofluorescent chemistry, and migration.

1.1 Autologous Grafts The gold standard of autologous grafts is the internal mammary artery [3]. However, even this gold standard has a major limitation: patients in need of revascularization often have already utilized these arteries, or the arteries are too damaged to use. Thus, the next viable autologous grafting option utilizes the saphenous vein or the radial arteries. Radial arteries provide the same difficulties as the internal mammary arteries, since they often arenâ&#x20AC;&#x2122;t available to use. While the saphenous vein is usually viable, veins are a mechanical mismatch compared to arteries, which can lead to failure. Any autologous graft also requires a secondary harvest procedure, which increases the time, cost, and potential for morbidity during surgery [3]. The harvest site provides an additional surgical site that increases the possibility of post-operative infection [4]. 1.2 Synthetic Grafts Synthetic grafts prove to be a suitable replacement for medium (6-8mm) and large (>8mm) diameter vascular grafts. When making the transition to a small diameter graft (<6mm), synthetic grafts show diminishing patency results [5]. Commonly comprised of polytetrafluoroethylene, these grafts also provide biological issues at the

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blood-material interface leading to thrombosis and graft failure due to hyperplasia or acute occlusion [3]. Pediatric cases also present a pressing concern since the patient would require multiple procedures as they mature, and their vasculature grows. 1.3 Current Methods and Our Approach A clinically-viable small diameter tissue engineered vascular graft (TEVG) would fill the need for a suitable revascularization graft. Our lab combines the biologic and synthetic approach by seeding human mesenchymal stem cells (MSCs) into a biodegradable, biomimetic scaffold. This cellular seeding technique has shown promising results in our lab as well as various others such as the Shinoka and Breuer groups [6,7]. MSCs offer several advantages over primary cells including their ease of isolation, self-renewal capacity, differentiation potential, and ability to secrete a wide spectrum of factors with varying pro-remodeling effects [8]. After 8 weeks of in vivo remodeling, the seeded scaffold becomes a vessel-like TEVG. As a measure of primary success, the TEVG remains patent over the 8-week period, but it also changes composition as evidenced by the presence of new rat endothelial cells, smooth muscle cells, collagen, and elastin. Notably, the seeded human cells which were present in the originally implanted scaffold no longer remain at the end of 8 weeks.

device, the cultured MSCs (ranging from passage 3 to passage 9) were seeded into a poly (ester urethane) urea (PEUU) biodegradable, bilayered, elastomeric tubular scaffold. The PEUU is first formed into a porous inner layer using thermally induced phase separation, or TIPS, to support cell seeding and integration into the pores [1]. A second layer of PEUU is then utilized to form a denser electrospun outer layer to give mechanical support. A 48-hour dynamic culture period in a custom spinner flask post seeding was included to allow cells to bind to the scaffold prior to implantation. 2.2 Construct Implant/Explant After anesthetizing a Lewis rat (Charles River Laboratories, Wilmington, MA), a section of the abdominal aorta was isolated, clamped and transected. The seeded scaffold was then sutured interpositionally into the infrarenal abdominal aorta. After releasing the clamps and observing blood flow through the graft, the rat was closed. After 1 or 4 weeks in vivo (n=1 and 3, respectively) angiography was performed to assess graft patency, as seen in the TEVG in Fig. 1; the TEVGs were then explanted, and the rats sacrificed.

The fate of the seeded MSCs is unclear. It does not appear that the MSCs transdifferentiate into vascular cells. Rather, the evidence points to these cells being mediators of host cell recruitment [7]. Within the first week after implantation, a blank (non-seeded) scaffold will have a 100% occlusion rate [8]. This demonstrates the key role of the seeded MSCs to prevent acute thrombosis within the first week of implantation. There is a critical need to obtain a more specific time frame of when the seeded cells and their important paracrine recruitment factors leave as the native cells accumulate in the graft. We hypothesize that the seeded MSCs depart the graft, inversely corresponding to the time host cells repopulate the scaffold. We decided to look at the native smooth muscle and endothelial cells entering the graft, along with the human MSCs departing, using immunofluorescent chemistry.

2. Methods 2.1 Cell Seeding Adult human MSCs (RoosterBioÂŽ, Frederick MD) were cultured in RoosterNourish-MSC (KT-001) media in T-175 flasks. Utilizing a rotational vacuum seeding

Figure 1. Confirmation of TEVG Patency: An angiogram of the Lewis rat that was taken at the 4 week time point, before explant. Note the dye can be seen traveling through the TEVG to the branching renal arteries, showing no occlusion of the graft.

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2.3 Immunochemistry Explanted TEVGs were fixed in paraformaldehyde for 40 minutes, soaked in 30% sucrose, and frozen down using OCT media (Fisher HealthCare, Houston TX). The central portion of the graft (furthest from the suture sites) was sectioned to a thickness of 9 μm and mounted on gelatin-coated slides, as done previously in our lab [1]. After sectioning, the samples were stained using immunofluorescent chemistry for human nuclear antigen (HNA), alpha-smooth muscle actin (αSMA), calponin (to mark contractile smooth muscle cells), and von Willebrand factor (vWF, to mark endothelial cells) (all antibodies were sourced from Thermo Fisher Scientific, Waltham, MA). These markers were chosen because smooth muscle cells and endothelial cells are vital components that we know to be present in native vasculature. Imaging was completed at both 10x and 20x using an epifluorescence microscope and its corresponding NIS Elements software (Nikon Instruments, Melville, NY, USA).

3. Results TEVGs were observed to show 100% patency at 1 week (n=1) and 4 weeks (n=3) post-implant. Immunostaining was positive, yet discontinuous along the luminal lining for the vWF stains at both 1 and 4 weeks post-implant, indicating that the endothelial lining is not yet fully developed after 4 weeks, even though endothelial cells are present as early as 1 week post implantation. Fig. 3 illustrates the presence of the different markers (HNA, calponin, αSMA, and vWF) indicating the presence of human MSCs, early and late stage smooth muscle cells,

2.4 Cell Quantifications The desired images were quantified for the presence of positive DAPI (total cell count) staining using a custom algorithm in ImageJ software (Fig. 2 image B) [9]. The images were then manually quantified for the presence of positive HNA, αSMA, and calponin markers as demonstrated in Fig. 2 image A. A total cell count (positive DAPI staining) combined with the positive expression of HNA, αSMA, or calponin allowed us to determine a percentage of human and contractile smooth muscle cells within our graft at the different time points.

Figure 2. Methodology for Quantifying Cellularity and Cell Phenotype: (A) A section of the scaffold image was imaged at 10x and cells positive for the stain of interest were counted by hand (+). (B) Total cell count was obtained by an ImageJ algorithm.

Figure 3. Qualitative Image Stain Comparisons of HNA, Calponin, αSMA, and vWF in 1 and 4 Week TEVG Explants: Human cells (HNA, red) were present at both 1 (a) and 4 (b) weeks, albeit reduced at the 4-week time point. No late stage smooth muscle cells (calponin, red) were expressed at the 1 (c) week time point. The initial calponin(red) presence is observed in the 4 (d) week time point. Early stage contractile smooth muscle cells (αSMA, red) were observed to have decreased from the 1(e) to 4 (f) week time point, respectively. Positive yet discontinuous staining along the luminal layer for endothelial cells (vWF, green) is seen in the 1 (g) week time point and similarly in the 4 (h) week time point. In all images, nuclei (blue) were stained by DAPI. Scale bar = 100 µm

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Figure 4. Quantification of Immunofluorescence Data Reveals Departure of Human MSC and Appearance of Calponin-positive Cells: Graphical display showing the results of HNA decrease, αSMA decrease and calponin increase between 1(blue) and 4(grey) weeks.

and endothelial cells in our TEVG explants. We observed an apparent decrease in the percentage of HNA positive vascular cells from 1 to 4 weeks (from ~10 to 2.5%, respectively, see Fig. 4). Similarly, the percentage of vascular cells that were observed to be αSMA positive appeared to decrease (from 13.1 to 9.4%, respectively). An apparent increase in the percentage of calponin positive cells was also observed from 1 to 4 weeks (from 0 to 21.1%, respectively).

4. Discussion 4.1 Our Findings Our hypothesis was supported in that a qualitative loss of seeded human cells was observed as the native cells migrated in. Since the HNA results show the presence of human cells still remaining in the graft as late as 4 weeks, this may indicate a prolonged role for the seeded MSCs as active modulators of host cell remodeling or thrombosis. These findings are surprising in the context of previous studies performed by the Breuer group, indicating cell evacuation within the first week [7]. Since non-seeded TEVGs have a 100% occlusion rate within the first week post implantation, the seeded MSCs play a key role in the prevention of acute thrombosis and overall success of the graft, despite their early evacuation [8].

An example of progressive host cell remodeling can be seen in both the αSMA and calponin results. The presence of αSMA without calponin in the 1-week explant shows that the smooth muscle cells are maturing and continually migrating into the graft. The calponin stain can be expressed in human MSCs, however we use a rat specific stain that allows us to conclude that the positive staining is indeed from the smooth muscle cells and not the seeded MSCs. The positive staining results from the vWF show the presence of endothelial cells as early as the one week time point. Since we observed discontinuous vWF fluorescence staining along the lumen, we can conclude that the endothelial lining is not yet completely formed even at 4 weeks. Partial endothelization along with the increasing smooth muscle cells and late stage smooth muscle cells demonstrate the successful preliminary stages of remodeling, primarily the infiltration of host cells into the TEVG. 4.2 Limitations The most prominent limitation of this study is the lack of samples we have for the 1-week and 4-week time points. Without more explants from these time points we are prevented from stating the significance of our current data. Another potential limitation we face is the seeded MSCs source. Optimally this graft will seed human stem cells into a graft for a human subject; however, we are currently utilizing human MSCs in a Lewis rat model. Lewis rats are an inbred strain of rat that has minimal to no detectable immune rejection of implanted human cells [10-14]. Utilizing this model with human cells we have never encountered immunological complications, and explants for up to one year have shown no signs of lymphocyte activity.

5. Conclusion From the current observations seen in the 1 and 4-week timepoints, our hypothesis has thus far been supported. There seems to be an inverse relationship between the departing human MSCs and the infiltration of the native rat cells. The HNA results demonstrated an apparent decrease in the human MSCs from 1 to 4 weeks. Calponin was found to show an apparent increase from 1 to 4 weeks. vWF was observed to be present and discon-

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tinuous both at the 1 and 4-week time point. Along with data already generated for 8 week explants [1], we plan to collect more samples at the 1 and 4-week time points to gather a more robust picture of cellular infiltration and human MSC evacuation. Additionally, data collection at a later timepoint, such as 6-weeks, would help us determine when 100% of the initially seeded MSCs are no longer present therefore no longer playing a remodeling role within our graft. After the collection and analysis of the data is completed we will have a more robust idea of when the paracrine recruitment factors leave, and native host cells migrate in during these 8 weeks of remodeling, allowing us to move closer to a clinical transition.

Acknowledgements I would like to acknowledge funding from Dr. David Vorp, the Swanson School of Engineering and the Office of the Provost. I would also like to acknowledge Alex Josowitz for writing the ImageJ cell quantification code.

References [1] Krawiec, J. T., el al. (2016). “In Vivo Functional Evaluation of Tissue-Engineered Vascular Grafts Fabricated Using Human Adipose-Derived Stem Cells from High Cardiovascular Risk Populations.” Tissue Eng Part A 22(9-10): 765-775. [2] Rosamond W, el al. (2008). “Heart disease and stroke statistics--2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.” Circulation. 117(4):e25-146. Epub 2007 Dec 17 [3] Ravi S, Chaikof el al. (2010). “Biomaterials for vascular tissue engineering.” Regenerative medicine. 107. doi:10.2217/rme.09.77. [4] Daenens, K., et al. (2003). “Ten-year experience in autogenous reconstruction with the femoral vein in the treatment of aortofemoral prosthetic infection.” Eur J Vasc Endovasc Surg 25(3): 240-245. [5] Pashneh-Tala, S., et al. (2016). “The Tissue-Engineered Vascular Graft—Past, Present, and Future.” Tissue Engineering. Part B, Reviews 22(1): 68-100. [6] Patterson, Joseph T et al. “Tissue-Engineered Vascular Grafts for Use in the Treatment of Congenital Heart Disease: From the Bench to the Clinic and Back Again.” Regenerative Medicine 7.3 (2012): 409–419. PMC. Web. 8 Dec. 2017.

[7] Roh, J. D., et al. (2010). “Tissue-engineered vascular grafts transform into mature blood vessels via an inflammation-mediated process of vascular remodeling.” Proc Natl Acad Sci U S A 107(10): 4669-4674. [8] Krawiec, J. T., et al. (2015). “A cautionary tale for autologous vascular tissue engineering: impact of human demographics on the ability of adipose-derived mesenchymal stem cells to recruit and differentiate into smooth muscle cells.” Tissue Eng Part A 21(3-4): 426-437. [9] Josowitz A, K.J., Fedorchak M, D’Amore A, Weinbaum JS, Wagner WR, Little S, Vorp DA, “Characterizing The Seeding Distribution Of Microspheres In Tissue Engineered Vascular Grafts,” in Annual Meeting of the Biomedical Engineering Society. October 2015. [10] Fitzpatrick JR, 3rd, Frederick JR, McCormick RC, Harris DA, Kim AY, Muenzer JR, et al. Tissueengineered pro-angiogenic fibroblast scaffold improves myocardial perfusion and function and limits ventricular remodeling after infarction. The Journal of thoracic and cardiovascular surgery. 2010;140(3):667-76. doi: 10.1016/j.jtcvs.2009.12.037. PubMed PMID: 20363480. [11] He W, Nieponice A, Soletti L, Hong Y, Gharaibeh B, Crisan M, et al. “Pericyte-based human tissue engineered vascular grafts.” Biomaterials. 2010;31(32):8235-44. [12] Leong WK, Henshall TL, Arthur A, Kremer KL, Lewis MD, Helps SC, et al. “Human adult dental pulp stem cells enhance poststroke functional recovery through nonneural replacement mechanisms.” Stem cells translational medicine. 2012;1(3):177-87. doi: 10.5966/sctm.2011-0039. PubMed PMID: 23197777; PubMed Central PMCID: PMC3659845. [13] Nieponice A, Soletti L, Guan J, Hong Y, Gharaibeh B, Maul TM, et al. “In vivo assessment of a tissueengineered vascular graft combining a biodegradable elastomeric scaffold and muscle-derived stem cells in a rat model.” Tissue Engineering Part A. 2010;16(4):1215-23. [14] Rogers SA, Mohanakumar T, Liapis H, Hammerman MR. “Engraftment of cells from porcine islets of Langerhans and normalization of glucose tolerance following transplantation of pig pancreatic primordia in nonimmunesuppressed diabetic rats.” The American journal of pathology. 2010;177(2):854-64. doi: 10.2353/ajpath.2010.091193. PubMed PMID: 20581052; PubMed Central PMCID: PMC2913376.

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The Effect of Zeolite Additives on Li-ion Conductivity of Gel-Polymer Electrolytes Philip A. Williamsona, Pavithra M. Shanthib, Ramalinga Kurubac, Prashanth J. Hanumanthac and Prashant N. Kumtaa-c

Department of Mechanical Engineering and Materials Science, bDepartment of Chemical and Engineering, and c Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract As the demand for efficient energy storage devices rises, lithium-sulfur batteries continue to be a technology of interest due to their high theoretical charge density. To effectively utilize this electrochemical system for energy storage, novel cell components need to be developed to ensure the Li-S cell functions as needed. Recent developments in this area include gel-polymer electrolytes (GPE) [1] and separators with zeolite additives [2]. Our research is concerned with the ability of zeolite infused GPEs to mitigate the negative effects of intermediary reactions in the Li-S battery. The present study performs initial characterization on zeolite infused GPEs by investigating their morphology and ionic conductivity. Battery separators made from non-woven polymer fiber mattes containing zeolite fillers were prepared by electrospinning. Gel-polymer electrolytes were then prepared by soaking these separators in a lithium ion electrolyte solution. The dependence of the ionic conductivity of GPE on zeolite content was investigated using electrochemical impedance spectroscopy. The difference in electrical conductivity between the GPEs with zeolite filler and those without zeolite filler was found to be negligible. SEM images revealed that the fibers of electrospun polymer separators displayed crystals along their lengths, indicating successful mixing between these polymer fibers and the zeolite fillers. Key Words: Lithium-Sulfur battery, Gel-polymer electrolyte, zeolite

1. Introduction The increasing demand for powerful electrochemical energy storage devices (EESDs) has spurred research into novel technologies and chemistries for use in a variety of energy storage applications. Among these exploratory technologies, lithium-sulfur batteries incorporating a metallic lithium anode and a sulfur cathode in the form of

a carbon-sulfur composite material are emergent systems. Bruce et al. claim sulfur is an attractive cathode material as Li-S cells exhibiting theoretical specific energy of 2567 Wh/kg, compared to the 387 Wh/kg specific energy of conventional lithium cobalt oxide and carbon batteries [3]. This high capacity is based on the reversible reaction of sulfur with lithium to form lithium sulfide (Li2S) by accepting two electrons per sulfur atom via the reaction: 2Li+ + 2e- + S â&#x2020;&#x201D; Li2S This is compared to the one or fewer electrons accepted per transition-metal ion in the insertion-oxide cathodes of conventional lithium ion batteries. However, this reaction takes place over a series of intermediary reactions wherein lithium reacts with cyclic sulfur to form polysulfides (Li2Sx, 2 < x <=6) [4]. Unfortunately, polysulfide formation is a technical challenge in these cells as the polysulfides are soluble in traditional battery electrolytes (e.g. EC, DEC, DMC) and can shuttle between the electrodes of the cell, passivating them [4,5]. One proposed solution to impede polysulfide shuttling is the generation of gel-polymer electrolytes (GPEs). Rao et al. make the claim that polysulfides are less soluble in GPEs and this leads to a suppressed shuttling effect. Their data, along with data from Marmorstein et al., show that Li-S batteries utilizing a gel-polymer electrolyte experience a lower capacity fade, an effect attributed to suppressed polysulfide dissolution and shuttling [6,7]. Polymer electrolytes in general have several advantages, including no internal shorting and no leakage of electrolytes [8]. Gel-polymer electrolytes are also preferable to solid polymer electrolytes (SPEs) as the latter tends to have low ionic conductivity at ambient temperatures compared to liquid electrolytes [1]. Incorporating additives and fillers into gel-polymer electrolytes may also improve their performance. Recent studies have investigated the effects ceramic particles and

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molecular sieves have on ion conductivity and mechanical strength in solid-polymer electrolytes [9]. Nunes-Pereira et al. demonstrated that NaY zeolite additives improve the ion conductivity of GPEs due to their crystal structure providing sites for ion transport [2]. In our research, we are interested in exploring the possibility of using another zeolite, ZSM-5, in the GPEs to improve Li-ion conductivity and suppress polysulfide shuttling. In this present study, initial characterization is performed on the gel-polymer electrolytes (GPEs) infused with the zeolite ZSM-5 to test the claim that zeolites improve ion conduction. Additionally, the morphology of the electrospun polymer fibers with zeolite fillers is studied to determine how well the two materials mix to form a composite during the electrospinning process.

2. Methods Solutions of 10wt% polymer and 1wt% ZSM-5 in DMF were prepared. Polyacrylonitrile (PAN) with 150,000g/ mol average MW was used as the polymer in one solution. The second solution used 5wt% Poly(vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP), having approximately 400,000g/mol average MW, and 5wt% PAN. PAN (CAS Number 25014-41-9) and PVdF-HFP (CAS Number 9011-17-0) were both obtained from SigmaAldrich. DMF was also purchased from Sigma-Aldrich (CAS Number 68-12-2) and ZSM-5 was obtained from ACS Material, LLC (CAS Number 1318-02-1). Two more solutions identical to these were prepared without ZSM-5. All the solutions were stirred at 40 °C for 2 hours. Electrospun mattes were then prepared at room temperature from each solution using methods outlined by Shi et al. [10]. A syringe was filled with 10mL of solution and fitted with a Luer-lock style steel dispensing needle obtained from Ramé-Hart Instrument Co. The solution flowrate was maintained at 1.25mL/hr using a Chemyx Fusion 200 syringe pump. The tip of the needle was placed 10cm from a rotating steel drum wrapped in aluminum foil to collect fibers. A 20kV potential was applied between the needle and drum using an ES20 voltage source obtained from Gamma High Voltage Research. After electrospinning was completed, each matte was dried in vacuum for 12 hours to evaporate any remaining DMF. The morphology of the electrospun mattes was studied with scanning electron microscopy using a JEOL JSM-6510 machine to determine the degree of mixing between the zeolite crystals and the polymer fibers. The presence of zeolite in the electrospun polymer fibers was verified by the observation of key features, including

crystals and extensions (changes in fiber diameter) along the fiber lengths. The electrospun mattes were punched into 1.75cm diameter circular separators. The separators were then placed in an argon glovebox where temperature was maintained at 25 °C and O2 and H2O levels were maintained below 5ppm. To make gel-polymer electrolytes, the dry separators were allowed to soak for 10 minutes in an electrolyte composed of 1.8M lithium triflate (LiCF3SO3¬, CAS Number 33454-82-9) and 0.1M lithium nitrate (LiNO3, CAS Number 7790-69-4), both obtained from SigmaAldrich, in a 50:50 by volume dioxolane/dimethoxyethane (DOL/DME) solvent. Anhydrous DOL (CAS Number 646-06-0, <0.005%) and DME (CAS Number 110-71-4, <0.03% water) were purchased separately from Sigma-Aldrich. After soaking, coin cells were assembled by sandwiching each GPE between two layers of metallic lithium in 2025-type coin cells. Electrochemical impedance spectroscopy (EIS) was performed on the Li/GPE/Li coin cells using a Gamry Potentiostat applying an AC voltage of 10 mVRMS with frequency ranging from 10 mHz to 100 kHz. Equivalent circuit modeling was performed using Z-view 2.0 (Scribner Associates Inc.) to obtain the bulk resistance value for each GPE using methods as described in the literature [11].

3. Results and Discussion Figure 1 shows the SEM images of the four electrospun mattes.

Figure 1. SEM images taken of a) PAN electrospun fibers, b) PVdF-HFP/PAN (50:50) electrospun blended fibers, c) PAN fibers with ZSM-5, and d) PVdF-HFP/PAN (50:50) blended fibers with ZSM-5.

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Figure 1a and Figure 1b respectively show images of PAN fibers and of the 50:50 PVdF-HFP/PAN blended fibers, both without zeolite. Figure 1c and Figure 1d show images of PAN fibers with zeolite and of the 50:50 PVdFHFP/PAN blended fibers with zeolite. These latter images show thicker fibers with some extensions and crystals resting along the polymer fibers. The portions of the fiber length showing extensions can be attributed to the inclusion of ZSM-5 particles into the fiber structure. These images suggest that the zeolite has successfully mixed with the PAN fibers and the PVdF-HFP/PAN blended fibers during the electrospinning process. Comparison of these with the polymer fibers in Figure 1a and Figure 1b, show clearly the smoother morphology of the fibers exhibiting no extensions. Nyquist plots obtained from the EIS tests of our Li/GPE/ Li coin cells are shown in Figure 2.

Figure 3. Equivalent circuit model used to determine GPE resistance from EIS data

conductivity of each GPE, taking the separator thickness and area into account using Formula 1. These values are shown in Table 1.

Table 1. Ionic conductivity of the different GPE systems.

Figure 2. EIS Nyquist plots for the GPE systems.

To determine the resistance of each GPE, equivalent circuit modeling was performed by comparing each coin cell to the simulated circuit depicted in Figure 3, using principles outlined in the literature [11]. Here, the resistor element RS models the resistance of the systemâ&#x20AC;&#x2122;s electrolyte, or GPE. The parallel resistor, RP, and constant phase element (CPE) model capacitance and resistance effects at the electrode-electrolyte interface. Using Z-View 2.0, the parameters of this simulated equivalent circuit are adjusted until the response obtained is comparable to the experimentally obtained EIS data. Once the appropriate parameters are determined, the resistance for RS is taken to be the resistance of the GPE. This bulk resistance is used to determine the Li-ion

While the zeolite additives were expected to improve conductivity, the values in Table 1 show that there is minimal difference in the ionic conductivity between the separators made from only the polymer(s) contrasted with those containing zeolite. These results could be due to several factors. The concentration of zeolite in the electrospinning solution may have been too low to significantly affect the conductive properties of the GPE separators. The zeolite/polymer ratio in the nonwoven mattes produced for this study was approximately 10% while the zeolite/polymer ratio in the study performed by NunesPereira et al. was as high as 32%. Morphology could also be impacting this outcome. The study performed by Nunes-Pereira et al. used membranes made by solution casting rather than electrospinning, as is described in the current study. Finally, the type of zeolite species used may affect the degree of improved conductivity as the NaY zeolite used in the Nunes-Pereira et al. study and the ZSM-5 used in this study differ in their chemical and ionic compositions [2].

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4. Conclusions We have successfully generated a composite of polymer with zeolite via the electrospinning process. The goal was to explore the mixing of polymers with ZSM-5. SEM images of these fibers indicate successful mixing of polymer fibers and zeolite particles during electrospinning. In regards to ion conductivity, our data did not show a significant difference between GPEs with zeolite and those without. This may be because of the low amount of zeolite used to generate the electrospun mattes. Morphology and zeolite species type may also have an effect. This result warrants further studies to explore the effect of zeolite proportion, zeolite species, and morphology on the ionic conductivity of the GPE. Additionally, the effects that the zeolites have on polysulfide shuttling suppression is yet to be investigated to determine if they improve the performance of Li-S batteries.

Acknowledgements Support for this research was provided by the Swanson School of Engineering, Department of Energy, the National Science Foundation, Edward R. Weidlein Chair Professorship funds, the Center for Complex Engineered Multifunctional Materials (CCEMM) and the Office of the Provost.

References [1] J.Y. Song, Y.Y. Wang, C.C. Wan, Review of gel-type polymer electrolytes for lithium-ion batteries, J. Power Sources. 77 (1999) 183–197. doi:10.1016/S03787753(98)00193-1. [2] J. Nunes-Pereira, A.C. Lopes, C.M. Costa, L.C. Rodrigues, M.M. Silva, S. Lanceros-Méndez, Microporous membranes of NaY zeolite/poly(vinylidene fluoride- trifluoroethylene) for Li-ion battery separators, J. Electroanal. Chem. 689 (2013) 223–232. doi:10.1016/j. jelechem.2012.10.030.

[3] P.G. Bruce, S.A. Freunberger, L.J. Hardwick, J.-M. Tarascon, Li–O2 and Li–S batteries with high energy storage, Nat. Mater. 11 (2011) 172–172. doi:10.1038/ nmat3237. [4] A. Manthiram, Y. Fu, S. Chung, C. Zu, Y. Su, Rechargeable Lithium − Sulfur Batteries, Chem. Rev. 114 (2014) 11751–87. doi:10.1021/cr500062v. [5] M. Wild, L. O’Neill, T. Zhang, R. Purkayastha, G. Minton, M. Marinescu, G.J. Offer, Lithium sulfur batteries, a mechanistic review, Energy Environ. Sci. 8 (2015) 3477–3494. doi:10.1039/C5EE01388G. [6] M. Rao, X. Geng, X. Li, S. Hu, W. Li, Lithium-sulfur cell with combining carbon nanofibers-sulfur cathode and gel polymer electrolyte, J. Power Sources. 212 (2012) 179–185. doi:10.1016/j.jpowsour.2012.03.111. [7] D. Marmorstein, T.H. Yu, K.A. Striebel, F.R. Mclarnon, J. Hou, E.J. Cairns, Electrochemical performance of lithiumrsulfur cells with three different polymer electrolytes, J. Power Sources. 89 (2000) 219–226. doi:10.1016/ S0378-7753(00)00432-8. [8] A.M. Stephan, Review on gel polymer electrolytes for lithium batteries, Eur. Polym. J. 42 (2006) 21–42. doi:10.1016/j.eurpolymj.2005.09.017. [9] S. Jung, D.W. Kim, S.D. Lee, M. Cheong, D.Q. Nguyen, B.W. Cho, H.S. Kim, Fillers for solid-state polymer electrolytes: Highlight, Bull. Korean Chem. Soc. 30 (2009) 2355–2361. doi:10.5012/bkcs.2009.30.10.2355. [10] X. Shi, W. Zhou, D. Ma, Q. Ma, D. Bridges, Y. Ma, A. Hu, Electrospinning of Nanofibers and Their Applications for Energy Devices, J. Nanomater. 2015 (2015). doi:10.1155/2015/140716. [11] J.R. Scully, D.C. Silverman, Electrochemical Impedance : Analysis and Interpretation, ASTM International, Philadelphia, PA, 1993. doi:10.1520/STP1188-EB.

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The Effect of an Osteoarthritis Unloader Brace on Knee Joint Space During Gait Shumeng Yang1, Kanto Nagai2, 3, and William Anderst2

Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA 2 Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA 3 Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Japan

Abstract Osteoarthritis (OA) is one of the most common joint conditions in the US. OA unloader braces are intended to reduce pain by unloading the medial compartment of the knee. The purpose of this study was to quantitatively evaluate the effects of a DonJoy unloader brace on joint space during walking. It was hypothesized that medial compartment joint space would increase and ground reaction force (GRF) decrease with brace use. Ten OA patients were tested while walking on an instrumented treadmill within a biplane X-ray system for three trials with and without the brace. CT scans of the femur and tibia were reconstructed into 3D models, and tibiofemoral motion was determined using a validated model-based tracking process from biplane radiographs. The medial tibial plateau was divided into 9 regions, and the region with smallest joint space was selected for analysis. Joint space and GRF were measured over the first 40% of the gait cycle, and compared between unbraced and braced conditions. Brace use increased joint space by an average of 0.27 mm while there was no significant difference in GRF. This suggests the brace caused a reduction in medial compartment loading in OA patients. Key Words: Knee osteoarthritis, Unloader brace, Joint space, biplane radiography

1. Introduction OA is one of the most common chronic joint conditions, affecting an estimated 27 million Americans [1]. The knee is the most commonly affected joint, with approximately 6% of US adults 30 and older living with symptomatic OA in the knee. The medial compartment is more commonly affected than the lateral compartment during normal gait [2,3]. The compression of the medial compartment of the joint is often the source of pain for OA patients. OA unloader braces are a noninvasive

treatment believed to reduce pain, improve function, and possibly slow OA progression by unloading the medial compartment of the knee. Increasing medial compartment joint space is thought to shift a portion of the knee load from the medial compartment to the lateral compartment, targeting the source of pain. Controversy still exists as to whether the brace has a real effect on tibiofemoral joint space in the medial compartment during functional activity in OA patients. Previous studies have determined the effect of OA unloader braces using techniques including force sensors, torque analysis, and two-dimensional fluoroscopy with mixed results regarding the effect on knee joint space [3,4]. These results are limited by a lack of dynamic in vivo quantitative data for joint space because of the difficulty of performing in vivo analysis during movement. This is the first study to combine dynamic data and loading. The purpose of this research was to quantitatively evaluate the effects of a DonJoy OA unloader brace on dynamic joint space during level walking. It was hypothesized that medial compartment joint space would increase when wearing the brace compared to without the brace due to the unloading actions of the brace, and loading would decrease due to patients favoring the unaffected leg.

2. Materials and Methods 2.1 Subjects Prior to evaluation, 10 subjects (Age: 52±8years; 8 male, 2 female; BMI: 27±4) were instructed on brace fitting and use by a trained orthotist and wore the brace at least two hours daily for at least two weeks. (Defiance, DonJoy Inc.). Institutional Review Board (IRB) approval and informed consent were obtained. Patients were eligible if they were 35 to 65 years old and diagnosed with symptomatic unicompartmental OA with <10° varus knee alignment and no evidence of ligament instability. Exclusion criteria included history of tibial or femoral fracture, previous knee surgery, cardiovascular disease or diabetes, neurological or balance impairment, and rheumatoid arthritis.

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2.2 Data Collection Testing consisted of walking on a level instrumented treadmill (Bertec Corp, Columbus, OH) at 1.0 m/s within a biplane radiography system. Three trials were performed for each condition, braced and unbraced walking, in a randomized order. Synchronized biplane radiographs were collected at 100Hz using a Dynamic Stereo X-ray (DSX) system (Figure 1). The ground reaction force (GRF), normalized to each subjectâ&#x20AC;&#x2122;s body weight, was measured at 1000Hz using the instrumented treadmill.

Figure 2. The model-based tracking technique using the femur. The radiographs obtained during testing (red radiographs #1 and #2) are compared to CTreconstructed radiographs (green projections onto the radiographs) created by a ray-tracing algorithm for a given position and orientation of the bone.

Figure 1. DSX system, positioned to obtain anteriorsuperior and anterior-inferior views of the knee during gait while eliminating occlusion of bony details by the brace

2.3 Data Processing Patient-specific high-resolution CT scans of the knee (0.4 x 0.4 x 1.25 mm) were segmented and reconstructed into three-dimensional (3D) models of the femur and tibia (Mimics, Marterialise, Inc. Leuven, Belgium). Modelbased tracking was used to measure tibiofemoral motion by optimizing the correlation between the 3D bone models obtained from the CT images with radiographs taken from the biplane x-ray images. This determines its exact pose at each frame. (Figure 2). This method has been validated for tracking the femur and tibia with a precision of 0.9Â° or better in rotation and 0.7 mm or better in translation [6]. Six degree-of-freedom rotation and translation of the tibia relative to the femur were calculated for each trial using a joint coordinate system proposed by Grood and Suntay [5]. The bone motion data were filtered at 10 Hz using a 4th order, low-pass Butterworth filter to calculate joint kinematics in a 6 degree of freedom (DOF) anatomical coordinate system. Data from 0-40% of the gait cycle was available for 9 subjects, and data for 0-20% of the gait cycle was available for 1 subject.

2.4 Dynamic Joint Space Calculation The medial tibial plateau was divided into 9 regions (Figure 3). Dynamic joint space was calculated for each region by measuring the minimum distance from each point on the subchondral bone surface of the tibia to nearest point on the subchondral bone surface of the femur and averaging the minimum distances [7]. Average joint space in each of the 9 regions and ground reaction forces were measured over 10% intervals for the first 40% of the gait cycle starting at heelstrike, and averaged over the three braced and unbraced trials. This interval was selected to capture most of the loading phase of the gait cycle and keep the knee in the imaging area of both X-ray sources. The average minimum joint space in the region with smallest space was selected for statistical analysis. The time of heelstrike and gait cycle lengths were determined using GRF data from the treadmill. The difference in joint space and ground reaction force between the braced and unbraced walking conditions was evaluated using two-way repeated-measures ANOVA, and the differences within each 10% increment of the gait cycle was determined using post-hoc paired t-tests (SPSS Statistics, IBM Software Group, Chicago, IL, USA).

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4. Discussion

Figure 3. The medial tibial plateau divided into 9 regions. Region 5 was selected for 9 of the 10 subjects, and region 8 for 1 subject based on their minimal dynamic joint space.

3. Results Medial compartment joint space increased when using the OA unloader brace by an average of 0.27 Âą 0.15 mm (ANOVA: p=0.004) (Figure 5A). The difference between average joint space in unbraced and braced trials ranged from -0.04 to 0.92 mm, with negative values indicating a decrease in joint space after bracing. The greatest average difference in joint space was 0.4 mm, observed during the first 10% of the gait cycle. The smallest average difference in joint space was 0.2 mm, observed during the second 10% of the gait cycle. No significant difference in GRF was observed with and without brace use (mean 0.019 % body weight, ANOVA: p=0.15) (Figure 5B).

The main finding of this study is that medial compartment dynamic joint space increased when wearing an OA unloader brace. This finding supports the first hypothesis and indicates decreased cartilage loading in the knee. The two-way ANOVA shows no overall change in GRF, but found a difference in joint space. Post-hoc paired t-tests indicates that this difference is apparent in each section of the gait cycle. The lack of significant difference in GRF between braced and unbraced trials refutes the second hypothesis and suggests that participants were not significantly reducing loading in the knee while wearing the brace. The GRF data was consistent with the average joint space for each section: The first 10% of the gait cycle was associated with the largest joint space and lowest GRF, while the 10-20% section of the gait cycle had the smallest joint space and highest GRF. This difference was present from heel strike through mid-stance phase. Overall, these results suggest that the brace is effective in increasing joint space and could be used as treatment for OA. These findings agree with several previous studies that have used single-frame images to calculate static joint space [4, 8]. In contrast, Haladik et al. failed to detect significant changes in medial joint space between braced and unbraced conditions using similar dynamic biplane radiography imaging. These discrepancies could be due to several factors: the previous study looked at minimum instead of average joint space over a region, did not examine GRF, and did not randomize testing order [9].

Figure 5. Average dynamic joint space in the medial compartment (A) and average GRF (B) over 10% intervals from heelstrike to terminal stance in braced and unbraced conditions. Data shown as mean Âą 1 SE.

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5. Conclusion The unloader brace induced a small but significant increase in medial compartment dynamic joint space during gait while no significant differences in GRF during gait were found between unbraced and braced conditions. This suggests the increase of medial joint space was not due to decreased loading of the affected limb, but due to the unloading actions of the brace. Therefore, OA unloader brace may reduce medial compartment joint loading during dynamic loading activities, and is an effective treatment that can alleviate symptoms for OA patients. This study includes several limitations, including a small sample size and lack of data for the push-off phase of the gait cycle. Future work may include examining the effect of bracing on lateral compartment joint space, as well as comparing the effect of different brace models on joint space and stability.

Acknowledgements This project was funded by DonJoy Orthopaedics, the Swanson School of Engineering, and the Biodynamics Lab. The Defiance braces were provided by DJO, Inc.

References [1] Jordan et al. Osteoarthritis: New Insights. Part 1: The Disease and Its Risk Factors. Annals of Internal Medicine, 133: 635-646, 2000

[2] Ramsey et al. Unloader Braces for Medial Compartment Knee Osteoarthritis. Sports Health, 1: 416-426, 2009 [3] Pollo et al. Reduction of medial compartment loads with valgus bracing of the osteoarthritic knee. American Journal of Sports Medicine. 30: 414-421, 2002 [4] Komistek et al. An In Vivo Analysis of the Effectiveness of the Osteoarthritic Knee Brace During Heel-Strike of Gait. Journal of Arthroplasty, 14: 738-742, 1999 [5] Grood ES, Suntay WJ. A joint coordinate system for the clinical description of three-dimensional motions: application to the knee. Journal of Biomechanical Engineering. 105(2):136-144, 1983 [6] Anderst et al. Validation of three-dimensional modelbased tibio-femoral tracking during running. Medical Engineering and Physics 31:10–16, 207. Anderst A method to estimate in vivo dynamic articular surface interaction. Journal f Biomechanics, 36: 1291-1299, 2003 [8] Dennis et al. Evaluation of Off-Loading Braces for Treatment of Unicompartmental Knee Arthrosis. Journal of Arthroplasty, 21:2–8, 2006 [9] Haladik et al. Bracing improves clinical outcomes but does not affect the medial knee joint space in osteoarthritic patients during gait. Knee Surgery, Sports Traumatology, Arthroscopy. 22:2715–2720, 2014

Undergraduate Research at the Swanson School of Engineering

Ingenium 2018

Trigger Rate Monitoring for the ATLAS Experiment at CERN Daniel Zheng1, Andrew Todd Aukerman2, and Dr. Tae Min Hong2

Department of Electrical and Computer Engineering and 2 Department of Physics and Astronomy, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract ATLAS is a general-purpose detector at the Large Hadron Collider. It investigates a wide range of physics processes, probing previously unreachable phenomena such as the Higgs boson, the heavy strong sector, and possible new physics such as supersymmetry. To process the enormous readout of data from the detector, ATLAS uses an advanced trigger system to decide which events to record and which to ignore. “Xmon” is a trigger rate monitoring tool that runs in the ATLAS Control Room and ensures the optimal function of the detector by predicting trigger rates through a pileup regression. By comparing predicted rates with actual rates, ATLAS trigger shifters can more accurately spot errors in the data-taking process. Upgrades to the Xmon tool were performed to improve its efficiency and monitoring capabilities. Key Words: ATLAS, CERN, Trigger, Monitoring

1. Introduction The ATLAS experiment at European Center of Nuclear Research (CERN) is one of the general-purpose experiments operating at the Large Hadron Collider (LHC) in Geneva, Switzerland [1]. At design specifications, the LHC provides proton-proton (pp) collisions at a center of mass energy of √s=14 TeV and an instantaneous luminosity of L=1034 cm-2s-1 [1]. The ATLAS detector captures collision data at one of the four collision points located on the main LHC accelerator ring. Most of these collisions are uninteresting; the protons simply bounce off each other. However, sometimes two protons undergo ‘hard’ scattering, releasing energy to produce new particles which interact and scatter out from the collision point into the detector. The readout from ATLAS detector make up a massive data stream. Due to bandwidth constraints, it is infeasible to save all this data. ATLAS uses a Trigger and Data Acquisition (TDAQ) system to select interesting collision events to save for analysis. These data are then utilized by researchers all over the world to search for new physics phenomena.

The TDAQ system is comprised of two levels, the first being a hardware based, low-granularity Level-1 (L1) system with a design latency of 2.5μs [3]. This system constructs Regions-of-Interest, seeding the softwarebased algorithms used in the subsequent High-Level Trigger (HLT) system, which reconstructs the event with full detector read-out granularity. The L1 system is further divided into three subcomponents, the Calorimeter Trigger (L1Calo), the Muon Trigger (L1Muon) and the Central Trigger Processor (CTP). The L1 system reduces the 40 MHz collision rate to 100 kHz, and the HLT trigger reduces the rate further to 3-4 kHz [3]. The TDAQ system identifies certain signatures, including but not limited to, missing transverse momentum, lepton production, and high pT jets [2]. Flaws in the TDAQ system can invalidate data and cause costly setbacks, so it is of the utmost importance to accurately monitor the health of the system. Xmon is a trigger rate monitoring tool for the ATLAS trigger system that was originally created to monitor the L1 and HLT systems. This paper describes recent upgrades to and commissioning of the Xmon tool. The authors contributed additional functionality to monitor the newly introduced Level-1 Topological Trigger (L1Topo) system and various code optimizations to improve efficiency.

2. Methods 2.1 TDAQ Both the L1 and HLT triggers impose kinematic requirements to select a variety of physics signatures which indicate interesting events. Some such examples are electrons, energetic jets, muons, photons, and missing transverse momentum. These triggers serve a variety of physics analyses, and together constitute the physics menu. Each individual trigger contributes a specific rate as a function of time. A visual composition of this menu is shown below in Fig. 1. These rates are of immense importance, because they indicate the taking of data that forms the cornerstone of every physics analysis. As physicists are probing increasingly exotic aspects of the

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universe, larger and larger amounts of data are necessary to make statistically significant discoveries.

Figure 1. June 2017 physics trigger rates as a function of time. Note the signatures (e.g. electrons) each have a specific rate [2].

The nominal operation of the detector and trigger system is the priority of the trigger operations team, which includes dedicated specialists, experts, on-calls, and shifters. This dedicated team relies upon a variety of monitoring tools, which together, casts a wide net over the operation of the detector. In the realm of the TDAQ system, Xmon stands as one of the primary monitoring tools. Thus, its continued operation, commissioning, and updating is of great importance. 2.2 Xmon Rate Predictions As the LHC operates, the observed trigger rate falls with time as the beam luminosity decreases. This reduction in luminosity is unavoidable, as protons are lost during typical operation of the LHC. While the reduced trigger rate is generally expected, its specific rate as a function of time is complex and arbitrary. It is subject to a wide variety of factors, including beam conditions, trigger prescale, and detector operation. To formulate a meaningful metric and thus an accurate prediction of these rates, a new methodology was devised: luminosity scaling. The trigger rate can be broken down into a product of the instantaneous beam luminosity, with which the rate scales linearly, and the trigger cross section, a special measure of a trigger process, which is then parametrized by the bunch pileup.

Cross section is a type of probability expressed in units of area. The total interaction cross section for proton-proton collisions is about 80 millibarns, which relates loosely to the physical size of the proton. There exists a measure of cross section for specific pp interactions, such as those which result in certain hadronic observables or produce particles such as the Higgs boson. In the case of Higgs boson production, the cross section is about 10 picobarns, meaning only one Higgs boson is created in every 10 billion events. An observed cross section can be calculated directly from only two parameters, the trigger rate and the beam luminosity. The rate, R, has units of s-1 and the beam luminosity, L, has units of cm-2s-1. The experimental cross section forms some process ‘x’ is expressed as:

This cross section value is then plotted as a function of pile-up. Pile-up is a special kind of beam parameter because it can impose ‘fake’ rate, which makes it appear that the trigger cross section is changing. For certain triggers this relationship is flat with respect to pile-up, indicating no “fake rate effect”. However, for some which are sensitive to pile-up, this relationship is monotonically increasing, though not well defined. The cross sectional relationship is then stored and placed in a configuration file which the online run control software can access. When a new data-taking run begins, Xmon is automatically configured and launched. The first action performed is to access this configuration file, tabulate the list of triggers, and then query the ATLAS Information Server (IS) for these triggers’ rate data. Additionally, the live luminosity and pile-up is queried about 3 times per second, and available from the Online Luminosity Calculator server (OLC). From this information, the expected trigger rate can be calculated by solving Eqn. 1 for the rate. This prediction is then compared against the live rates and pushed back to the IS. Existing tools organize this information into easily understandable plots, which are then subject to 24/7 monitoring.

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3. Implementation The previous Xmon tool was separated into two different programs, one for L1 and one for HLT. Each of these adapters had to maintain separate connections to the servers which store the luminosity and rate data. A schematic of the Xmon system prior to the update is shown below in Fig. 2.

Creation of the new adapter involved attaining an in-depth understanding of the separate L1 and HLT code. Git for version control was adopted, and continuous integration tests were implemented to automatically compile and test new code. Many C++ functions and classes were reworked, and a new C++ structure was added for the L1Topo ratio functionality. The run control software, which automatically configures and launches every application which executes during a data-taking run, was also updated to configure the new adapter. A separate segment within the Trigger Rate Presenter (TRP) partition was created, which independently executes Xmon.

4. Results The merged adapter greatly simplifies future maintenance and reduced the size of the overall codebase by over 40%. Any updates to the adapter will now require about half the time and effort, as any new code must only be implemented once, instead of twice.

Figure 2. Block diagram showing the relationship between the IS servers (green), IS receiver objects (blue), and the Xmon L1 and HLT application processes (yellow). The redundancy in receiver objects is one of the motivations for merging the adapters.

Given that the rate predictions involve similar calculations for both the L1 and HLT system, the Xmon tool was rebuilt to merge L1 and HLT prediction adapters into a single binary. This required a generalization of member functions to handle both L1 and HLT predictions, which involve slightly different methods of prediction. For example, any HLT prediction must account for the prescale of both the HLT trigger and the L1 seed. Carried out simultaneously with the merging of the adapters, a new functionally was added to the L1 adapter. The L1Topo subsystem entered its final validation phase in the summer of 2017. This system imposes kinematic and angular constraints to trigger objects, reducing the rate while maintaining high selection efficiency. The ratio of these L1Topo trigger items to those without the constraints became of interest to the L1Topo group, and thus Xmon was updated to provide these ratios, which were expected to be constant with respect to time. This was instrumental in the validation of the L1Topo system and demonstrated that it was ready to be utilized in the Level-1 menu.

Through an extensive commissioning process, the new Xmon adapter was tested and validated. Both online and offline unit testing was performed, ensuring that the adapter would remain functional in all situations. L1Topo ratio functionality was also tested and validated. An example of this functionally is shown below in Fig. 3. The near perfect agreement between the predicted ratio and observed (online) ratio indicates that this aspect of the L1Topo subsystem is operating nominally.

Figure 3. A L1Topo muon trigger (red) and its associated predictions (green) [4].

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4.1 Error Detection Throughout its operation, Xmon has proved to be a vital tool in the daily operations of within the ATLAS experiment. One specific use case of Xmon is to help identify physical malfunctions of the detector subsystems. The trigger rate can serve as a type of “vital sign” of detector operation, and changes in the rate from the expected rate indicate a possible deviation in the detector parameter. An example involving HLT electron identification is presented in Fig 4. As Xmon continued to operate, more these use cases became clear. This example also demonstrates the importance of the real-time predictions. The deviation of 10% may have been undetectable by eye, however it is clearly highlighted in contrast to the predicted rate.

5. Discussion Some specific successes from 2017 involve the identification of issues or simple changes ranging from the LHC to specific mistakes in software configuration. For example, to push to higher luminosities, the LHC changed the bunch configuration, which describes the pattern of protons bunches in the beam pipe. This affected only the muon triggers, which have special algorithms which are sensitive to bunch spacing. This effect was demonstrated in the rate predictions, and Xmon showed that while the rate did change, it changed in the expected way. Additionally, it has been shown that Xmon works synergistically with other monitoring tools. During a run, an HLT configuration mistake caused massive errors in RoI builder prefetching, which is an important part in the execution of the HLT system. Every one of the rates in the HLT predictions tab showed a decrease of 50%. This discrepancy was quickly noticed, and cross-checked with errors emerging from other tools to quickly resolve the problem.

6. Conclusion The rewritten Xmon adapter provides increased efficiency, a cleaner codebase, and additional functionality. It was specifically instrumental in the validation process of the L1Topo subsystem and is now being used to monitor live trigger rates and helps to ensure smooth data-taking for the ATLAS experiment.

Figure 4. HLT tight electron at 26 GeV (HLT_e26_lhtight) deviated from the predicted value by about 10%. It was discovered that the voltage of a detector subsystem was too low [4].

However there is still further room for improvement of the Xmon tool. Currently, Xmon performs a linear regression (first degree polynomial fit). While this works very well for many triggers, some, like L1_XE60, are more complex and difficult to fit. Future work will involve adding higher-order polynomial fits to Xmon to better fit these triggers’ cross sections, as well as building a web interface to make the Xmon tool more accessible. Additionally, more detailed information will be made available, including the run conditions associated with the current predictions to better pinpoint sources of error. Undoubtedly, the work put forth into simplifying, expanding, and streamlining Xmon will continue to be invaluable for the ATLAS experiment.

Undergraduate Research at the Swanson School of Engineering

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Acknowledgements Iâ&#x20AC;&#x2122;d like to thank my advisor, Dr. Tae Min Hong, as well as my mentor Andrew Aukerman for their guidance. I would also like to thank the Swanson School for funding my work.

References [1] ATLAS Collaboration, The ATLAS Experiment at the CERN Large Hadron Collider, 2008 JINST 3 S08003

[2] ATLAS Collaboration. Performance of the ATLAS Trigger System in 2015. 2016. arXiv:1611.09661 [3] ATLAS TDAQ Collaboration, The ATLAS Data Acquisition and High Level Trigger system, 2016 JINST 11 P06008 [4] ATLAS Collaboration. Trigger Operations Public Results.

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Ingenium: Undergraduate Research at the Swanson School of Engineering, 2018

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