2024 Ingenium - Journal of Undergraduate Research

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Ingenium: Undergraduate Research at the Swanson School of Engineering, 2024

151 Benedum Hall 3700 O’Hara Street Pittsburgh, PA 15261

Undergraduate Research at the Swanson School of Engineering

2 0 2 4 The University of Pittsburgh is an affirmative action, equal opportunity institution. Published in cooperation with the Office of University Communications. 113032-0321

University of Pittsburgh Swanson School of Engineering Undergraduate Research Benedum Hall, 3700 O’Hara Street, Pittsburgh, PA 15261 USA Spring 2024

Hematoxylin and Eosin staining (H&E) of Adipose tissue sample: Adipose + Palmitic acid + Urolithin A (See page 60 by Celeste Lintz).

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.

Table of Contents Message from Dr. Vorp

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Message from the Co-Editors-in-Chief

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Graduate Student Review Board – Ingenium 2024

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 HER2+ breast cancer cells induce expression of TumorAssociated Macrophage markers in Monocyte-Derived Macrophages in-vitro Charles W. Blackledge1, Ioannis Zervantonakis1,2 1 Tumor Microenvironment Engineering Laboratory, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 UPMC Hillman Cancer Center, Pittsburgh, PA

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 Implementation of a capacitance-to-digital converter using an open-source tool flow Ryan Caginalp1,2, In Hee Lee1,2 1 PITT Circuit Lab 2 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 14 u Computational Evaluation of Spinel MnFe2O 4 Adsorbent to Remove Heavy Metal from Wastewater Zhengkai Chen1, Ying Fang1, Boyang Li1, Guofeng Wang1 1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 18  Effects of estrogen on platelet activation relative to cardiovascular health Adriana Cocco1,2, Shivbaskar Rajesh1,2, James F. Antaki4, Harvey S. Borovetz1,2,3 1 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 3 Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 4 Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 25

 First experiences integrating functional near-infrared

spectroscopy brain imaging and virtual reality Kasey Forsythe1,2, Hendrik Santosa2,3, Theodore Huppert 2,4 1 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Multimodal Methods for Noninvasive Neuroimaging Lab 3 Department of Radiology, University of Pittsburgh, Pittsburgh, PA 4 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 30

 Comparison of prosthetic knee joint types in relation to

slip risk Elizabeth Ibata-Arens1, April Chambers1, Goeran Fiedler2 1 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA 35 u Cell fate analysis of ovarian cancer in response to chemotherapeutic treatment Tyler Johnston1,2, Wayne Stallaert 2 1 Department of Bioengineering, University of Pittsburgh, PA, USA 2 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA

 Triggerable dissolution of environmentally benign plastic in marine environments Samuel M. Landon1, Susan K. Fullerton- Shirey1,2, Eric J. Beckman1,3 1 Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 3 Mascaro Center for Sustainable Innovation, University of Pittsburgh, Pittsburgh, PA 45 u Materials for Solar Energy Technology: Cation Distribution and Behaviors of Spinel Oxide MnFe2O 4 Hannah Levine1, Ying Fang1, Boyang Li1, Guofeng Wang1 1 Department of Mechanical Engineering and Materials Science University of Pittsburgh, Pittsburgh, PA 49  Determining the effects of amino acid composition on the activity of laccase-mimicking bionanozymes Zhehao Li1, Meng Wang 2* 1 Department of Mechanical and Material Science, University of Pittsburgh 2 Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 55  Generation of Fat-Cartilage Microphysiological Model as a New Tool to Study Obesity-Associated Osteoarthritis Celeste E. Lintz1,2, Katelyn E. Lipa1,2, Meagan J. Makarczyk1,2, Sophie E. Hines1,2, Hang Lin1,2 1 Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA 2 Department of Orthopaedic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 58

Category Definitions

u Computational Research—using computational techniques to address a scientific question  Device Design—focusing on the development of a product or device

 Experimental Research—using laboratory methods to achieve a novel overarching experimental aim

 Methods—developing new techniques and tools for research and design  Other

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Ingenium 2024

 Cognitive Perseveration Is Not Associated with TrainingInduced Improvements in Motor Perseveration In Older Adults Shaoyi Liu1,2, Shuqi Liu1,3, Gelsy Torres-Oviedo1,3 1 Sensorimotor Learning Laboratory, Department of Bioengineering, University of Pittsburgh, PA, USA 2 Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 3 Center for the Neural Basis of Cognition, Pittsburgh, PA 63  Density variations in large Binder Jet 3D Printed Inconel 625 Parts for Mechanical Testing Sampling Jose Morales1, Pierangeli Rodriguez De Vecchis1, Zachary Harris1, Markus Chmielus1 1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 68  The Development of PLCL and Polyurethane Small Diameter Vascular Grafts of Varying Compliance Trin R. Murphy1, David R. Maestas1, Jr., Katarina Martinet1, William R. Wagner2, Sang-Ho Ye2, Jonathan P. Vande Geest1,2,3 1 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 3 Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 73 u Anticipatory licking and dopamine release in the monkey striatum Raymond Murray1, Jiwon Choi1, Usamma Amjad1, Helen Schwerdt1 1 Department of Bioengineering, University of Pittsburgh, PA, USA 77  Effects of Commensal Respiratory Bacteria on The Persistence of Influenza A Virus in Droplets Daniel S. Nolan1, Shannon C. David2, Tamar Kohn2 1 Environmental Chemistry Laboratory, School of Architecture, Civil and Environmental Engineering EPFL, Lausanne, Vaud, Switzerland 2 Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 82

u System-level modeling & analysis of Pennsylvania plastic waste: Implications for a circular economy Caymus R. Ruffner1, Vikas Khanna1 1 Department of Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, PA

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u Modeling Materials and Configurations of Radioisotope Thermoelectric Generator (RTG) Heat Sources Ganesh Selvakumar1, Matthew M. Barry1 1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 97

 Cyber-Informed Engineering in the Context of Prioritizing U.S.

Grid Security Isabella Hsia1, Kameren Jouhal2, Shanker Pillai2, Philippe Van de Putte2, Evan Wang 3, Daniel Cole 4, Brandon Grainger5,6 1 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Computer Science, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 3 College of Business Administration, University of Pittsburgh, Pittsburgh, PA 4 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 5 Energy GRID Institute, University of Pittsburgh, Pittsburgh, PA 6 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 102  Characterization of High-Temperature Magnetic Stability for an Extreme-Temperature Inductor Application Zisong Wang1, Tyler William Paplham1, Lauren Wewer1, Paul Richard Ohodnicki1 1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 107  Magnetic Properties of Conventionally and Flash Annealed Iron-Nickel Magnetic Alloys Aron Wiener1, Lauren Wewer1, Tyler Paphlam1, Paul Ohodnicki1 1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 111

 Binder Jet Printing and Permeability Testing of Porous Metallic Shapes For Filtration Applications Steven Panico1, Pierangeli Rodriguez De Vecchis1, Markus Chmielus1 1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 87

Category Definitions

u Computational Research—using computational techniques to address a scientific question  Device Design—focusing on the development of a product or device

 Experimental Research—using laboratory methods to achieve a novel overarching experimental aim

 Methods—developing new techniques and tools for research and design  Other

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A Message from the Senior Associate Dean for Research & Facilities Although the term is rarely used in the modern vernacular, the concept of Ingenium is as relevant today as it was thousands of years ago. Ingenium, which references innate talent or natural ability, hearkens to the ancient, relentless quest for understanding which drives innovation and discovery. The undergraduate student researchers whose work is featured in the following pages embody the spirit of Ingenium with their ingenuity, curiosity, and insight as they explore the boundaries of our knowledge. We celebrate the timelessness of Ingenium and its ability to challenge the expanse of human potential through the naming of this publication.

David A. Vorp, Ph.D.

On behalf of the Swanson School of Engineering and Interim U.S. Steel Dean of Engineering Sanjeev G. Shroff, I proudly present the tenth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering, a compilation of articles representing the achievements of our exceptional undergraduate students and their 2023 summer research projects.

As with each year and each edition of Ingenium, one thing remains the same—the notable and impressive academic and professional growth and development in our outstanding undergraduate students when given the opportunity to engage in scientific research. As always, our students took their skills, knowledge, resources, and information that they learned in their course work and applied it in a thoughtful way outside of the classroom. These students, the future of both our institution and our world, will go on to become engineers, scientists, academics, physicians, or whatever else they set out to accomplish. They will, without a doubt, make incredibly significant impacts in the fields of technology, medicine, travel, space, and communication, just to mention a few.

The student authors of the articles in this issue of Ingenium studied mostly under the guidance of faculty mentors in the Swanson School. At the conclusion of the summer research program, students were asked to submit abstracts summarizing the results of their research. The abstracts were reviewed by the Graduate Student Review Board (GSRB), and the authors of the highest-ranking abstracts were invited to submit full manuscripts for peer review by the GSRB for inclusion in this edition of Ingenium. Therefore, Ingenium serves as more than a record of our undergraduate student experience in research; it is also a practical experience for them in scientific writing and in the author’s perspective of the peer-review process. Additionally, it provides graduate students with an opportunity to experience the editorial review process and the reviewer’s perspective of the peer-review process.

I would like to acknowledge the hard work and dedication of the co-editors-in-chief of this issue of Ingenium, Pierangeli Rodriguez De Vecchis and Alireza Asadbeygi, as well as the design team at AlphaGraphics and the team in the Office of University Communications and Marketing. This issue would not have been possible without the hard work of the graduate student volunteers who constitute the GSRB and who are listed by name in this issue. It is also altogether fitting to thank the faculty mentors and other coauthors of the reports included in this issue.

I hope that you enjoy reading this edition of Ingenium and that the many talents of our students inspire the engineers of the future.

Hail to Pitt!

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

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Message from the Co-Editors-in-Chief Greetings!

We are excited to present the tenth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering (SSOE). Ingenium introduces undergraduate students to the scientific peer-review process, offering them the opportunity to enhance their research communication skills through the submission of written manuscripts. These manuscripts undergo review by Swanson School of Engineering (SSOE) graduate students, who volunteer to give comprehensive feedback. This process is mutually beneficial, allowing undergraduates to appreciate the reviewer’s viewpoint and gain insights into new subjects, while graduate students have the chance to impart their knowledge.

Alireza Asadbeygi

Moreover, Ingenium enables undergraduate students to engage deeply with established research methodologies through direct collaboration with PhD students and the guidance of faculty mentors. This invaluable experience prepares them for future professional endeavors, whether they aim to pursue further studies in graduate school or embark on a career in industry.

This volume features 22 articles from undergraduate students at the University of Pittsburgh’s SSOE. This year’s articles show how the talents and hard work of these students provide new perspectives in relevant scientific topics being developed today. This year’s edition of Ingenium displays a sample of the diverse research that can be found in SSOE labs, and the opportunities undergraduate students are exposed to. We are so proud of all participating students for their creativity, critical thinking, hard work, and commitment to their research. We hope all authors, mentors, and reviewers share our excitement and pride and that you enjoy all articles as much as we did!

Pierangeli Rodriguez De Vecchis

We would like to thank everyone in the production team of this year’s Ingenium volume. We deeply thank Dr. David Vorp, Senior Associate Dean for Research and Facilities, for his vision and continued commitment to this publication. We are also extremely grateful to Emily VonderPorten, for her advice, guidance, and continued support throughout the entire year. We also deeply appreciate all the mentors who guided the students’ research and the graduate students on the GSRB, for dedicating so much of their not-so-free time and sharing their knowledge to advise the authors. Finally, we would like to thank everyone in the Office of University Communications and Marketing and the AlphaGraphics team, especially Michelle Bloom and John Kasunic for their amazing work with the production and design of this Ingenium edition.

We have learned so much from everyone involved in this year’s Ingenium edition, and we are honored to have served as Co-Editors-in-Chief. It was truly a most rewarding experience to continue this Pitt SSOE tradition and to be part of a remarkable research community that invests in students and their academic and personal development. We hope that as you read this year’s articles, you let yourself be submerged in the wonderful research developments, as well as the passion and hard work shown by the authors.

Congratulations to the authors and happy reading!

Alireza Asadbeygi Co-Editor-In-Chief

Pierangeli Rodriguez De Vecchis Co-Editor-In-Chief

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Graduate Student Review Board – Ingenium 2024 *Co-Editors-in-Chief Name...................................................................................................... Department Gilgal Ansah............................................................................................... Department of Bioengineering Lizzy Bentley.............................................................................................. Department of Bioengineering Tia Calabrese............................................................................................. Department of Bioengineering Pooja Chawla............................................................................................. Department of Bioengineering Isabelle Chickanosky................................................................................ Department of Bioengineering Faith Dias................................................................................................... Department of Bioengineering Lily Farmerie.............................................................................................. Department of Bioengineering Adam Forrest............................................................................................. Department of Bioengineering Abigail Gondringer.................................................................................... Department of Bioengineering Anne Gormaley......................................................................................... Department of Bioengineering Pete Gueldner........................................................................................... Department of Bioengineering Dorota Jazwinska...................................................................................... Department of Bioengineering Lucy Liang.................................................................................................. Department of Bioengineering Shuqi Liu.................................................................................................... Department of Bioengineering Jennifer Mak.............................................................................................. Department of Bioengineering Ande Marini............................................................................................... Department of Bioengineering Katarina Martinet...................................................................................... Department of Bioengineering Alireza Mohammadzadeh........................................................................ Department of Bioengineering Tracey Moyston......................................................................................... Department of Bioengineering Sharada Narayanan.................................................................................. Department of Bioengineering Temitope Obisesan................................................................................... Department of Bioengineering Danielle Pitlor............................................................................................ Department of Bioengineering May Pwint.................................................................................................. Department of Bioengineering Adiya Rakymzhan...................................................................................... Department of Bioengineering Andrea Sajewski........................................................................................ Department of Bioengineering Rinu Sebastian........................................................................................... Department of Bioengineering Bryce Aniszewski....................................................................................... Department of Civil and Environmental Engineering Charles Donnelly....................................................................................... Department of Civil and Environmental Engineering Kathryn Kennebeck.................................................................................. Department of Civil and Environmental Engineering Trevor Neece............................................................................................. Department of Civil and Environmental Engineering Rodrigo Sosa.............................................................................................. Department of Civil and Environmental Engineering Isaiah Spencer-Williams........................................................................... Department of Civil and Environmental Engineering Arundhati Tewari...................................................................................... Department of Civil and Environmental Engineering Yihan Song................................................................................................. Department of Chemical and Petroleum Engineering Zeineb Bouzid............................................................................................ Department of Electrical and Computer Engineering Mahmoud Ashraf Mohamed................................................................... Department of Industrial Engineering Alireza Asadbeygi*.................................................................................... Department of Mechanical Engineering and Materials Science Mahsa Beyk Khorasani............................................................................. Department of Mechanical Engineering and Materials Science Ruikang Ding............................................................................................. Department of Mechanical Engineering and Materials Science Joshua Frantz............................................................................................. Department of Mechanical Engineering and Materials Science Qifeng Hu................................................................................................... Department of Mechanical Engineering and Materials Science Anthony Immormino................................................................................ Department of Mechanical Engineering and Materials Science Stephanie Liu............................................................................................. Department of Mechanical Engineering and Materials Science Suraj Mullurkara....................................................................................... Department of Mechanical Engineering and Materials Science Pierangeli Rodriguez De Vecchis*........................................................... Department of Mechanical Engineering and Materials Science

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HER2+ breast cancer cells induce expression of TumorAssociated Macrophage markers in Monocyte-Derived Macrophages in-vitro Charles W. Blackledge1, Ioannis Zervantonakis1,2 Tumor Microenvironment Engineering Laboratory, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 UPMC Hillman Cancer Center, Pittsburgh, PA 1

Will Blackledge is a senior bioengineering student from Spring City, PA. His research interests include cancer biology, immunology, and fluid dynamics. After graduation, he plans to pursue a Ph.D. in Biomedical Engineering.

Will Blackledge

Ioannis Zervantonakis

Ioannis Zervantonakis was born in Athens (Greece), completed his BS in Mechanical Engineering, Ph.D. in Mechanical/Biological Engineering and postdoc fellowship in Cancer Biology.

His lab is interested in developing microfluidics and systems biologybased approaches to understand how cancer cells interact with their environment.

Significance Statement

Tumor-Associated Macrophages (TAMs) have been shown to significantly influence the formation of an immunosuppressive and tumor-promoting microenvironment. This study identifies conditions for TAM generation using conditioned medium from HER2+ breast cancer cells, which is a critical first step in the development of models to test novel TAM-related immunotherapeutic treatments.

Category: Experimental Research

Keywords: Breast Cancer, Macrophage, Tumor Microenvironment, Macrophage-targeted Immunotherapy Abbreviations: Tumor-Associated Macrophage (TAM), Tumor-Conditioned Medium (TCM), MonocyteDerived Macrophage (MDM), Human Epidermal growth factor Receptor 2 (HER2)

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ABSTRACT Immunotherapy has shown great promise in recent breast cancer treatment studies, lacking the harsh side effects of common chemotherapy treatments or the limited scope of patient-specific treatments. A major target proposed for new treatments is the TumorAssociated Macrophage (TAM), which has been shown to increase tumor-promoting properties in cancer patients. However, a lack of knowledge exists on how different HER2+ breast cancer cells can promote TAM formation. In this study, we present an optimized protocol for monocyte-derived TAM generation after exposure to HER2+ breast cancer secreted factors through the quantification of two TAM markers: CD206 and CD204. We tested numerous experimental variables, including culture duration, concentrations of Macrophage Colony Stimulating Factor (M-CSF) and different HER2+ breast cancer cell lines. Our results demonstrate high TAM formation levels after exposure to secreted factors from the HER2+ breast cancer cell line HCC1954. The results from this study will aid in the future studies of identifying regulators of tumormacrophage paracrine signaling mechanisms.

1. INTRODUCTION Breast cancer in females is one of the most commonly diagnosed forms of cancer, with the National Cancer Institute predicting 15% of all new cancer cases in 2023 to be breast cancer [1]. The four main molecular subtypes of breast cancer: Luminal A, Luminal B, Basal, and HER2+ contribute to both the large number of new cases and the complicated nature of developing effective therapies [2]. Chemotherapy, while used extensively in current cancer treatments, suffers from severe side effects. Additionally, subtype-specific treatments such as HER2-targeted therapies are only effective against a narrow range of cancer cases [2].

Immunotherapy is emerging as a new approach to cancer treatment because of the inherent power of the human immune system and the presence of many therapeutic targets [3]. Thus, much time has been invested into the study of immunotherapy to develop new treatment protocols.

One such therapeutic target is the macrophage: a defining cell of the innate immune system.

Macrophages are able to polarize into a wide range of phenotypes that can be tumor-suppressing, tumorpromoting, or a combination of both [4]. When exposed to the dynamic Tumor Microenvironment (TME) consisting of cancer cells, immune cells, and signaling molecules, Tumor-Associated Macrophages (TAMs) can form. TAMs are identified by antibody markers such as CD206, CD163, and CD204 [5], and are well documented to promote tumor growth, motility, invasiveness, and decrease long-term patient outlook [6]. Prior studies have focused on basal-like (Triple Negative) breast cancer in TAM formation protocols [7], however limited

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knowledge exists regarding the effects of different HER2+ breast cancer models and experimental condition on the reprogrammability of macrophages into TAMs. In this study, we present a systematic experimental protocol and analysis of TAM marker expression in monocyte-derived macrophages with a focus on studying the role of different breast cancer cell lines, growth factor concentrations, and cell culture duration on TAM reprogramming.

2. METHODS A schematic illustrating the general protocols utilized in this study is shown in Figure 1.

2.2.1: Cell Culture Isolated monocytes were counted, resuspended at 1E6 cells/mL in RPMI 1640/10% FBS cell media, and seeded in a 96-well plate at a density of 50k cells/well.

Next, the monocytes were allowed to differentiate by exposure to 50 μL of 25 ng/mL Macrophage Colony Stimulating Factor (M-CSF) in each well. The monocytes were exposed to M-CSF for 2 days in an incubator to form monocyte-derived macrophages (MDMs). At the conclusion of the differentiation period, the M-CSF was removed and the MDMs were exposed to cell media (negative control), IL4/IL13 cytokines (positive control) or HER2+ Tumor Conditioned Medium (TCM) from six cell lines (HCC1954, HCC1569, SKBR3, HCC1419, EFM192, and BT474) to evaluate their TAM generation capacity. 50 μL of cytokines/TCM was added to each well, diluted in a 1:1 ratio with 50 μL RPMI 1640/10% FBS cell media. The cells were exposed to cytokines/TCM in an incubator for a period of 5 days. Environmental cell culture conditions were maintained at 37˚C and 5% CO2.

2.2.2: Image Analysis

Figure 1: Schematic of experimental protocols. First, human monocytes are exposed to M-CSF. After 2 days, M-CSF is removed, and TCM is added for 3 days. PFA is then used to fix cells, and the addition of primary/secondary antibodies will stain cells for the presence of TAM markers.

To identify optimal conditions, factors highlighted in red will be modified throughout this study. Selecting TCM will occur during Experiment 1, and optimizing M-CSF concentration, M-CSF duration, and TCM duration will occur during Experiment 2.

2.1: Human Monocyte Isolation Human pan-monocytes were isolated from healthy donor blood utilizing a Miltenyi Biotec® Pan Monocyte Isolation Kit according to manufacturer specifications.

Monocytes not immediately utilized for cell culture were frozen for future use in 90% FBS/10% DMSO.

2.2: Experiment 1: Identifying Cancer Cell Lines that promote a TAM state in macrophages In this experiment, isolated human monocytes were exposed to harvested Tumor Conditioned Medium (TCM) from six HER2+ breast cancer cell lines (described below) in order to identify two cancer cell lines that generate TAMs for further study and protocol optimization.

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At the conclusion of the 5-day exposure to cytokines/ TCM, the cells were fixed in 4% paraformaldehyde (PFA). A standard immunofluorescent staining protocol followed, with a one-hour incubation in Intercept® Blocking Buffer (Li-Cor) at 4˚C. Cells were stained for the CD206 antibody (Purified anti-human CD206 (MMR) Antibody, BioLegend) overnight. Following 3 sequential washes of the cells in Phosphate-Buffered Saline (PBS), a Hoechst nucleus stain (DAPI) and secondary antibodies (Cy5, Goat anti-Mouse IgG, AlexaFluorTM 647, ThermoFisher Scientific) were added for one hour. After a final sequence of 3 PBS washes, 16 images per well of the antibody-stained cells were captured utilizing a Cytell® Cell Imaging System.

2.2.3: Data Processing A CellProfiler pipeline was utilized to detect each cell, segment cell boundaries, and measure the mean intensity of each TAM marker within each cell. Violin plots displaying the mean marker intensity of each cell were created with GraphPad Prism 9, and data was statistically analyzed using the built-in One-Way ANOVA and Tukey’s multiple comparison statistical tests in GraphPad Prism 9. A significance value of α=0.05 was used to determine statistical significance. Multiple experimental and technical replicate experiments were performed to evaluate repeatability and statistical significance of our results.

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2.3: Experiment 2: Identifying the effects of optimal experimental conditions on TAM marker expression This experimental methodology is the result of several protocols conducted to maximize TAM generation by utilizing the two cancer cell lines identified in Section 2.2. M-CSF concentrations between 5 ng/mL and 50 ng/mL, duration of exposure to M-CSF between 2 days and 5 days, and duration of exposure to TCM between 3 days and 7 days were all optimized. Due to space limitations, most of these experiments will not be discussed, however an overview of all tested conditions is present in Table 1.

Experiment Number

M-CSF Concentration (ng/mL)

M-CSF Culture Duration (days)

TCM Culture Duration (days)

1

5

2

3

2

5

2

7

3

50

5

3

4

5 & 25

2

5

5

5

2

3

6

5 & 25

2

3

3. RESULTS 3.1: Effects of six different breast cancer cell lines on TAM reprogramming After a 2-day exposure to 25 ng/mL M-CSF and a 5-day exposure to TCM, a significant increase in CD206 TAM marker intensity was observed from MDMs cultured with HCC1954 and HCC1569 TCM compared to the negative control condition (Figure 2). BT474 TCM yielded a nonsignificant increase and all other cell lines yielded a significant decrease in CD206 TAM marker intensity compared to the control.

3.2: Optimized expression of different TAM markers in macrophages exposed to two HER2+ breast cancer models

Table 1: Overview of all experiments conducted in this study, highlighting variability in tested conditions for M-CSF concentration and culture durations.

2.3.1: Cell Culture The optimized protocol utilizes the same monocyte isolation techniques and monocyte seeding protocol (50k cells/well) as Experiment 1 (Section 2.2.1) described above. Key differences include the culture of monocytes with 50 μL of 5 ng/mL M-CSF for 2 days (previously 50 μL of 25 ng/mL M-CSF for 2 days), TCM culture for 3 days (previously 5 days), and the utilization of only HCC1954 and BT474 TCM.

2.3.2: Image Analysis and Data Processing Image Analysis and Data Processing steps remain the same as previously described in Sections 2.2.2 and 2.2.3, with the exception of an additional stain for CD204 TAM markers (Purified anti-human CD204 (MMR) Antibody, BioLegend) in addition to the CD206 TAM markers.

Figure 2: Mean CD206 intensity per cell after 2-day exposure to 25ng/mL M-CSF and 5-day exposure to cell media (no TCM, negative control), IL4/IL13 (positive control) or TCM. HCC1954 and BT474 CM were selected for further protocol optimization.

We also evaluated CD206 TAM expression following optimization of the MDM generation protocol (optimal conditions included exposure to 5 ng/ml M-CSF for 2 days and TCM for 3 days). Consistent with our results in Figure 2, we found higher levels of CD206 TAM marker intensity for the HCC1954 TCM compared to both BT474 TCM and the negative control condition (Figure 3A).

MDMs were also stained for an additional TAM marker, CD204, to further evaluate TAM reprogramming. A significant increase in CD204 expression is shown in the HCC1954 condition, while the BT474 condition was similar compared to the control.

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Figure 3: A) Mean CD206 intensity per cell after protocol optimization (2-day exposure to 5ng/mL M-CSF and 3-day exposure to TCM). A significant increase in CD206 is shown after exposure to HCC1954 TCM. B) Mean CD204 intensity per cell.

Images of the TAMs in the optimized conditions were acquired using a fluorescent light microscope (Figure 4). Significantly more CD206 expression, appearing in magenta (Cy5), can be observed in the HCC1954 condition compared to the negative control and the BT474 condition. Appearing in cyan (DAPI) are cell nuclei.

Figure 4: Images of TAMs taken on a Cytell® microscope at a 10x magnification. A significant increase in CD206 marker intensity (Cy5) is shown after exposure to HCC1954 CM, matching quantitative data from Figure 3.4.

4. DISCUSSION Our results have shown that a significant increase in TAM markers on monocyte-derived macrophages in-vitro occurs when macrophages are exposed to HCC1954 Tumor Conditioned Medium. A secreted factor may be present within the TCM which enhances TAM development, which could explain the cell linedependent variability of TAM formation and the low intensity levels of TAM markers when exposed to BT474 TCM. HCC1954 and BT474 were chosen to be included in the study from the six originally tested cell lines in order to study a cell line with a high potential for TAM generation (HCC1954) and a low potential for TAM generation (BT474). Additionally, an extensive literature review of TAM generation in-vitro with Triple-Negative breast cancer was conducted to obtain the range of M-CSF concentrations and culture durations used within this study [8]. Future experiments could further modify experimental conditions, including refreshing TCM throughout the culture period, to obtain stronger TAM generation for any cell line.

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5. CONCLUSIONS

REFERENCES

This study has shown that a significant increase in TAM markers on monocyte-derived macrophages can occur when macrophages are subjected to an optimized experimental protocol: a 2-day culture with 5 ng/mL M-CSF followed by a 3-day culture with HCC1954 Tumor Conditioned Medium. These results could signify an increased level of cancer cell-induced TAM formation in breast tumors mimicking the secretory profile of HCC1954, potentially leading to more aggressive tumors and worse clinical outcomes. The creation of an optimized experimental protocol to generate TAMs fills a knowledge gap that is critical for discovering tumor-macrophage crosstalk mechanisms and novel macrophage-targeted immunotherapies. Future studies will be directed towards measuring the infiltrative ability of macrophages in vitro when exposed to HCC1954 and BT474, and identifying potential signaling pathways in macrophages that regulate TAM reprogramming.

[1] National Cancer Institute, “Cancer Stat Facts: Breast Cancer,” National Cancer Institute. 2023. [Online].

Available: https://seer.cancer.gov/statfacts/html/ breast.html [Accessed Dec. 28, 2023].

6. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering, the Office of the Provost, and the Department of Bioengineering at the University of Pittsburgh. Special thanks to Ruxuan Li, Matthew Poskus, Youngbin Cho, and other members of the Tumor Microenvironment Engineering Lab for their continued support and guidance.

[2] H. T. Amer, U. Stein, and H. M. El Tayebi, “The Monocyte, a Maestro in the Tumor Microenvironment (TME) of Breast Cancer,” Cancers (Basel), vol. 14, no. 21, Nov 7, 2022, doi: 10.3390/ cancers14215460.

[3] G. L. Szeto and S. D. Finley, “Integrative Approaches to Cancer Immunotherapy,” Trends Cancer, vol.

5, no. 7, pp. 400-410, Jul 2019, doi: 10.1016/j. trecan.2019.05.010.

[4] R. Noy and J. W. Pollard, “Tumor-associated macrophages: from mechanisms to therapy,” Immunity, vol. 41, no. 1, pp. 49-61, Jul 17, 2014, doi: 10.1016/j.immuni.2014.06.010.

[5] S. D. Jayasingam, M. Citartan, T. H. Thang, A. A. Mat Zin, K. C. Ang, and E. S. Ch’ng, “Evaluating the Polarization of Tumor-Associated Macrophages into M1 and M2 Phenotypes in Human Cancer Tissue: Technicalities and Challenges in Routine Clinical Practice,” Front Oncol, vol. 9, p. 1512, 2019, doi: 10.3389/fonc.2019.01512.

[6] X. Huang, J. Cao, and X. Zu, “Tumor-associated macrophages: An important player in breast cancer progression,” Thorac Cancer, vol. 13, no. 3, pp. 269276, Feb 2022, doi: 10.1111/1759-7714.14268.

[7] X. Qiu, T. Zhao, R. Luo, R. Qiu, and Z. Li, “TumorAssociated Macrophages: Key Players in TripleNegative Breast Cancer,” Front Oncol, vol. 12, p.

772615, 2022, doi: 10.3389/fonc.2022.772615.

[8] B. Benner et al., “Generation of monocyte-derived tumor-associated macrophages using tumorconditioned media provides a novel method to study tumor-associated macrophages in vitro,” J Immunother Cancer, vol. 7, no. 1, p. 140, May 28, 2019, doi: 10.1186/s40425-019-0622-0.

11

Implementation of a capacitance-to-digital converter using an opensource tool flow Ryan Caginalp1,2, Inee Lee1,2 PITT Circuit Lab Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 1 2

Ryan Caginalp is an senior computer engineering undergraduate student at the University of Pittsburgh. He is mainly interested in doing digital integrated circuit design, and hopes to continue this interest by pursuing a PhD after graduation.

Ryan Caginalp

In hee Lee received his B.S. and M.S. degrees in electrical and electronic engineering from Yonsei University, Seoul, Korea, in 2006 and 2008, respectively, and a Ph.D. degree in electrical engineering from the University of Michigan, Ann Arbor, MI, USA, in 2014. From 2015 to Inhee Lee 2019, he was with the University of Michigan as an assistant research scientist. In 2019, he joined University of Pittsburgh as an assistant professor. His research includes energyefficient application specific integrated circuit design and miniature system development.

Significance Statement

Traditionally, chip design has required the use of expensive CAD tools. However, in recent years, similar open-source tools have been developed. This paper demonstrates that one can use open-source tools to create designs with comparable features to those done with professional tools by implementing an existing capacitance-to-digital converter (CDC).

Category: Device Design

Keywords: Open-Source, Sensor Design, Digital VLSI, CAD Abbreviations: Capacitance-to-Digital Converter (CDC), Design Rule Check (DRC), Layout versus Schematic (LVS), Hardware Description Language (HDL), Integrated Circuit (IC)

12

Ingenium 2024

ABSTRACT In this paper, we implement an existing CDC design using the openROAD toolflow. While the CDC is an analog circuit, the only analog component was a transmission gate, which was manually designed using the Magic tool. With this, the design could easily be integrated into the openROAD toolflow. This toolflow consists of many individual processes that are used to implement a chip, including synthesis, which is used to translate a high-level description of a design into a list of individual gates, floorplanning, which is used to plan out the structure of the chip, placement, which is used to place individual gates, and routing, which is used to form the connections between gates. After the chip design was completed, design rule checks (DRC) were run to ensure that the chip was manufacturable, and layout versus schematic (LVS) was run to ensure that the designed chip had the same functionality as planned. Finally, simulations were conducted using ngSPICE to verify circuit functionality, and, when adjusted for the different manufacturing processes, we found that the resulting design had comparable resolution, time, power, and energy consumption.

1. INTRODUCTION Tools needed for designing integrated circuits (IC) are prohibitively expensive for those not involved with industry or a university. For example, Cadence OrCAD, which is a tool primarily used for layout and simulation, costs over fifteen thousand dollars a year [1]. The openROAD toolkit helps solves this barrier of entry issue [2] [3]. It is an open-source toolflow that transforms a Hardware Description Language (HDL) file, which describes digital circuit functionality using basic programming statements, into a manufacturable, graphic data system (GDS) file, which contains information about the size and positioning of layers used to manufacture a chip. The openROAD toolflow can be used for analog IC designs as well. For example, openFaSoC uses openROAD to create several different types of analog ICs based on high-level user input, such as a temperature sensor that can be adjusted based on a user specified temperature range and power, as well as other parameters [4]. Among analog ICs, capacitanceto-digital converters (CDCs) have important uses for emerging Internet-of-Things applications as readout circuits of pressure, position, chemical detection, material composition, and flow sensors [5] [6]. In this work, we develop an IC design process of a CDC using the openROAD toolflow. This CDC also mostly consists of digital components, meaning that it can be easily integrated into the openROAD flow. Because the CDC has the dual properties of being implemented using an open-source toolflow and mostly consisting of digital components, it can easily be modified by those who may not have much knowledge about analog design, thus enabling them to create their own customized capacitance-to-digital converters for a needed

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application.

2.2. CDC Chip Design Process

2. METHODS

Figure 2 shows the developed CDC IC design process.

First, we investigated a given library of standard cells from a target manufacturing process (SkyWater 130nm process) and created a missing, essential cell for the CDC design, which was a transmission gate.

Here, standard cells are logic gate designs given by a foundry (IC chip manufacturing company), and logic gates are devices that implement simple logic functions (e.g., NAND gate). Also, a transmission gate is a circuit that functions as either a closed or open circuit depending on the gate voltage. In the CDC, it is used to charge the input capacitance at the beginning of operation. After this, we perform synthesis on the CDC. Usually, synthesis entails transforming a generic HDL file into a list of gates used and their connections.

This is convenient for digital design, since it is usually far less tedious to automatically convert a high-level description of circuit functionality into a list of gates and connections, rather than to outline them manually.

However, since the CDC is an analog design, one must precisely define each gate and their connections, and thus cannot rely on a synthesizer to assemble the design properly. Thus, the HDL file used for the CDC is merely lists of gates and their connections in several different modules, and the synthesis tool merely aggregates without optimization. After synthesis, we create a floor plan of the CDC at a high level. In this, the basic dimensions of the chip and the structure of the power networks are created. For the latter, different power spaces of the chip are allocated based on the three supply voltages of circuits, V_HIGH, V_SENSE, and V_LOW. To do this, we needed to modify the existing digital toolflow, since in standard digital designs, creating multiple power networks is unnecessary, as there is only one supply voltage. After floorplanning, placement is conducted, in which the physical designs of the synthesized gates are placed onto the design and fillers are inserted in the gaps between the gates to ensure that the transistor bodies are at the correct voltage for proper function. This process did not need any modifications to make it compatible with the CDC design process. Next, we route paths between standard cells. Most of this process was suitable for the CDC, but our transmission gate required connecting either the V_HIGH or V_SENSE network to a transmission gate input or output. However, in the openROAD toolflow, a power network cannot be connected to a gate input or output, requiring us to circumvent this by creating a pseudo-power-wire with the same geometry as the power network, based off the technique used by openFaSoC. Finally, the final physical design of the CDC undergoes design rule checks (DRC) and layout versus schematic (LVS) analysis. In DRC, certain checks are run that ensure that the design is manufacturable, such as whether certain wires are too close together, and in LVS, an equivalent circuit is extracted from the

2.1. CDC Background The CDC that was implemented in this work is based off the one published in [5]. Figure 1 shows a simplified version of this CDC containing two inverter chains, one of which is powered by an input capacitor for sensing (INV1), and the other by a constant low voltage, V_LOW (INV2). For background, an inverter is a device that takes an input that is either ‘1’ (supply voltage), or ‘0’ (ground), and “inverts” it, meaning a ‘0’ input results in a ‘1’ output and vice versa. At the beginning of the CDC operation, the capacitor is fully charged to a high voltage (V_HIGH). As the operation commences, input pulses, either rising from ‘0’ to ‘1’ or falling from ‘1’ to ‘0’, are generated at the input of the inverter chains.

In INV1, regardless of which input pulse is used, half of the inverter outputs rise from ‘0’ to ‘1’ using the charge stored in the capacitor. After many such input pulses, the capacitor voltage (V_SENSE) will eventually reach V_LOW as shown in Figure 1. At the earlier part of the discharging process, when V_SENSE is higher than V_LOW, each input pulse “propagates” through INV1 faster than INV2 since inverters powered by higher supply voltages have shorter delays. Eventually, INV1 becomes slower than INV2 as V_SENSE dips below V_LOW, and the operation terminates shortly after. The number of pulses it takes for V_SENSE to reach V_LOW occur is recorded, and, as it turns out, the resulting number is linear with respect to the input capacitance, as long as each individual charge drain from each pulse of the inverter chain is small with respect to the total amount of charge in the input capacitor.

Figure 1. Simplified version of designed CDC and a graph of its operation.

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physical design and is compared against a schematic from the synthesized HDL file to ensure that the design was implemented correctly. Figure 3 shows the fully implemented CDC design. It is worth noting that the bottom left region is the V_HIGH domain, while the top left region is the V_SENSE domain, while the rest of the chip is allocated for the V_LOW domain.

Figure 2. CDC design process.

2.3. CDC Simulation To conduct simulations, we first extracted an equivalent circuit that included parasitic capacitance from the final physical design of the CDC using an open-source tool (Magic). Using this, we simulated the extracted design at the transistor level through another opensource tool (ngSPICE), which is the most accurate circuit simulation level. We measured the output voltages of the counters at the end of the simulation, the time for each measurement, and the power consumption of each supply voltage. We ran these tests for input capacitances of 1.25 pF, 2.5 pF, 5 pF, 7.5 pF, and 10 pF.

3. RESULTS Figure 4 shows our measured capacitance values against the input capacitance. Table 1 compares this design with the previous work [5].

Figure 4. Measured CDC output versus input capacitance.

Figure 3. Fully Designed CDC Chip.

14 Undergraduate Research at the Swanson School of Engineering

Experiment Number

M-CSF Concentration (ng/mL)

M-CSF Culture Duration (days)

TCM Culture Duration (days)

1

5

2

3

2

5

2

7

3

50

5

3

4

5 & 25

2

5

5

5

2

3

6

5 & 25

2

3

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4. DISCUSSION

5. CONCLUSIONS

Figure 4 clearly shows a linear trend, as expected from the circuit functionality. In Table 1, the designed CDC from this work is worse in power, energy, and resolution, but better in terms of time. This mainly comes down to two reasons: higher supply voltages and an older manufacturing process (130nm versus 40nm processes). Using a simple model for manufacturing process scaling and power in terms of voltage, we expect that the previous work should have a lower energy by a factor of 3, a lower power by 10, a worse time by 3, and a better resolution by 10. Using these corrections, the circuits have similar energy, though our resolution is still less by a factor of 2. The latter may be caused by inaccuracies in our simple model, or the use of a standard cell library with different powers and delays. However, even with the correctional factor of 3, our circuit’s measurement time is far better than the previous work, which more than offsets the resolution difference. To summarize, the lesser properties of our CDC are purely due to the manufacturing process that was needed for open-source design and will fix itself as more processes are made open-source. However, this CDC excels at being easily adjustable, as the design process is almost “programming” rather than manual IC design requiring an analog IC design expert. For example, if one increases the number of inverters in the chains, more charge will drain out of the capacitor for each input pulse, meaning the total number of pulses required to discharge the input capacitor to V_LOW is decreased, causing the resolution to worsen, but also causing the measurement time to shorten.

The only analog block in the CDC is a transmission gate, and since it is one of the popular basic analog circuits, the gate can be included in a standard-cell library for automatic analog IC design. Thus, this CDC process also helps enable fully automatic analog IC design through its use of the transmission gate.

In this paper, we developed a CDC using an opensource toolflow. With this, we discovered that the CDC had similar properties, namely resolution, time, power, and energy, to a similar CDC implemented using professional tools, when corrected for manufacturing process. This suggests that if open-source tools can be developed for more advanced manufacturing processes, individuals who were traditionally unable to design chips due to the enormous barrier to entry imposed by the high cost of professional tools will be able to participate in the field.

6. ACKNOWLEDGEMENTS The author thanks Professor In Hee Lee for his guidance during the summer undergraduate research internship. The author also thanks the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh for providing the funding to make the internship possible.

REFERENCES [1] https://shop.cadence.com/Ecom/all-products, accessed January 8, 2024 [2] T. Ajayi et al., “INVITED: Toward an Open-Source Digital Flow: First Learnings from the OpenROAD Project,” 2019 56th ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, USA, 2019, pp. 1-4.

[3] https://github.com/The-OpenROAD-Project, accessed January 8, 2024 [4] A. Hammoud, V. Shankar, R. Mains, T. Ansell, J. Matres and M. Saligane, “OpenFASOC: An Open Platform Towards Analog and Mixed-Signal Automation and Acceleration of Chip Design,” 2023 International Symposium on Devices, Circuits and Systems (ISDCS), Higashihiroshima, Japan, 2023, pp.

01-04, doi: 10.1109/ISDCS58735.2023.10153547.

[5] W. Jung, S. Jeong, S. Oh, D. Sylvester and D. Blaauw, “27.6 A 0.7pF-to-10nF fully digital capacitanceto-digital converter using iterative delay-chain discharge,” 2015 IEEE International Solid-State Circuits Conference - (ISSCC) Digest of Technical Papers, San Francisco, CA, USA, 2015, pp. 1-3, doi: 10.1109/ISSCC.2015.7063137.

[6] Vemulapalli Sravani, Santhosh Krishnan Venkata, “An improved capacitance pressure sensor with a novel electrode design,” Sensors and Actuators A: Physical, 2021, Volume 332, Part 1, doi: 10.1016/j. sna.2021.113112.

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Computational Evaluation of Spinel MnFe2O4 Adsorbent to Remove Heavy Metal from Wastewater Zhengkai Chen1, Ying Fang1, Boyang Li1, Guofeng Wang1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 1

Zhengkai Chen is a senior Material Science and Engineering student at the University of Pittsburgh, studying in a 2+2 joint degree program with Sichuan University, China. His major is Material Science and Engineering and his research interest is in sustainability.

Zhengkai Chen

Ying Fang is a 3rd-Year PhD student of Material Science and Engineering.

She is good at developing machine learning models and combining them with material science.

Ying Fang

Boyang Li is a 5th-year Ph.D. student of Material Science and Engineering.

He is a professional in computational material and will continue his career in the National lab.

Boyang Li

Guofeng Wang

Guofeng Wang is Professor at the Department of Mechanical and Material Science, Swanson School of Engineering, University of Pittsburgh.

His research field is Computational Material and he is working on applying computational skills to understand material science and make accurate predictions of the material’s properties.

Significance Statement

Experimental adsorption research can quantify some adsorption indices, like adsorption rate and capacity.

However, it is hard to understand the adsorption mechanism from performance. This study presents a reliable computational approach that can be extended to study the adsorption behavior under a more complex hydrated model and understand the hydrolysis effect of heavy metal ions on the hydrated surface.

Category: Computational Research

Keywords: density functional theory, heavy metal adsorption, spinel crystal, equilibrium structure, hydrated model 16

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ABSTRACT In this study, the first principles density functional theory (DFT) calculations have been performed to predict the structure and energy of metal ion Cu and Pd adsorbed on the spinel MnFe2O4 under different pH values. This computational work integrates surface reconstruction and Wulff construction to explore the thermodynamically preferable structure of MnFe2O4.

Using the predominant surfaces (100) shown in the equilibrium structure, different pH models of these two surfaces were further developed and combined with the Nernest function to predict the corresponding pH values and adsorption energy. Agreeing with the experimental result, the computational approach predicts correctly that the adsorption sites on the surface can be activated by pH value to get a higher affinity and adsorption energy to the heavy metal atom.

1. INTRODUCTION Industrial wastewater often contains heavy metal ions which are detrimental to human health and the ecosystem.[1][2] For example, copper and lead ions are toxic even at a low concentration in the wastewater because they could affect cellular organelles and components such as cell membrane and DNA.[3][4] Various heavy metal wastewater treatment techniques have been developed such as chemical precipitation[5], ion exchange[6], and adsorption[7]. Adsorption is considered a promising method as it can provide high-quality treated effluent and absorbent can be regenerated by suitable desorption process.[8] The adsorption mechanism involves physical adsorption and chemical adsorption based on intermolecular interaction between adsorbate and adsorbent.[9] Metal oxides are effective absorbents for heavy metal removal which has ample surface adsorption sites and high accessible surface area.[10] The hybrid MnFe2O4 is found to have a high adsorption affinity to these two heavy metal ions which shows an adsorption capacity of 608.46 mg/g for Cu and 471.45 mg/g for Pb, respectively.[11] Chen et al. investigated the adsorption behavior of six divalent heavy metal cations (Cu(II), Cd(II), Pb(II), Co(II), Ni(II), Hg(II)) on MnFe2O4. They found the following relationship of adsorption capacity of different heavy metals from the calculated adsorption energy: Ni(II) > Cu(II) > Co(II) > Pb(II) > Cd(II) > Hg(II) and using the differential charge graph to visualize the electronic interaction between MnFe2O4 surface and heavy metals ions.[11] Xiao et al. studied the chitosan-coated MnFe2O4 nanoparticles (CCMNPs) as recyclable absorbents to heavy metal ions such as Cu(II) and Cr(VI). The adsorption capacity of Cu(II) was found much higher than Cr(VI) suggesting a great affinity to Cu(II).[12] When interacting with water, MnFe2O4 was found responsible for developing charge on the surface of MnFe2O4 -based adsorbents which can influence

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the surface interaction between heavy metal ions.[13] The research of Ren et al. found that pH=6 is the most feasible pH range for both Pb(II) and Cu(II) adsorption, at which there were no hydroxide precipitations formed and the adsorption capacities were high enough.[14] However, there are still some knowledge gaps between experiments and hidden mechanisms. For example, it is difficult to understand how pH influences the adsorption behavior of heavy metal ions to the surface of the material and the corresponding change in adsorption energy. The density functional theory (DFT) calculations are proven helpful for understanding the adsorption process on the metal oxide adsorbents.

Previous computational works on water adsorption on spinel materials put forward some hydrated surfaces.

Zasada et al. investigated the water adsorption on (100), (110), and (111) facets of Co3O4 under a wide range of temperature. They found that the water adsorption process will normally accompany with water dissociation on the surface. With the adding of water molecules to the surface, some molecules will keep the original form without dissociation.[15] Garcês Gonçalves et al. simulated the water adsorption on (001) surface of Mn3O4 using both molecular and dissociative mechanism. They concluded that the molecular water adsorption is more stable compared to the dissociative way on the (001) surface of Mn3O4.[16] Hu et al. studied the adsorption of U(VI) and Eu(III) on magnetic MnFe2O4 nanocubes. They concluded that the adsorption mechanism of heavy metals is mainly based on electrostatic attraction and surface complexation.

Moreover, plenty of accessible -OH groups on the material’s surface in aqueous environment will also facilitate the adsorption process.[17] Shi et al. investigated the high coverage H2O adsorption on CuAl2O4 surface by adding the water molecules to the material surface one by one. They found that the first water molecule is adsorbed on the (100) and (110) surfaces via dissociative adsorption. And for (100) surface there are three possibilities of surface: clean surface, six and eight water molecules adsorption on the CuAl2O4 (100) surface vs. six possibilities: clean surface, four, five, six, and eight water molecules adsorption on the CuAl2O4 (110) surface.[18] Lu et al. studied the adsorption mechanism of heavy metal on the surface of MnFe2O4. They selected the (100) surface, a typical low-index surface, as the adsorption surface.

Then, they simulated the heavy metal adsorption process on this surface and found that Mn is active during the adsorption process by providing adsorbing sites.[19] This work uses the density functional theory (DFT) to calculate different surface energies of MnFe2O4 crystal.

And the calculated structural energy is also combined Nernst equation to simulate the surface model under different pH values and better help to explore the adsorption mechanism of heavy metal on the surface.

2. COMPUTATIONAL METHOD The first-principles spin-polarized calculations were performed using the Vienna Ab initio Simulation Package (VASP).[20] Plane wave basis associated with the projector-augmented wave approach was employed.

[21] Exchange correlation was treated with generalized gradient approximation (GGA) in the form of PerdewBirke-Ernzerhof (PBE) functional.[22] In all calculations, the plane-wave cut-off energy was set as 500 eV and the total energy of system was converged within 10 −5 eV. For the transition-metal elements (Mn and Fe), the DFT+U method was applied to describe the strong correlation of the d-orbital electrons.[23] The U value was set to be 2.0 eV for Mn, and 4.5 eV for Fe, respectively.[24] The structural optimization calculations used a 4×4×4 Monkhorst-Pack k-point mesh for bulk crystal, 4×4×1 for (100) surface slab, 4×6×1 for (110) surface and 3×6×1 for (111) surface slab.[25]

Figure 1. The unit cell of the normal MnFe2O 4 cubic spinel structure indicating the positions of the Mn (purple), the Fe (brown) and O (red) atomic species.

3. CALCULATIONAL RESULT AND ANALYSIS 3.1 Bulk Crystal In this study, an orthorhombic unit cell comprising eight formula units was utilized for modeling the bulk crystal of MnFe2O4, as shown in Fig.1. In a normal structure of MnFe2O4 spinel crystal, Mn ions occupy all the tetrahedral sites and Fe occupies all the octahedral sites. For this normal structure, the optimized lattice parameter was predicted to be 8.56 Å, consistent with other computational results 8.553[26] and 8.44[27]. For experimental result, the lattice parameter of standard ceramics crystal (with 20% degree of inversion) is 8.5.[28] The energy difference between the normal structure and inverse structure was found to be about 0.28 eV (per unit). This normal structure of MnFe2O4 has been widely predicted in the previous computational works.[26][27] In the bulk crystal, each O ion is

17

surrounded by four metal cations (three Fe and one Mn ions), each Mn ion at the tetrahedral sites is surrounded by four O ions, and each Fe ion at the octahedral sites is surrounded by six O ions, respectively. In this study, we have modelled the atomistic structures of five lowindex surfaces of MnFe2O4, as shown in Fig.1.

3.2 Surface Energy In this work, the energy of three typical surfaces: (100), (110), and (111) surfaces were calculated using the formula listed as follows:

where Esurf is the surface energy, Eslab is the slab supercell energy, Ebulk is the bulk energy per atom, N is the number of atoms on the slab surface, and A is the surface area of the slab.

We reconstruct the surface using the following methods: For (100) surfaces, there are potentially 2 terminated surfaces, one is Mn terminated, and the other one is Fe/O terminated. For the Mn terminated surface, one of two Mn atoms from the top surface was moved to the bottom surface as shown in Fig.2. And for the Fe/O terminated surface, four O atoms and two Fe atoms were moved from the top surface to the bottom surface as shown in Fig.3.

Figure 2. Mn (purple) terminated (100) surface from (a) side and (b) top view.

We further considered the condition of the (110) surface and found two existing forms: Fe/O terminated, and Mn/Fe/O terminated. In order to equalize the atoms in the top and the bottom layers, we did the surface reconstruction. For Fe/O terminated surface, two O and one Fe were moved from the top layer to the bottom layer. For Mn/Fe/O terminated surface, we moved two O, one Fe, and one Mn to the bottom surface.

For the (111) surface, we did not do the reconstruction method of this surface since the reconstruction method is significant and still under contradiction. Even though the significant surface reconstruction can greatly lower the surface energy, Benedek et al. speculate that their reconstructed (111) surface still needs further verification.[29] The calculated surface energy of the 5 surfaces are listed below, the surface energy for Mn Terminated (100) surface is 1.16 J/m2, the surface energy for Fe/O Terminated (100) surface is 1.50 J/m2, the surface energy for Fe/O Terminated (110) surface is 1.25 J/ m2, the surface energy for Mn/Fe/O Terminated (110) surface is 1.25 J/m2, and the surface energy for unreconstructed Mn/Fe/O Terminated (111) surface is 1.73 J/m2.

Mn terminated (100) surface has the lowest energy which is well obey with other DFT calculation conclusion that (100) surface is the most stable surface of MnFe2O4.[30] Because low-energy surfaces will extend and can dominate more surface area, it is reasonable to be selected as an adsorption surface. This result also corresponds to the previous experimental literature, as the (100) surface was chosen as the computational surface for DFT adsorption calculation[19]. Furthermore, it can be learned that different surface reconstructions influence the (100) surface energy greatly, which may be due to different bond-breaking energies during the surface cutting.

The (110) surface is a second stable surface while the surface reconstruction causes a minor impact on the surface energy. The (111) surface has the greatest energy among the three. This result is aligned with the conclusion of Chandunika et al that (111) surface has the greatest surface energy among spinel ferrites.[31] It is believed that if surface reconstruction is applied to (111) surface, it may have a lower surface energy than (110) surface. However, we decided to keep the origin.

Therefore, the original (111) surface is more likely to shrink in the equilibrium shape and have less possibility to interact with the substance to be adsorbed.

Figure 3. (100) Fe (brown) / O (red) terminated (100) surface from (a) side and (b) top view.

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3.3 Wulff Construction

3.4 Adsorption Energy

Wulff construction is a classic method linking the surface energy and particle shape. It can predict a particle shape given its orientation-dependent surface free energy.[32] It can predict a particle shape given its orientation-dependent surface free energy.[33] Complementary to these calculations, we combined the calculated results and Wulff construction to get the equilibrium shape of the crystal as shown in Fig. 4, revealing the realistic spatial configuration of its adsorption surfaces. Integrating these data – equilibrium crystal shape, surface areas, and computed adsorption energies – provides a more comprehensive insight into the adsorption behavior. Our computational results identify the (100) surface as the most energypreferable. This aligns with our morphological analysis of MnFe2O4 particles, which typically present a cubic polyhedral shape dominated by (100) facets as shown in Fig.4.

We found that there are 7 potential combining sites of Cu and Pb on the (100) surface of MnFe2O4. We calculated the adsorption of Cu and Pb on each site respectively. We found that the 3 sites on the surface are unstable. And the site with highest adsorption energy for these two heavy metals is the same, as shown in Fig. 5. And the adsorption energy on this site is 4.467 eV for Cu and 3.892 eV for Pb, respectively. The potential mechanism may be this site leaves enough space for atom to accommodate and have two oxygen atoms to form strong bonds.

From the Fig. 5, it can be seen that Cu and Pb atom is adsorbed on the surface through forming the chemical bond with O atom. We further measure the bond length between the oxygen and Cu, Pb atom is 1.805 Å and 2.145 Å, respectively.

Figure 5. Adsorption of a Cu atom (blue) on the (100) surface of MnFe2O4, Mn (purple), Fe (brown) and O (red).

3.5 Hydronated Surfaces We further investigated the hydronated surfaces under different pH values. In the model construction, we found that Mn has the greatest affinity to the hydroxide group. Therefore, to keep the structure stable, the hydroxide group always occupies the Mn site in our models.

Figure 4. The equilibrium MnFe2O 4 particle with a cubic polyhedral shape dominated by (100) facets predicted from the calculated GGA + U surface energies and the Wulff construction.

For neutral surface, four hydrogen ions were attached to the oxygen atoms on the surface, four hydroxide groups were linked to three Fe sites and one Mn site on the surface, and one H2O molecule was kept on the left Fe site, as shown in Fig. 6a.

For acid aqueous surface, four hydrogen ions are regarded been neutralized by hydrogen ions in an acid solution. The acid model was constructed by four hydrogen ions attached to the oxygen atoms on the surface and five H2O molecules were linked to four Fe sites and one Mn site on the surface, as shown in Fig. 6b.

19

For alkaline aqueous surface, the four hydrogen ions on the surface are neutralized. Because the oxygen atoms on the surface have less adsorption affinity to the H2O molecule, we can regard the H2O molecule in the free state. And the final structure is four hydroxide groups on three Fe sites and one Mn site. The leftover Fe is occupied by one H2O molecule, as shown in Fig. 6c.

We further combined the energy of these three models with the Nernst equation and got the acid model with a pH value of 1.10 and the alkaline model with a pH value of 8.75.

Solution type

Adsorption Energy (eV)

Acid solution

2.57

Neutral solution

3.30

Alkaline solution

4.62

Table 1. Predicted adsorption energy of Cu on MnFe2O 4 (100) surface in different types of wastewater.

4. CONCLUSION In summary, this work applied DFT calculation to optimize the structure and found out the lattice parameter under different degree of inversion of the MnFe2O4 crystal. The calculated lattice parameters obey the experimental testing results, which under an error of 3%. Then in order to find out the thermodynamically preferable surface, the energies of different surfaces were calculated, and the surface reconstruction was applied to the (100) and (110) surfaces to neutralize the charges on the slabs. The equilibrium shape of the crystal was obtained based on the calculated surface energy and Wulff construction, which presents a cubic polyhedral shape dominated by (100) facets.

The adsorption affinity of each combining site on the (100) surface to heavy metal ions were further tested.

Based on the hydronated Surfaces energies and Nernst equation, the surface model and pH value were linked and got the adsorption energies of Cu under different pH which follow the result of experiment. Future work can be done to explore how the coordination number of surface atoms influence the adsorption energy and apply the method for other heavy metal ions like lead (Pb).

5. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering and Mascaro Center for Sustainable Innovation & Office of Sustainability.

Figure 6. (100) surface under (a) neutral pH=7, (b) acid pH=1.10, and (c) alkaline, pH=8.75, aqueous solution, H (white), Mn (purple), Fe (brown) and O (red).

Copper atom was then placed on these three surfaces to quantify the adsorption energy change under different pH value. The calculated adsorption energy was listed as Tab. 1 which basically follow the experimental conclusion that the adsorption dynamic increases with the increase of pH value.[14]

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Ingenium 2024

REFERENCES [1.] Volesky, B., Detoxification of metal-bearing effluents: biosorption for the next century. Hydrometallurgy 2001, 59 (2-3), 203-216.

[2.] Vunain, E., Mishra, A., Mamba, B., Dendrimers, mesoporous silicas and chitosan-based nanosorbents for the removal of heavy-metal ions: A review. International journal of biological macromolecules 2016, 86, 570-586.

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[5.] Wu, R., Removal of Heavy Metal Ions from Industrial Wastewater Based on Chemical Precipitation Method.

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[7.] Chakraborty, R., Asthana, A., Singh, A. K., Jain, B., Susan, A. B. H., Adsorption of heavy metal ions by various low-cost adsorbents: a review. International Journal of Environmental Analytical Chemistry 2022, 102 (2), 342-379.

[8.] Fu, F., Wang, Q., Removal of heavy metal ions from wastewaters: a review. Journal of environmental management 2011, 92 (3), 407-418.

[9.] Yagub, M. T., Sen, T. K., Afroze, S., Ang, H. M., Dye and its removal from aqueous solution by adsorption: a review. Advances in colloid and interface science 2014, 209, 172-184.

[10.] Gupta, K., Joshi, P., Gusain, R., Khatri, O. P., Recent advances in adsorptive removal of heavy metal and metalloid ions by metal oxide-based nanomaterials.

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[11.] Chen, Q., Tang, Z., Li, H., Wu, M., Zhao, Q., Pan, B., An electron-scale comparative study on the adsorption of six divalent heavy metal cations on MnFe2O4@ CAC hybrid: Experimental and DFT investigations.

Chemical Engineering Journal 2020, 381, 122656.

[12.] Xiao, Y., Liang, H., Chen, W., Wang, Z., Synthesis and adsorption behavior of chitosan-coated MnFe2O4 nanoparticles for trace heavy metal ions removal.

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[13.] Podder, M., Majumder, C., SD/MnFe2O4 composite, a biosorbent for As (III) and As (V) removal from wastewater: Optimization and isotherm study.

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[14.] Ren, Y., Li, N., Feng, J., Luan, T., Wen, Q., Li, Z., Zhang, M., Adsorption of Pb (II) and Cu (II) from aqueous solution on magnetic porous ferrospinel MnFe2O4.

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[15.] Zasada, F., Piskorz, W., Cristol, S., Paul, J.-F., Kotarba, A., Sojka, Z., Periodic density functional theory and atomistic thermodynamic studies of cobalt spinel nanocrystals in wet environment: Molecular interpretation of water adsorption equilibria. The Journal of Physical Chemistry C 2010, 114 (50), 2224522253.

[16.] Garces Goncalves Jr, P. R., De Abreu, H. A., Duarte, H. l. A., Stability, structural, and electronic properties of hausmannite (Mn3O4) surfaces and their interaction with water. The Journal of Physical Chemistry C 2018, 122 (36), 20841-20849.

[17.] Hu, Y., Zhao, C., Yin, L., Wen, T., Yang, Y., Ai, Y., Wang, X., Combining batch technique with theoretical calculation studies to analyze the highly efficient enrichment of U (VI) and Eu (III) on magnetic MnFe2O4 nanocubes. Chemical Engineering Journal 2018, 349, 347-357.

[18.] Shi, L., Meng, S., Jungsuttiwong, S., Namuangruk, S., Lu, Z.-H., Li, L., Zhang, R., Feng, G., Qing, S., Gao, Z., High coverage H2O adsorption on CuAl2O4 surface: a DFT study. Applied Surface Science 2020, 507, 145162.

[19.] Lu, M., Su, Z., Zhang, Y., Zhang, H., Wang, J., Li, Q., Jiang, T., Mn-Doped Spinel for Removing Cr (VI) from Aqueous Solutions: Adsorption Characteristics and Mechanisms. Materials 2023, 16 (4), 1553.

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23. Anisimov, V. I., Zaanen, J., Andersen, O. K., Band theory and Mott insulators: Hubbard U instead of Stoner I. Physical Review B 1991, 44 (3), 943.

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[24.] Huang, J.-R., Cheng, C., Cation and magnetic orders in MnFe2O4 from density functional calculations.

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[27.] Mounkachi, O., Lamouri, R., Salmani, E., Hamedoun, M., Benyoussef, A., Ez-Zahraouy, H., Origin of the magnetic properties of MnFe2O4 spinel ferrite: Ab initio and Monte Carlo simulation. Journal of Magnetism and Magnetic Materials 2021, 533, 168016.

[28.] Yang, A., Chinnasamy, C., Greneche, J., Chen, Y., Yoon, S. D., Chen, Z., Hsu, K., Cai, Z., Ziemer, K., Vittoria, C., Enhanced Neel temperature in Mn ferrite nanoparticles linked to growth-rate-induced cation inversion. Nanotechnology 2009, 20 (18), 185704.

[29.] Karim, A., Fosse, S., Persson, K. A., Surface structure and equilibrium particle shape of the LiMn2O4 spinel from first-principles calculations. Physical Review B 2013, 87 (7), 075322.

[30.] Pottker, W. E., de la Presa, P., Gonçalves, M. A.. Ramalho, T. C., Hernando, A., La Porta, F. A., Nanocrystalline Spinel Manganese Ferrite MnFe2O4: Synthesis, Electronic Structure, and Evaluation of Their Magnetic Hyperthermia Applications. In Functional Properties of Advanced Engineering Materials and Biomolecules, Springer: 2021; pp 335348.

[31.] Chandunika, R., Vijayaraghavan, R., Sahu, N. K., Magnetic hyperthermia application of MnFe2O4 nanostructures processed through solvents with the varying boiling point. Materials Research Express 2020, 7 (6), 064002.

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22 Undergraduate Research at the Swanson School of Engineering

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Effects of estrogen on platelet activation relative to cardiovascular health A. Cocco1,2, S. Rajesh1,2, J.F Antaki4, H.S Borovetz1,2,3 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 3 Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 4 Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 1

Adriana Cocco

Shivbaskar Rajesh

Harvey Borovetz

James Antaki

Adriana Cocco is a senior bioengineering student at the University of Pittsburgh with a focus in cellular engineering. Her primary interests include regenerative medicine and cardiovascular health.

She currently plans to pursue a graduate degree in bioengineering.

Shivbaskar Rajesh is enrolled in graduate studies to pursue a scientific career in improving quality of life for cardiopulmonary medical device users. As a University of Pittsburgh PhD candidate, his research focuses on blood trauma from assisted circulation at the McGowan Institute of Regenerative Medicine.

Harvey Borovetz is a distinguished professor and former chairman of the University of Pittsburgh.

He dedicates himself to decades long R&D of cardiovascular organ replacements for adult and pediatric patients. His current efforts are focused on the design and preclinical testing of a miniaturized, fully-implantable, magneticallylevitated mixed-flow blood pump, the PediaFlow Pediatric VAD. James Antaki is a Susan McAdam Professor of Heart-Assist Technology.

He has devoted the majority of his professional career to the development of blood-wetted medical devices. As Director of Artificial Heart Research at the University of Pittsburgh, he lead a multidisciplinary team to develop the world’s first magnetically levitated heart-assist pump, known as the Streamliner.

Significance Statement Cardiovascular health is greatly impacted by hormonal concentrations. Unfortunately, research relative to how certain hormones impact the heart is contradictory.

While current studies fail to indicate the direct effects of the estrogen compound in particular, this information has the potential to determine the safety of post-menopausal treatments such as hormone replacement therapy (HRT).

Category: Experimental Research

Keywords: cardiovascular, post-menopausal, platelet activation, estrogen Abbreviations: cardiovascular disease (CVD), hormone replacement therapy (HRT), venous thromboembolism (VTE)

ABSTRACT The aging, female population is particularly susceptible to cardiovascular disease (CVD). This is commonly associated with menopause and therefore the overall decline of sex hormones such as estrogen. In response to drastic drops in hormone concentrations, women often undergo hormone replacement therapy (HRT), in which they are administered specified dosages of estrogen. Previous studies have revealed HRT to be both beneficial and harmful to aspects of cardiovascular health, specifically the risk of thrombosis due to the biochemical activation of platelets. Accordingly, this study was undertaken to better determine the effects of estrogen on platelet activation. Blood samples were spiked with three different dosages of estrogen (1, 2, and 10 μM) and analyzed for platelet activation rates. The estrogen compound was dissolved in 100% ethanol and diluted in PBS before being added to blood samples. Samples with only 100% ethanol/PBS served as control. Platelet activation of each sample was observed with the use of flow cytometric quantification of P-selectin (CD62p-PE).

Analysis of data revealed increased platelet activation in the blood samples with estrogen compared to those containing the control solution. These results suggest further investigation of the risks of HRT with respect to thrombotic events and/or other unfavorable cardiovascular outcomes.

1. INTRODUCTION The increased prevalence of heart disease in aging women compared to men has been well established.

The American Heart Association (AHA) states that 44 million women are living with some type of heart disease, corresponding with about 1 in every 3 female

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deaths [1]. Menopause, which alters the hormone concentrations in the average woman’s body, is a major factor that has been associated with this phenomenon.

To aid the drastic drop in post-menopausal hormone levels, women often undergo hormone replacement therapy (HRT). This treatment commonly includes distribution of the estrogen compound, a highly contradictory hormone relative to its effects on cardiovascular health. Specifically, estrogen has been found previously to be a major risk factor of thrombosis, the basis of major cardiovascular disorders such as arterial cerebral thrombosis and venous thromboembolism (VTE) [2]. In contradistinction, Del Principe and Ruggieri state the presence of estrogen can protect women from cardiovascular disease through decreasing leukocyte adhesion to endothelial cells, therefore lessening the likelihood of thrombotic events [3]. The purpose of this current study is to determine whether the presence of estrogen in blood leads to a significant increase in platelet activation using flow cytometric techniques.

2. MATERIALS AND METHODS 2.1 Blood Source This study used fresh venipuncture female ovine blood (Shiloh Farm Montadales, PA) anticoagulated with CPD-A. Ovine blood was chosen due to its comparable coagulation properties to human blood [4]. Each trial utilized blood from a different donor animal.

(3mL volume – 1, 2, and 10 μM). Upon completion of rocking, 0.5mL of blood was extracted from each test tube for testing. Plasma free hemoglobin was measured to assess that the rocking method provided similar changes among all compared trials. Hemolysis data is represented in Figure 1, the minimal standard deviation values represented by the error bars portray equivalent treatment and handling of all blood samples over the duration of the 5 trials.

Figure 1: Averaged hemoglobin concentrations (+/- SD) among 5 different trials are represented above. Each pair of bars represents one test trial (n=5), in which the blue (striped) bar illustrates still (control) samples and the orange (solid) bar portrays rocking samples. For reference, trial 1 still samples averaged a hemoglobin concentration of 10.2 mg/dL, while rocking samples averaged a hemoglobin concentration of 18.7 mg/dL.

2.2 Blood and Estrogen Preparation

2.3 Data Processing

Upon arrival in the lab, blood was filtered using a 40 µm blood transfusion filter (Haemonetics, PA) to remove any microaggregates, clots, and debris. Hematocrit of the filtered blood was then measured to ensure a normal proportion of red blood cells. 17-β-Estradiol (Sigma Aldrich, MO) was dissolved in 100% ethanol and diluted in phosphate buffered saline in a 1:9 ratio. This estradiol stock solution was then used to mimic blood-hormone concentrations of the average postmenopausal woman undergoing HRT (1, 2, and 10 μM).

Platelet activation of each sample was observed with the use of flow cytometric quantification of P-selectin (CD62p-PE, Miltenyi Biotec, MD) in a fixed, whole blood assay using a MACSQuant 10 flow cytometer (Miltenyi Biotec, MD). CAPP2a (Sheep CD61, Novus Biologicals, CO) conjugated with a violet fluorescent secondary antibody (VioBlue, Miltenyi Biotec, MD) was used to identify platelets. The flow cytometer was set to detect CAPP2a at a 2% fluorescence intensity threshold and was operated until 5,000 single platelet scattering events were collected. Within this gated population, P-selection was identified. Figure 2 graphically displays this data, revealing the platelet activation present in a single blood sample. Platelet activation was reported as a percent difference with baseline measurements to determine whether activation rates were dosage dependent [6].

2.3 Experimental Procedure Each trial divided samples into a control (at rest) sample set and an activated sample set, in which the samples underwent 90 minutes of rocking with steel beads.

All trials were performed at room temperature. The rocking method is based on the red blood cell (RBC) mechanical fragility testing protocol as outlined by Zeigler et al. [5]. Each group included a baseline sample (3mL whole blood) and three estradiol infused samples

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3.RESULTS Flow cytometry indicated that the presence of estrogen increased platelet activation relative to the ethanol condition. Figure 3 depicts the control sample data collected from 5 trials, indicating an increase in platelet activation due to the estradiol solution compared to that of the ethanol solution.

These findings are further supported by the activated sample data shown in Figure 4. While activation values varied over the 5 trials, those corresponding to the estradiol solution samples were consistently higher than those containing the control solution. Fixed effect tests revealed that the observed rise in platelet activation was indeed significant.

Figure 2: Flow cytometric quantification of P-selectin in a fixed, whole blood assay, in which the gating technique is utilized to encompass platelets in the “activated” state. The quantification shown reveals a platelet activation rate of 77.37% in a 10 μM estradiol sample (rocking).

2.3 Data Analysis The percent platelet activation for each sample was recorded and compared to the baseline sample via percent difference calculations. Taking into consideration the adverse effects of ethanol – 1, 2, and 10 μM dosages were mimicked lacking the estradiol component. These platelet activation results were directly compared to samples including estradiol to minimize ethanol’s influence on the blood.

Statistical software (JMP: Statistical Software, NC) was used to determine whether the ethanol component significantly impacted platelet activation. Data components were first analyzed according to p-values under the fixed effect tests; p-values<0.05 were considered significant. Observed effect sizes relative to least squares mean and standard error data were used to observe the significance level of estrogen presence. Statistical analysis considering the following components was performed: concentration, presence of estrogen, presence of prior activation, trial number, and change in platelet activation from baseline. Using fixed effect tests, effect details for both concentration and presence of estrogen interaction effects were collected.

Figure 3: Averaged percent platelet activation values (+/- SD) of still (at rest) test samples among 5 trials, where blue (solid) represents samples infused with stock solution containing estradiol dissolved in ethanol and orange (striped) represents samples containing stock solution with only ethanol.

Figure 4: Averaged percent platelet activation values (+/- SD) of rocking test samples among 5 trials, where blue (solid) represents samples infused with stock solution containing estradiol dissolved in ethanol and orange (striped) represents samples containing stock solution with only ethanol.

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Further statistical analysis was performed to determine whether platelet activation varied according to the concentration of estrogen administered. Among the 5 trials, a significant difference in activation values was not present between the different dosages. In other words, increased activation rates were seen in blood with the minimum estrogen concentration of 1 μM. Obtained statistical values suggest that a larger sample size with similar trends could potentially result in statistically significant changes in activation rates according to dosage.

4. DISCUSSION The results of this study correspond to trends of previous research, suggesting altered activation rates of platelets due to the addition of the estradiol compound. This effect could be due to several factors, one of which being estradiol’s proven tendency to influence levels of procoagulant factors VII, X, XII, and XIII, and prothrombin fragments 1 + 2 [7]. The alteration of these levels can be associated with activation of coagulation, leading to the release of the von Willebrand factor and factor VIII via endothelial cells [8]. These components of research strongly support the idea that the estrogen compound is likely to increase platelet activation, leading to the downstream effects of thrombotic events.

5. CONCLUSIONS This study investigated the impact of estrogen on blood platelets. We conclude that estrogen induces platelet activation, a leading source of thrombogenesis and therefore unfavorable cardiovascular conditions. Upon confirmation from future testing, clinical practices regarding the use of post-menopausal treatment therapies such as HRT should be approached with caution, especially in individuals with pre-existing cardiovascular disease. Further research should be performed using human blood samples to confirm the present findings of increased platelet activation.

6. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering and contract CDMRP: PR190230 awarded to Dr. Antaki.

The lack of significance identified in terms of the concentration of estrogen administered could be relative to varying aspects of the study. These include but are not limited to the following: donor-to-donor variability, the amount of estrogen chosen to be administered, or potential experimental artifacts due to utilization of the rocking method. Future experiments should consider analyzing samples containing an estrogen concentration of < 1 μM. Additionally, implementation of a more sensitive treatment could reduce the risk of experimental artifacts; ideally, other forms of platelet activation should be minimized.

Despite previous claims to the contrary, various studies continue to provide evidence that estrogen has little to no effect on platelets. Moreover, the claim that estrogen is beneficial to cardiovascular health by possessing the ability to adjust lipoprotein levels, lower LDL, and raise HDL are overshadowed by the potential harmful effects of administering the hormone. To better understand the impact of administering estrogen, additional research should explore the direct effects of the hormone on platelet aggregation and downstream cardiovascular effects.

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REFERENCES [1.] “Compared with Men, Women with Heart Disease More Likely to Report More Treatment and Care Disparities.” Johns Hopkins Medicine, Dec. 2018, https://www.hopkinsmedicine.org/news/newsroom/ news-releases/2018/12/compared-with-menwomen-with-heart-disease-more-likely-to-reportmore-treatment-and-care-disparities.

[1.] D. Del Principe, A. Ruggieri, D. Pietraforte, A. Villani, C. Vitale, E. Straface, and W. Malorni, “The Relevance of Estrogen/Estrogen Receptor System on the Gender Difference in Cardiovascular Risk.” International Journal of Cardiology, vol.

187, 2015, pp. 291–298, https://doi.org/10.1016/j. ijcard.2015.03.145.

[2.] A. Eisenberger and C. Westhoff. “Hormone Replacement Therapy and Venous Thromboembolism.” The Journal of Steroid Biochemistry and Molecular Biology, vol. 142, 2014, pp. 76–82, https://doi.org/10.1016/j. jsbmb.2013.08.016.

[3.] J. M. Siller-Matula, R. Plasenzotti, A. Spiel, P. Quehenberger, and B. Jilma, “Interspecies differences in coagulation profile,” Thrombosis and Haemostasis, vol. 100, no. 3, pp. 397–404, Sep. 2008, https://pubmed.ncbi.nlm.nih.gov/18766254/ [4.] L. A. Ziegler, S. E. Olia, and M. V. Kameneva, “Red Blood Cell Mechanical Fragility Test for Clinical Research Applications.” Artificial Organs, vol. 41, no. 7, 2016, pp. 678–682, https://doi.org/10.1111/ aor.12826.

[5.] C. A. Johnson, Jr., T. A. Snyder, J. R. Woolley, and W. R. Wagner, “Flow cytometric assays for quantifying activated ovine platelets,” Artif Organs, col. 32, no. 2, pp. 136–45, Feb 2008, doi: 10.1111/j.15251594.2007.00498.x.

[6.] R. E. Peverill, “Hormone Therapy and Venous Thromboembolism.” Best Practice &amp; Research Clinical Endocrinology &amp; Metabolism, vol. 17, no. 1, 2003, pp. 149–164, https://doi.org/10.1016/ s1521-690x(02)00079-9.

[7.] V. M. Miller, M. Jayachandran, and W. G. Owen, “Ageing, Estrogen, Platelets and Thrombotic Risk.”

Clinical and Experimental Pharmacology and Physiology, vol. 34, no. 8, 2007, pp. 814–821, https:// doi.org/10.1111/j.1440-1681.2007.04685.x

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First experiences integrating functional near-infrared spectroscopy brain imaging and virtual reality Kasey Forsythe1,2, Hendrik Santosa2,3, Theodore Huppert2,4 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Multimodal Methods for Noninvasive Neuroimaging Lab, 3 Department of Radiology, University of Pittsburgh, Pittsburgh, PA 4 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 1

Kasey Forsythe is a junior bioengineering student from Albuquerque, New Mexico. She plans to continue her education after graduation by pursuing her PhD.

Kasey Forsythe

Hendrik Santosa

Theodore Huppert

Hendrik Santosa received his PhD in cogno-mechatronics engineering from Pusan National University, Korea, in 2016. Currently, he is a research assistant professor in the Department of Radiology, University of Pittsburgh. His research interest includes statistical method, brain– computer interface, hyperscanning, advance brain signal processing, and multimodal techniques (i.e., NIRS-EEGMEG-fMRI).

Theodore Huppert, PhD, is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh. His research focuses on novel method development for multimodal neuroimaging.

Significance Statement

Compared to controlled conditions of most neuroimaging studies, cognitive tasks encountered in the real-world are more complex and nuanced. While rigid control provides a framework for quantitative hypothesis testing, the generalizability of such experiments to the real-world remains unclear. By using virtual reality to present novel functional tasks for brain imaging, the rigidity of these experimental designs can be relaxed in a controlled way.

Category: Methods

Keywords: Neuroimaging, Virtual reality, Mixed reality, fNIRS 28

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ABSTRACT Traditional experimental cognitive paradigms used in functional neuroimaging studies are carefully designed to manipulate only a few variables of interest while trying to control all other confounding factors. However, real-world cognitive tasks such as navigating an unknown environment or socially interacting with other people, are much more complex and less controllable. Integrating non-invasive methods of neuroimaging with Virtual Reality (VR) could potentially give researchers an opportunity to collect data in environments that can be used to mimic real-world settings, while still maintaining a controlled environment for collecting reliable data and testing quantitative hypotheses. In this study, we used a portable, brain imaging method called functional Near-Infrared Spectroscopy (fNIRS) to investigate the feasibility and reproducibility of recording human brain activity during a VR task to assess spatial working memory while immersed in a virtual room.

Although, brain activity was recorded successfully from a total of four participants, our initial experiences with integrating these two technologies highlighted additional challenges such as susceptibility to motion sickness and participants’ prior experience with virtual reality, which will need to be considered when designing future tasks.

1. INTRODUCTION Advances in functional, non-invasive neuroimaging technology, such as functional magnetic resonance imaging (fMRI), have led to an extraordinary expansion of our knowledge of human brain function and cognition. However, a drawback of these technologies is often the limitations placed by the requirements for data collection and analysis. For a typical fMRI study, the participant lays motionless and supine while presented visual stimulus. Moreover, because almost all functional neuroimaging methods provide relative measurements that require a comparison between two or more observation sets (e.g., between a test and control task), there are requirements needed for analysis that are imposed on the experimental paradigm design. While such neuroimaging studies provide important data about cognitive mechanisms, the relationship of these controlled tasks to the more nuanced and complex cognitive scenarios encountered in the real world is unclear. Integrating Virtual Reality (VR) with brain imaging would allow researchers to collect data from subjects immersed in environments that more accurately mimic real world cognitive decision-making whilst still being able to provide reliable data from a controlled environment.

Additionally more portable methods of neuroimaging like functional near-infrared spectroscopy (fNIRS) allow researchers more flexibility in their experimental set-up so data collection is less constrained to a static setting.

In this study, we took the first steps towards combining

Ingenium 2024

VR with fNIRS. Although, brain activity was recorded successfully from a total of four participants our initial experiences with integrating these two technologies highlighted additional challenges such as susceptibility to motion sickness and participants’ prior experience with virtual reality, which will need to be considered when designing future tasks.

2. METHODS

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method of collecting brain activity based on changes in the local concentrations of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain during a task. FNIRS is based on spectroscopic optical blood measurement through tissue like photoplethysmography (i.e., finger-tip pulse oximeter). This technique uses red to near-infrared light (650-900nm) transmitted between optical sources and detectors placed on the surface of the scalp.

The high intrinsic optical scattering and low optical absorption of biological tissue within this range of wavelengths allows fNIRS to measure optical changes in the brain and convert them to changes in brain activity (hemodynamic response) via modified BeerLambert law [1]. FNIRS is also a portable brain imaging technology which can use wearable instruments and allow unrestricted ambulatory movement of the participant during brain recording.

2.2 fNIRS

Over the last several years, researchers have begun to use fNIRS to measure the brain in naturalistic or “realworld” environments (reviewed in [2]). To date, fNIRS has been used in several such tasks such as walking/ gait [3], during car driving [4], and social interaction [5]. However, one of the challenges of such naturalistic experimental designs is the analysis of such data since the real-world n is often in direct conflict with the requirements for well-designed, statistically balanced, and well-controlled tasks as typical of traditional functional studies. Virtual reality (VR) and mixed reality paradigms could offer an intermediate solution between the traditional task design and mimics of the real-world, which could be used to test new analysis methods aimed at supporting more naturalistic designs. Over the last decade, VR methods have begun to be used in neuroimaging studies including fMRI (reviewed in [6]). However, these require custom VR setups that are compatible with fMRI imaging which is often less than ideal with respect to mimicking the realworld [7].

2.1 Subject demographics In this study, 4 healthy adult volunteer subjects (ages 18-44 yo; 3M/1F) participated. This study was approved by the local institutional review board at the University of Pittsburgh.

To collect brain activity during the task, we used the NIRx NIRSPORT2 instrument (NIRx Medizintechnik GmbH Berlin, Germany). 42 measurement channels were distributed across bilateral frontal and sensorimotor brain regions. Fig. 1 shows the sensitivity of the probes overlying Brodmann’s areas which represent different functional regions of brain activity. During brain activity, there is a change in concentrations hemoglobin optically measured by the system. In this experiment, the fNIRS system was worn underneath the Oculus Meta Quest 2 headset to collect brain activity while the Oculus Meta Quest 2 ran the VR program. The fNIRS headset was designed to integrate with the Oculus headset to limit mechanical and optical interference. During the task, the optical signals were recorded at 8Hz at 780nm and 850nm, which were used to estimate the change in concentrations of hemoglobin.

Figure 1: fNIRS probe used in the study shown with underlying Brodmann’s areas.

Figure 2: Set-up of the integrated fNIRS and Oculus headsets on a participant.

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2.3 Box Task In this study, we used a box VR task, which was previously described by Leon et al to assess spatial memory [8]. This and similar visuospatial tasks have been demonstrated to be sensitive to cognitive impairments and declines in spatial working memory in aging populations [9], as well as populations with primary progressive aphasias [10]. These previous studies examined response accuracy but did not have direct measures of brain activity. The task program was built in Unreal Engine, to perform a spatial working memory box task. The program was built to fit a block design which distinctly separated the ‘task’ periods of the program from the ‘baseline’ periods of the program by certain time intervals to collect changes in brain activity from a task compared to a baseline. In this task, the participant was placed in a room with 16 identical boxes spread throughout the room. The user initially started in a 30-second survey period where they moved around the room and clicked on every box to find the 3 reward boxes. When pressed, a box would either turn green to indicate it was a reward or turn red to indicate it was not a reward. The user would then go through a 30-second baseline period where the boxes became invisible. This baseline period was followed by a retrieval period where the participant was spawned at a random location in the room and all the boxes’ colors are reset to the default and they are given 30 seconds to recall the location of the three reward boxes. In a level, the participant goes through a survey period and then alternating rest and retrieval periods for five trials.

We recorded the participant’s brain activity as they went through two levels. We manually marked when the participant started the level while collecting the fNIRS data and used the time structure from the block design to differentiate between the different task and baseline periods within the collected data.

Figure 3: Screens displayed during the retrieval period and the baseline period respectively in the VR task.

Figure 4: The image on the left shows the set-up of the room in the VR task with the randomly assigned reward boxes throughout the room. The image on the right shows the block design of the program with the survey period and the alternating retrieval and baseline periods.

2.4 Data Processing The data was processed and analyzed using the Brain AnalyzIR software package [4]. For preprocessing, the raw fNIRS data was resampled and converted into optical density. The modified Beer-Lambert Law was used to convert the wavelength-dependent changes in optical absorption into time-courses of oxy- and deoxyhemoglobin. One of the challenges of fNIRS is that the brain signals are recorded from the surface of the scalp and are sensitive to contamination from superficial systemic physiology (cardiac, blood pressure, and respiratory fluctuations in the scalp). In this dataset, our initial results showed global changes in oxy-Hb values indicating a systemic physiological response during the task. We used a principal component analysis (PCA) filter within the Brain AnalyzIR toolkit to remove this dominant spatial covariance from the signal to correct this artifact. For each scan, we removed the first n-principal components needed to remove 80% of the covariance. We then performed a general linear regression of the hemoglobin timecourses for each of the recording channels using the event timing for the Survey and Retrieval blocks. If the regression coefficients associated with the two event types are non-zero, the null hypothesis that this brain region is not related to the task is rejected. The statistical values (t-value) for each channel and event condition are examined to spatially map the location and magnitude of the brain activity. Group-level statistical maps are created using a linear mixed effects model. We used a Benjamini-Hochberg correction to control false discoveries.

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3. RESULTS

4. DISCUSSION

We recorded fNIRS data during the VR task with n=4 to find preliminary results. Brain activity changes were estimated between the Survey and the Retrieval periods compared to the baseline. Figure 5 shows the results from the fNIRS data collected. This figure shows the t-value (p-value [corrected] < 0.05) for oxy-Hb changes during the Survey and Retrieval phases of a task. In the preliminary data collected, there were some patterns of brain activity, the most significant being in the Survey period.

The goal of this project was to investigate the feasibility of recording brain activity during a VR task. Combining fNIRS with VR required balancing several competing design factors related to both the physical hardware of the devices and the experimental design requirements needed to collect and analyze fNIRS data.

Figure 5: Activation map shown with changes in concentration of oxygenated hemoglobin (Oxy-Hb) during the survey and the retrieval periods. The figure shows relative increases or decreases in Oxy-Hb between the different measurement pairs. The greater changes are shown in a darker color.

We estimated changes in brain activity over Brodmann’s areas and found statistically significant changes in BA-10L and BA-6L corresponding to a relative decrease in activity in the prefrontal cortex and a relative increase in activity in the premotor cortex.

Brodmann Area Activation During Tasks

Figure 6: Activation during the Survey and Find periods of the task shown over the overlying Brodmann’s areas. The dashed line indicates statistically significant changes in brain activity.

We found that users experienced motion sickness or dizziness. This was likely made worse by discomfort of the fNIRS head cap. This led us to modify the VR game to reduce character speed and motion controls.

However, this limited the number of trial and level repetitions that could be comfortably performed by a participant. Frequent, shorter duration trials are generally used to estimate reliable brain activity, but we found fewer, longer duration tasks were better tolerated. We found that when participants experienced dizziness during the task, they could not properly perform the task which made it difficult to collect brain activity of the participant during the task.

Of the four participants, two experienced dizziness while participating in the task with one being severely hindered in their performance of the task.

While we allowed participants time to get familiarized with the controls of the VR program, we found the participants’ experience with the Oculus’ user interface had a significant impact on the set-up of the experiment. Two of the participants were experienced with using the Oculus before the study. The less experienced participants had to be talked through how to set up the Oculus’ physical scene boundaries and reestablish the connection allowing the experimenter to co-view the VR. We found that blocking the light sensor used by the device to detect when it is “off subject” helped but was still imperfect. While this challenge complicated the set-up of the experiment it had limited interference with the data collected.

Another challenge we found while analyzing the data was accurately recording brain activity during the task period and establishing a baseline condition.

Because of the nature of the retrieval task, if a user was more familiar with the room set up or they were randomly spawned closer to the ‘reward boxes’ they may not take the entire duration of the trial period to find the rewards. In preliminary data, this resulted in some of the retrieval period data being collected when the participant was not actively performing the task.

Additionally, while the baseline or rest period makes the blocks invisible thus making it impossible for the participant to do the task. The preliminary results of the data suggest that the rest period should have a starker contrast from the survey or retrieval period.

31

We additionally encountered challenges with creating a task that was cognitively intensive enough to measure meaningful changes in brain activity. As participants became familiar with the room within a level, the cognitive load demanded of them during the task decreased. This impacted the data collected as there may not have been significant enough cognitive load in the task to extract meaningful brain activity.

Finally, synchronizing the timing of the VR and fNIRS had to be worked out. Since the brain’s hemodynamic response takes about 8-12 seconds after the onset of a task to peak, fNIRS is less sensitive to timing errors compared to other methods such as EEG. We manually marked event times by watching the screen cast along with logged response information. In the future, a UDP/ IP protocol (Lab Streaming Layer) can be used to reduce timing errors.

5. CONCLUSIONS While integrating VR with neuroimaging presents challenges with experimental design, we have found ways to overcome such challenges and observe brain activity during a task presented in virtual reality. While controlling for factors such as participants’ susceptibility to motion sickness, integrating fNIRS and VR shows promise for advancing the field of neuroimaging given its ability to collect brain activity data during a diverse array of immersive tasks that may not be possible to be presented in normal lab-based settings.

6. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh.

REFERENCES [1] D. T. Delpy et al., “Quantitation of Pathlength in Optical Spectroscopy,” Advances in Experimental Medicine and Biology, vol. 248, pp. 41–46, Jan. 1989, doi: https://doi.org/10.1007/978-1-4684-5643-1_5.

[4] P. Pinti et al., “A Review on the Use of Wearable Functional Near-Infrared Spectroscopy in Naturalistic Environments,” Japanese Psychological Research, vol. 60, no. 4, pp. 347–373, Jul. 2018, doi: https://doi.org/10.1111/jpr.12206.

[5] J. C. Menant et al., “A consensus guide to using functional near-infrared spectroscopy in posture and gait research,” Gait & Posture, vol. 82, pp.

254–265, Oct. 2020, doi: https://doi.org/10.1016/j. gaitpost.2020.09.012.

[6] T. Liu, M. Pelowski, C. Pang, Y. Zhou, and J. Cai, “Nearinfrared spectroscopy as a tool for driving research,” Ergonomics, vol. 59, no. 3, pp. 368–379, Sep. 2015, doi: https://doi.org/10.1080/00140139.2015.1076057.

[7] J. S. Taube, S. Valerio, and R. M. Yoder, “Is Navigation in Virtual Reality with fMRI Really Navigation?,” Journal of Cognitive Neuroscience, vol. 25, no. 7, pp.

1008–1019, Jul. 2013, doi: https://doi.org/10.1162/ jocn_a_00386.

[8] León, L. Tascón, J. J. Ortells-Pareja, and J. M. Cimadevilla, “Virtual reality assessment of walking and non-walking space in men and women with virtual reality-based tasks,” PLOS ONE, vol. 13, no. 10, p. e0204995, Oct. 2018, doi: https://doi. org/10.1371/journal.pone.0204995.

[9] R. P. C. Kessels and A. Postma, “The Box Task: A tool to design experiments for assessing visuospatial working memory,” Behavior Research Methods, vol.

50, no. 5, pp. 1981–1987, Sep. 2017, doi: https://doi. org/10.3758/s13428-017-0966-7.

[10] D. Foxe et al., “The Box Task: A novel tool to differentiate the primary progressive aphasias,” European Journal of Neurology, Jul. 2021, doi: https://doi.org/10.1111/ene.15035.

[11] H. Santosa, X. Zhai, F. Fishburn, and T. Huppert, “The NIRS Brain AnalyzIR Toolbox,” Algorithms, vol. 11, no. 5, p. 73, May 2018, doi: https://doi. org/10.3390/a11050073.

[2] Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., & Burgess, P. W. (2020).

The present and future use of functional nearinfrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464(1), 5-29.

[3] F. Klein, S. Huldreich Kohl, M. Lührs, D. MA Mehler, and B. Sorger, “From Lab to Life: Challenges and Perspectives of fNIRS for Hemodynamic Neurofeedback in Real-World Environments.,” Nov. 2023, doi: https://doi.org/0.31234/osf.io/qdjfh.

32 Undergraduate Research at the Swanson School of Engineering

Ingenium 2024

Comparison of prosthetic knee joint types in relation to slip risk Elizabeth Ibata-Arens1, April Chambers1 Goeran Fiedler2 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA 1

Elizabeth Ibata-Arens

April Chambers

Goeran Fiedler

Elizabeth Ibata-Arens is a senior majoring in bioengineering with a biomechanics focus and a minor in exercise science. She hopes to work in the field of prosthetics and orthotics and continue to research human mobility and assistive technology in the future.

April Chambers, PhD, is an Associate Professor in the Departments of Health & Human Development and Bioengineering at the University of Pittsburgh. Her research expertise is in the fields of human movement, biomechanics, human factors, and injury prevention.

Goeran Fiedler, PhD, is an Associate Professor at the Pitt School of Health and Rehabilitation Sciences, teaching in the Prosthetics and Orthotics program. His research interests include optimizing the prescription and fitting of prosthetic devices.

Significance Statement

Lower limb prosthesis users are at a greater risk for injurious slip-and-fall events. This research compares slip risk for users walking with both microprocessorcontrolled and mechanical/non-microprocessorcontrolled prosthetic knee joints. Findings from this study and further research may aid the development and accessibility of advanced technology in lower limb prosthetics.

Category: Methods

Keywords: Microprocessor-controlled knee (MPK), lower limb prosthetics, slip-and-fall risk, required coefficient of friction (RCOF)

ABSTRACT Advanced technology in the field of lower limb prosthetics is designed to better replicate normal human gait and improve stability and fall prevention.

The microprocessor-controlled prosthetic knee joint uses sensors and a microcomputer system to control knee flexion and extension during the stance and swing phases of gait. A body of research finds microprocessor-controlled knee users benefit from a safer and higher quality of life with more mobility and independence. This study was designed to further investigate injurious fall prevention for microprocessor-controlled and non-microprocessorcontrolled prosthetic knee users by assessing known biomechanical parameters associated with slip risk for above-the-knee amputee subjects walking on level-ground with both knee joint types. The required coefficient of friction at the shoe-floor interface, a direct predictor of slip likelihood, was examined at heel contact during the stance phase of gait. The results from this study showed a lowered total mean required coefficient of friction calculated for subjects walking with microprocessor-controlled knees (0.161 ± 0.051) than with non-microprocessor-controlled knees (0.192 ± 0.061), indicating lowered slip risk (p=0.019) for users walking with the more advanced prosthetic technology.

The findings from this research further prove and explain lowered slip-and-fall risk for microprocessorcontrolled knee joint users. This understanding can be used for slip detection and prevention controls in future prosthetic knee design and as additional evidence of the economic value and need for advanced technology in the field of prosthetics.

1. INTRODUCTION People with transfemoral or above-the-knee amputations (most commonly caused by trauma, infection, cancer, or vascular disease [1]) are at an increased risk of falling due to a loss of balance [2,3,4]. Numerous studies have investigated the fall history of lower limb prosthesis (LLP) users, finding that more than half of LLP users report falling at least once a year [2,3]. These falls lead to negative outcomes, including fall-related injuries and deaths, a lowered quality of life, and an increased fear of falling [2,3,4]. Slips on level ground are one of the most common reasons for reported falls in the transfemoral amputee (TFA) population [2,4]. However, while there exists a body of research assessing fall risk for TFA subjects in a laboratory setting [3], only a few studies have investigated slip perturbations and slip risk assessments with prosthetics [5,6,7,8].

Research in fall risk for LLP users has led to the development of improved fall prevention features in advanced prosthetic knee technology, like the microprocessor-controlled knee (MPK) joint. MPK joints replicate normal human locomotion through the use of sensors and a microcomputer system that controls 33

knee flexion and extension during the stance and swing phases of gait. Mechanical or non-microprocessorcontrolled knee (NMPK) joints act as mechanical hinge joints through which knee flexion and extension are controlled by hip muscle contractions or shifts in body weight. As seen in Kaufman et al., MPK joints have been shown to improve ambulation and balance in comparison to NMPK joints [9]. These results show an advancement in stability and a reduced risk of falling for MPK joint users walking on level ground.

The primary aim of this project is to further investigate any differences in slip-and-fall risk between MPK and NMPK joint users by examining known biomechanical variables associated with a greater risk of slipping for TFA participants walking on level ground. Specifically, the required coefficient of friction (RCOF), or the ratio of shear to normal ground reaction force measured at the shoe-floor interface, was analyzed at heel contact.

The RCOF has been found to be an important variable for predicting slipping events, with Beschorner et al. relating high peak RCOF values to a higher likelihood of slipping [10]. Expected RCOF values for ablebodied subjects walking on level ground range from 0.17 to 0.20, as seen in Redfern et al. [11]. Improved body positioning and control of the prosthetic limb throughout the gait cycle can be hypothesized to impact the shoe-floor interface and lower slip risk.

Therefore, we investigated whether RCOF recordings were lower for the MPK joint condition in this study.

The results of this research may lead to a further understanding of how advanced technology in lower limb prosthetics can benefit slip-and-fall incident prevention for LLP users.

2. METHODS 2.1 Data Collection This report analyzes data collected by the Human Movement and Balance Laboratory at the University of Pittsburgh for a previous prosthetic knee joint gait analysis study. Six older adult males (mean age 66.7 ± 6.7 years) with transfemoral amputations participated in the study to completion. Participation in the study consisted of two laboratory gait assessments. Subjects completed the first gait assessment with the prosthetic knee joint type that they were using regularly prior to the visit (MPK or NMPK) since they were already acclimated to it. Within a one-week period of the first gait assessment, subjects returned to the laboratory to exchange their prosthetic knee to the opposing knee joint type by a certified prosthetist. Following the exchange, an eight-week acclimation period was given before the second gait assessment was conducted in the laboratory. Participants who did not complete both gait assessments with both prosthetic knee joint types (MPK and NMPK) were not included in this analysis.

Reflective spherical markers were placed on the anatomical landmarks of the participant. A 14-camera Vicon T40S motion analysis system (Vicon, Oxford, UK) with a sampling rate of 120 Hz was used to record the movement of each subject as they walked across the 5.5 m long walkway. Two Bertec force plates (Bertec, Columbus, OH) were embedded within the walkway for the purpose of recording ground reaction forces at a sampling rate of 1080 Hz. Participants completed three to ten level-ground walking trials at a comfortable pace, attached to a safety harness to prevent any fall-related injuries at each visit.

2.2 Data Processing All data processing was performed using MATLAB software (MATLAB, Version 2023b, The Math Works, Inc., Natick, MA). The raw force plate data was extracted and filtered with a 9th-order lowpass Butterworth filter with a cutoff frequency of 35 Hz. The RCOF was calculated during the stance phase of the prosthetic limb for three clean force plate contacts recorded during the gait assessments. RCOF (eq. 1) was measured as the peak ratio of shear (Fshear) to vertical (Fnormal) ground reaction force during heel strike, once the vertical/normal force was greater than 100 N and the anterior/posterior (Flongitudinal) component of ground reaction force was in the posterior direction [8].

Statistical analyses were performed in JMP (JMP, Version 16.0, SAS Institute Inc., Cary, NC). Paired t-tests were used to compare individual trial RCOF results for each condition. Significance levels were set at 0.05 for all analyses. Marker motion-capture data was extracted and filtered with a 7th-order lowpass Butterworth filter with a cutoff frequency of 10 Hz. Motion-capture data was used to calculate spatiotemporal gait parameters.

34 Undergraduate Research at the Swanson School of Engineering

Ingenium 2024

3. RESULTS Lower total mean RCOF results were found for the walking trials where the participants used the MPK joints. In addition to the prosthetic limb recordings, three clean force plate contacts for each subject’s intact limb were collected from all walking trials recorded for both knee conditions. No difference in prosthetic limb and intact limb results were found. Table 1 below displays all total mean (standard deviation) RCOF results.

MPK RCOF 0.161 (0.051)

NMPK

Prosthetic Intact Limb Limb

0.192 (0.061)

0.176 (0.058)

Figure 2 below displays the averaged RCOF results for all recorded MPK trials compared to all recorded NMPK trials. The results for the NMPK joint trials fall within the expected range (0.17-0.20) for RCOF [11]. The results for the MPK joint trials fall below the expected range.

0.176 (0.026)

Figure 1 below displays the averaged RCOF results for the MPK and NMPK joint trials compared for each subject. Two subjects (S1 and S2) completed the first laboratory visit with an NMPK joint (Figure 1a).

Four subjects (S3, S4, S5, and S6) completed the first laboratory visit with an MPK joint (Figure 1b). Four subjects (S2, S4, S5, and S6) out of the six participants showed lowered RCOF results when walking with the MPK joints.

Figure 2: Mean and standard deviation of RCOF results for all MPK and NMPK joint trials. The dashed red lines represent the normal range for RCOF expected for non-amputee individuals [11].

Paired t-test results showed a significant difference between prosthetic knee joint types (p=0.019), with a lower total mean RCOF found for the MPK trials. Paired t-test results showed no significant difference in RCOF when trials were separated and analyzed by the order of the visits (p=0.273).

4. DISCUSSION

Figure 1: Mean and standard deviation of RCOF results for each subject for both knee joint conditions (a. subjects S1 and S2 completed the first visit with their regularly used NMPK joint, b. subjects S3-S6 completed the first visit with their regularly used MPK joint). *Subjects S2 and S4 walked with a cane on their intact side.

The lowered RCOF and slip risk found when participants walked with MPK joints is most likely due to the improved prosthetic limb control for MPK joint users.

Slip likelihood is dependent on many biomechanical factors of gait, such as foot-floor angle, heel velocity, step length, gait speed, etc. [10,11,12]. The advanced features of MPK joints allow users to more accurately control each step when ambulating, thus replicating a cautious and stable gait that is unlikely to lead to a slipping event. Additional biomechanical variables, such as the kinematics of the prosthetic foot and user postural control, will need to be examined using recorded motion capture data to confirm this reasoning.

Previous studies have examined RCOF values recorded at heel strike for amputee participants and found notably high RCOF results when compared to ablebodied subjects, as well as greater recorded values for the prosthetic limb compared to the intact limb [13,14].

While the total mean (standard deviation) prosthetic limb RCOF results of 0.176 (0.058) fall within the normal expected range for RCOF, it is important to note that the participants walked at a mean gait speed of 0.83 m/s during these walking trials. Normal gait speeds on level-ground range from 0.97 m/s to 1.51 m/s [11].

Considering the effect that slower gait speeds have on

35

lowering RCOF and slip potential [11], the results from this research support the literature with abnormally high RCOF results found for amputee subjects. When comparing the prosthetic (0.176 ± 0.058) and intact limb (0.176 ± 0.026) results, no difference in total mean RCOF was found. Further research is needed with younger participants walking within the normal range for gait speed to investigate this contradiction with the literature.

Limitations to consider in this study are the short (8week) accommodation period to the new prosthetic knee and the use of an assistive device while walking by two of the participants. An acclimation period of 4.5 months has been used in similar experimental protocols as the necessary duration of time to fully accommodate to a new prosthetic knee [9]. Larger variations in RCOF results can be seen for the subjects (S2 and S4) who walked with a cane on their intact limb side. Further research is needed to examine the impact that assistive devices have on the recorded RCOF for TFA participants.

5. CONCLUSIONS This study found a lowered risk of slipping based on RCOF results for participants walking with MPK joints compared to NMPK joints. These results support the literature with a potentially lowered risk of falling found for MPK joint users, further proving that advanced prosthetic technology may be necessary for improved prosthetic user safety and ambulation. This research provides further information on the biomechanical differences between MPK and NMPK joints during level ground walking in relation to slip risk. This knowledge can be used for the development of slip-prevention features in future prosthetic design. Given the small sample size of six older adult males only, further research is needed to confirm these findings in relation to the general lower limb prosthesis user population.

Additional known biomechanical parameters associated with slip-and-fall events (e.g., center of body mass, heel velocity, step length, and foot-floor angle) need to be investigated using recorded motion capture data to further assess any differences in slip severity or fall likelihood between the two prosthetic knee joint types [10,11,12].

6. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering Department of Bioengineering and the Office of the Provost at the University of Pittsburgh.

Thank you to the University of Pittsburgh Human Movement and Balance Laboratory and Department of Rehabilitation Science and Technology faculty and staff for providing additional resources used to support this project.

REFERENCES [1] M. Myers and B. J. Chauvin, “Above-the-Knee Amputations,” in StatPearls [internet]: StatPearls Publishing, 2023.

[2] J. Kim, M. J. Major, B. Hafner, and A. Sawers, “Frequency and circumstances of falls reported by ambulatory unilateral lower limb prosthesis users: a secondary analysis,” PM&R, vol. 11, no. 4, pp. 344353, 2019.

[3] V. Monaco, F. Aprigliano, L. Palmerini, P. Palumbo, L. Chiari, and S. Micera, “Biomechanical Measures for Fall Risk Assessment and Fall Detection in People with Transfemoral Amputations for the NextGeneration Prostheses: A Scoping Review,” JPO: Journal of Prosthetics and Orthotics, vol. 34, no. 3, pp. e144-e162, 2022.

[4] M. W. Whitmore, L. J. Hargrove, and E. J. Perreault, “Lower-limb muscle activity when walking on different slippery surfaces,” in 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015: IEEE, pp. 783-786.

[5] J. Yang et al., “The reaction strategy of lower extremity muscles when slips occur to individuals with trans-femoral amputation,” Journal of Electromyography and Kinesiology, vol. 17, no. 2, pp.

228-240, 2007.

[6] M. Vito et al., “Coupling an Active Pelvis Orthosis with Different Prosthetic Knees While Transfemoral Amputees Manage a Slippage: A Pilot Study,” in Wearable Robotics: Challenges and Trends: Proceedings of the 5th International Symposium on Wearable Robotics, WeRob2020, and of WearRAcon Europe 2020, October 13–16, 2020, 2022: Springer, pp. 53-57.

[7] V. Monaco et al., “An ecologically-controlled exoskeleton can improve balance recovery after slippage,” Scientific reports, vol. 7, no. 1, p. 46721, 2017.

36 Undergraduate Research at the Swanson School of Engineering

Ingenium 2024

[8] F. Zhang, S. E. D’andrea, M. J. Nunnery, S. M. Kay, and H. Huang, “Towards design of a stumble detection system for artificial legs,” IEEE transactions on neural systems and rehabilitation engineering, vol.

19, no. 5, pp. 567-577, 2011.

[9] K. R. Kaufman et al., “Gait and balance of transfemoral amputees using passive mechanical and microprocessor-controlled prosthetic knees,” Gait & posture, vol. 26, no. 4, pp. 489-493, 2007.

[10] K. E. Beschorner, D. L. Albert, and M. S. Redfern, “Required coefficient of friction during level walking is predictive of slipping,” Gait & posture, vol. 48, pp.

256-260, 2016.

[11] M. S. Redfern et al., “Biomechanics of slips,” Ergonomics, vol. 44, no. 13, pp. 1138-1166, 2001.

[12] B. Moyer, A. Chambers, M. S. Redfern, and R. Cham, “Gait parameters as predictors of slip severity in younger and older adults,” Ergonomics, vol. 49, no.

4, pp. 329-343, 2006.

[13] J. V. Durá, E. Alcántara, T. Zamora, E. Balaguer, and D. Rosa, “Identification of floor friction safety level for public buildings considering mobility disabled people needs,” Safety science, vol. 43, no. 7, pp. 407423, 2005.

[14] F. L. Buczek, P. R. Cavanagh, B. T. Kulakowski, and P. Pradhan, Slip resistance needs of the mobility disabled during level and grade walking. ASTM International, 1990.

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Ingenium 2024

Cell fate analysis of ovarian cancer in response to chemotherapeutic treatment Tyler Johnston1,2, Wayne Stallaert 2 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 1

Tyler Johnston

Wayne Stallaert

Tyler is a sophomore bioengineering student pursuing a minor in Chemistry and a certificate in Conceptual Foundations of Medicine.

His interest lays in the intersection between technology and medicine, and he plans to pursue an MD after graduation.

Dr. Stallaert is an Assistant Professor with the Department of Computational and Systems Biology.

He is fascinated by the interaction between a cell and its environment and the complex behaviors that emerge from this recursive relationship.

Significance Statement

Ovarian cancer treatment can lead to drug-resistant recurrent tumors. Understanding how individual cancer cells respond to treatment could reveal new therapeutic strategies to prevent tumor recurrence. This study models cell fate of ovarian cancer in response to chemotherapy. This modelling can be used in other cancers to cultivate similar understanding.

Category: Computational Research

Keywords: Ovarian cancer, fluorescence imaging, drug resistance

ABSTRACT Many first-line therapies for the treatment of ovarian cancer boast high initial remission rates, but also have high rates of treatment-resistant recurrent tumors. Cisdiamminedichloroplatinum (II) (cisplatin) exemplifies this pattern. High concentrations of cisplatin lead to cell death, while low concentrations lead to cell cycle arrest.

These arrested cells may re-enter the proliferative cell cycle and lead to recurrent tumors. In this project, multiplexed immunofluorescence imaging is used to obtain high-dimensional, single-cell molecular signatures of a cell population treated with cisplatin.

Manifold learning is then used to project these data into a two-dimensional representation, producing an interpretable “map” that visualizes the fate of cells following a variety of cisplatin concentrations and treatment durations. These maps not only illustrate this concentration-dependent variety of fates, but also suggest specific cellular mechanisms that determine these fates. Our findings suggest cell death results from errors in DNA synthesis during S phase and from mitotic catastrophe during M phase, both associated with an increase in cyclin-dependent kinase 2 (CDK2) activity. Findings also suggest cell arrest occurs during G2 phase in an effort to repair DNA damage, accompanied by a decrease in CDK2. This form of modelling may allow for advances in targeted therapies aimed at increasing CDK2 levels, to drive more cells towards death.

1. INTRODUCTION Ovarian cancer touches the lives of thousands across the globe, with over 300,000 diagnoses in 2020 alone [1]. One common and effective treatment for ovarian cancer is cis-diamminedichloroplatinum (II), or cisplatin.

Though treatment with cisplatin leads to remission in 80% of patients, 70% of those patients experience relapse [2]. The 1-year survival rate of ovarian cancer is over 70%, but the 10-year survival rate is just 35%, illustrating the failure of cisplatin in long-term treatment due to recurrence [3].

Recurrence is attributed to the effects of chemotherapies like cisplatin. When cells experience these treatments, a complex and poorly understood decision-making process leads the cell to either a state of cell cycle arrest, or programmed cell death [4]. By unearthing the fundamental principles which govern the fate decision, this process can be modulated to drive cells towards death — eliminating the ability of cancer cells to re-emerge from arrest and continue proliferating. Previous work has been done to analyze cell cycle effectors in many ovarian cancer samples [5] — however, these studies have focused on protein expression throughout tissues, which do not illustrate the single-cell protein dynamics that drive proliferation.

Through highly multiplexed immunofluorescence imaging, dimensionality reduction techniques, and manifold learning, the cell fate of ovarian cancer was

38

Ingenium 2024

mapped to analyze single-cell trajectories to arrest or death in response to cisplatin treatment. Trajectories uncovered by these maps have preliminary promise to understanding the mechanisms of proliferation effectors under pharmacologic stress and may open doors to targeted therapies.

2. METHODS 2.1 DATA PRODUCTION The OVCAR-8 cell line, derived from high-grade ovarian adenocarcinoma, was used as a human model cell line in this project. Cells were grown in RPMI Medium 1640, with 10% fetal bovine serum and 1% Penicillin/ Streptomycin (Gibco, USA) at 37ºC and 5% CO2.

Preliminary experiments were performed to optimize cell density, treatment, and plating conditions.

8-well µ-slides (Ibidi GmbH, Germany) were prepared with optimal conditions. Cells on these slides varied between three initial plating densities (25,000, 30,000, and 45,000 cells) and two concentrations of cisplatin — 1µM and 5µM. Treatment was administered either 3 or 5 days prior to fixation. A vehicle control well of dimethylformamide was also included.

After treatment, slides underwent chemical fixation with 4% paraformaldehyde and permeabilization with 0.1% Triton X-100. Slides were mounted in mounting media – composed of 50% glycerol with phosphate-buffered saline and 4% propyl gallate – after Hoechst staining.

Finally, iterative immunofluorescence staining was conducted. Stained samples were imaged using the Leica Biosystems Cell DIVE Multiplexed Imager, providing high resolution images of antibody staining over 30 cell cyclerelated biomarkers (TABLE 1) (FIGURE 1).

Antibody

Manufacturer

Catalog #

Dil.

Antibody

Manufacturer

Catalog #

Dil.

bCat

Millipore

C7738

1:50

p130

abcam

ab247453

1:50

CDC25C

abcam

ab205425

1:50

p16

abcam

ab192054

1:50

CDK2

R&D

AF4654

1:50

p21

CST

CST8587

1:50

CDK6

abcam

ab198946

1:200

p27

abcam

ab206927

1:30

cycA1

R&D

MAB7046

1:50

p65

CST

CST49445

1:50

cycB1

abcam

ab214381

1:200

pCDC6

abcam

ab247402

1:50

cycB2

abcam

ab250841

1:25

PCNA

CST

CST8580

1:50

cycD1

abcam

ab203448

1:50

PCNA

CST

CST82968

1:50

cycE1

abcam

ab194069

1:50

pH2AX

abcam

ab206900

1:50

cycE2

abcam

ab207336

1:50

pRB

CST

CST8974

1:50

Gem

abcam

ab225397

1:50

pS6

CST

CST5548

1:50

HES1

CST

CST21032

1:50

RB

CST

CST61121

1:50

LaminB1

abcam

ab194106

1:200

S6

CST

CST5548

1:50

NaKATPase

CST

CST99935

1:50

TNFa

Biolegend

502917

1:50

p-cJUN

CST

CST12714

1:50

YAP1

CST

CST53921

1:30

p-p130

abcam

ab284755

1:50

*Cell Signaling Technology (CST) Table 1: Complete list of antibodies used with their manufacturer information and dilution.

39

Figure 1: DAPI (pink) and ribosomal protein S6 (green) fluorescence signatures of OVCAR-8 staining overlayed, illustrating the types of raw images analyzed.

2.2 DATA ANALYSIS Image analysis was conducted in Python using raw images to provide a high-dimensional dataset of each cell’s molecular signatures for all tested antibodies.

Several deep learning algorithms were tested for singlecell segmentation; a custom Cellpose [6] model was chosen, trained on OVCAR-8 images in our dataset.

This segmentation was used to derive pixel-by-pixel intensity metrics of all biomarkers for each cell in raw fluorescence images, providing quantitative measures of protein localization and activity within each cell.

Potential of heat diffusion for affinity-based transition embedding (PHATE) [7], a non-linear dimensionality reduction algorithm, was employed to adapt the highdimensional dataset to an interpretable structure, positioning individual cells relative to others based on similarities in molecular and physical characteristics.

These structures — referred to as “cell fate maps” — conserve each cell’s quantitative profile, allowing for observation of protein dynamics within the population.

Iterations of maps were produced, varying based on different sets of input features and parameters, allowing for analysis of each map to produce an interpretable product.

3. RESULTS After hyperparameter optimization, the map in FIGURE 2 was produced to visualize the fate response to OVCAR-8 cells to cisplatin. A total of five treatment groups contributed to the production of this map, showing similar localization for similar treatment conditions.

Figure 2: Experimental populations projected onto the cell fate map. Higher concentrations of cisplatin are positioned to the right of the map, while lower concentrations are positioned to the left. Untreated cells are positioned at the top.

Overlaying the intensity values of cell cycle effectors on each cell of the map helped to identify the proliferation, arrest, and death trajectories of individual cells. FIGURE 3(A-D) shows the dynamics of cyclin D1, cyclin A1, and cyclin B1, which correspond to G1, G2, and M phase, respectively. Proliferating cell nuclear antigen (PCNA) represents S phase in which DNA is replicated.

Figure 3: Cell cycle effectors projected onto the cell fate map.

(A-D) Cyclin dynamics within the cell fate map. Red arrows illustrate changes within control cells, while green and blue arrows illustrate dynamics within low- and high-concentration cells, respectively. (E) Visualization of cells using nuclear staining.

Alongside the quantitative measures employed through projecting measurements onto the map, each cell’s location on the slide is retrievable. This provides visualization of cells within specific regions of the map.

FIGURE 3E illustrates the identification of mitotic cells with condensed chromatin, shown through the intense DNA content (top left of map). Likewise, an apoptotic cell was also identified on the opposite side of the cell fate map (bottom right of map).

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Additional proteins were found to be important in interpreting this map, shown in FIGURE 4.

Phosphorylated H2AX (pH2AX), a biomarker of doublestranded DNA damage, was highly upregulated in the high concentration group. Cyclin-dependent kinase 2 (CDK2), associated with proliferation promotion during several phases of the cell cycle, showed high levels in the high concentration group and low levels in the low concentration group.

Figure 4: pH2AX and CDK2 dynamics of cell fate map. (A-B) Increased DNA damage and CDK2 activity is observed within pre-apoptotic populations, with DNA damage and CDK2 activity decreasing in arrested cells.

5. DISCUSSION The unperturbed cell cycle can be used as a reference to analyze how drugs like cisplatin affect cancer cells.

The first deviation within the cell cycle occurs during S phase; as seen in FIGURE 3B, a population of PCNA-high cells diverges from the standard cycle accompanied by an increase in pH2AX, a marker of DNA damage (FIGURE 4A). The high cisplatin group dominates this divergent trajectory, which steadily decreases in PCNA activity until it reaches apoptosis as visualized above. This finding is consistent with known effects of cisplatin, which induces arrest in low concentrations and death in high doses.

A second divergence in the canonical cell cycle occurs during G2 phase, specifically within the cisplatin-low population. After DNA replication, cyclin A and cyclin D levels increase in the cell in the typical cell cycle. As seen in FIGURE 3(A-D), low treatment cells exit from this cyclin-high state to a state of low cyclin activity altogether. These cells are considerably larger than the untreated group and show a marked decrease in proliferative markers. This low-cyclin state is associated with cellular arrest, also known as senescence.

5. CONCLUSIONS Using multiplexed immunofluorescence imaging and dimensionality reduction to model single-cell trajectories and decisions through the proliferative cell cycle not only reproduces what is known about cisplatin’s effects on the cell but also offers unique insight to the dynamic paths cells can take through proliferation. Physical and genomic effects associated with cisplatin were apparent in this modelling, associating size and DNA damage with known effects of the drug. Experimentation to provide more evidence of the shown protein localization is currently being conducted through further multiplexed imaging, flow cytometry, and other biological measures.

Beyond success in modelling this complex decision, the alternative trajectories here show promise for targeted therapies which promote apoptosis and decrease senescence. In this population of cells, CDK2 activity was indicative of which fate a cell would choose, showing high activity in pre-apoptotic cells and low activity in senescent cells. Promotion of CDK2 would keep cells in this cyclin-high state, encouraging mitotic failure and pushing a larger proportion of cells towards death. To further understand the efficacy of CDK upregulation, multiplexed imaging and other biological experimentation with WEE1 (a CDK2 inhibitor) inhibition is being conducted.

6. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. Thank you to Dr. Wayne Stallaert for mentorship with every facet of this project.

Thank you to Dr. Katarzyna Kedziora for your extensive guidance with the computational aspects of this project. Thank you to Janet McLaughlin and Betsy Ann Varghese for your support with data production.

The final alternative trajectory found from the canonical cell cycle occurs during the mitotic division.

As seen by dynamics in cyclin B, the high cisplatin population can also exit the cell cycle — towards apoptosis — from a state of high cyclin B. These cells also show upregulation in CDK2 activity. The combination of high cyclin B levels, and high CDK2 activity, suggest cell death in the form of mitotic catastrophe in which the cell attempts mitosis and fails.

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REFERENCES [1] J. Huang et al., “Worldwide burden, risk factors, and temporal trends of Ovarian Cancer: a global study,” Cancers (Basel), vol. 14, no. 9, Apr. 2022.

[2] L. Yang et al., “Molecular mechanisms of platinumbased chemotherapy resistance in ovarian cancer (Review),” Oncology Report, vol. 47, no. 4, Apr. 2022.

[3] Cancer Research UK, “Ovarian cancer survival,” Cancer Research UK. [Online]. Available: https:// www.cancerresearchuk.org/about-cancer/ovariancancer/survival [Accessed Jan. 8, 2023].

[4] L. Galluzzi et al., “Systems biology of cisplatin resistance: past, present and future,” Cell Death Dis., vol. 5, e1257, 2014.

[5] A. Mitra et al., “In vivo growth of high-grade serous ovarian cancer cell lines,” Gynecologic Oncology, vol.

138, no. 2, pp. 372–377, Aug. 2015 doi: https://doi. org/10.1016/j.ygyno.2015.05.040.

[6] M. Pachitariu and C. Stringer, “Cellpose 2.0: how to train your own model,” Nature Methods, vol. 19, Apr., pp. 1634–1641, 2022.

[7] K. Moon et al., “Visualizing structure and transitions in high-dimensional biological data,” Nature Biotechnology, vol. 37, Oct., pp. 1482–1492, 2019.

42 Undergraduate Research at the Swanson School of Engineering

Triggerable dissolution of environmentally benign plastic in marine environments Samuel M. Landon1, Susan K. Fullerton- Shirey1,2, Eric J. Beckman1,3 Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 3 Mascaro Center for Sustainable Innovation, University of Pittsburgh, Pittsburgh, PA 1

Sam Landon is a Junior Chemical Engineering student at the University of Pittsburgh. During his time at Pitt, he worked in undergraduate research under Dr. Susan Fullerton as well as being part of the co-op program, working for BASF. Sam Landon

Susan Fullerton is an Associate Professor, Bicentennial Board of Visitors Faculty Fellow, and Vice Chair for Graduate Education in the Department of Chemical and Petroleum Engineering at the University of Pittsburgh. She earned her Ph.D. in Chemical Engineering at Susan Fullerton Penn State in 2009, and joined the Department of Electrical Engineering at the University of Notre Dame as a Research Assistant Professor. In 2015 she established the Nanoionics and Electronics Lab at Pitt as an Assistant Professor, and was promoted to Associate Professor with tenure in 2020. Fullerton’s work has been recognized by an NSF CAREER award, an Alfred P. Sloan Fellowship, a Marion Milligan Mason award for women in the chemical sciences from AAAS, and a Ralph E. Powe Jr. Faculty Award from ORAU. For her teaching, Fullerton was awarded the 2018 James Pommersheim Award for Excellence in Teaching in Chemical Engineering at Pitt. For more information: http://fullertonlab.pitt.edu/ Eric Beckman received his Ph.D. in polymer science and engineering from the University of MassachusettsAmherst in 1988. After postdoctoral research at Battelle’s Pacific Northwest Laboratory in 1987-88, Dr. Beckman assumed his faculty position at the University of Pittsburgh Eric Beckman (1989). Dr. Beckman was promoted to associate professor in 1994, and full professor in 1997. Dr. Beckman received a Young Investigator Award from the National Science Foundation in 1992, and the Presidential Green Chemistry Award in 2002. Dr. Beckman’s research group examines the use of molecular design to solve

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problems in green engineering and in the design of materials for use in tissue engineering. In 2003, with support from the Heinz Endowments, the Bevier estate, and John Mascaro, Dr. Beckman created the Mascaro Center for Sustainable Innovation, a school of engineering institute that examines the design of more sustainable products and infrastructure. Dr. Beckman’s group has published over 200 papers and he has received over 40 patents.

Significance Statement

As oceanic plastic waste continues to accumulate and decompose into toxic microplastics, the health of oceanic ecosystems is declining. To address this problem, biodegradable plastics have been explored; however, most are not designed to decompose in the ocean, resulting in microplastics – the same problem they were originally designed to solve. One alternative that can successfully decompose in the ocean is crosslinked sodium alginate.

Category: Experimental Research

Keywords: Biodegradable plastic, Crosslink, Sodium alginate, Triggerable dissolution

ABSTRACT The accumulation of single-use plastics in the ocean has been an area of environmental concern. As plastic degrades, it breaks down into microplastics, which are toxic to sea creatures that ingest them. Biodegradable alternatives have been explored, but they do not fully decompose in the ocean. One alternative is sodium alginate, a naturally occurring polymer that makes up sea algae, such as seaweed. Our team has shown that Ca2+-crosslinked, sodium alginate is a possible solution to this ocean plastic problem because it can safely decompose into benign components in the ocean. This research compares the degradation of two different methods of alginate crosslinking: uniform, where the crosslinker is distributed homogeneously throughout the film, and gradient, where the concentration goes from a high to low moving from the surface to the center of the film. Additionally, two crosslinkers are compared: Ca2+ to Sr2+. Our results show that gradient crosslinked samples release five times less Ca2+ ions in deionized (DI) water, and two times more than the uniform samples in oceanic conditions after five days.

This release of Ca2+ ions into solution correlates with the film’s degradation, making gradient films a more desirable biodegradable plastic. The film can more effectively survive benign conditions for a consumer, but experience more completely degrade once disposed of. It was also found that uniform Sr2+ crosslinked films release about three times less the amount of crosslinking ion than Ca2+ films in both benign and oceanic conditions after five days in solution.

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1. INTRODUCTION Modern petroleum-based, single-use plastics were designed to be both durable and profitable [1].

However, they turned out to be so durable that the use of these single-use plastics has become an environmental disaster – especially in aquatic environments such as the ocean. The ocean breaks apart these plastics into “microplastics” which are ingested by marine life, such as sea turtles [2], which can be detrimental to the organism’s health. Regardless of the species, ingestion of these microplastics can result in reduced fertility, stunted growth, and altered feeding patterns [3].

In recent years, environmentally friendly biodegradable alternatives have been introduced, such as polylactic acid (PLA). However, these biodegradable alternatives are designed to decompose in industrial composters, not in natural environments, such as the ocean. Thus, these biodegradable plastics cannot be the only solution to the global plastic crisis [4]. To address this issue, our team has proposed using solid films of sodium alginate as a biodegradable polymer, which maintains its properties in fresh water but readily degrades in saltwater. Unlike PLAs, the alginate is nontoxic once mechanically degraded.

Previous research surrounding sodium alginate has mostly focused on hydrogels (i.e., 90% water), particularly in tissue engineering and regenerative medicine [5]. Our work uses a different approach. A solid, dehydrated, form of sodium alginate is prepared, which can be handled similarly to traditional plastics.

Chemically, sodium alginate is a copolymer consisting of isomers α-L-guluronate (G) and β-D-mannuronate (M) [6]. To further strengthen alginate, the material is crosslinked with a divalent cation, such as Ca2+, which replaces the sodium ion. This crosslinking occurs at the GG units of the alginate, which has been called an “egg-box” formation (see Figure 1) [7]. Our team has shown that in the presence of a high concentration of monovalent cations (such as sodium in the ocean), the material can experience triggerable dissolution as the monovalent ions replace the divalent ions causing the film to dissolve.

Our lab uses two procedures to crosslink alginate: uniform and gradient crosslinking. To create uniformly crosslinked films, glucono-δ-lactone (GDL) is used to slowly release Ca2+ ions from CaCO3. To create the gradient crosslinked films, a non-crosslinked alginate film is dipped into a solution of CaCl2 in water. We hypothesized that the gradient crosslinked films would result in more desirable degradation characteristics than the uniform because the gradient crosslinking does not leave residual molecules in the film (e.g., CO3 and GDL). Additionally, they have a more lightly crosslinked interior, which will lead to faster dissolution in saltwater once the surface is compromised. Lastly, we address whether films can be crosslinked with divalent cations other than Ca2+ ions, such as Sr2+.

2. METHODS An alginate premix was made by mixing alginate powder (10 wt%) in DI water at low mixing speed.

Uniformly crosslinked films were made by mixing the alginate premix with a 2:1 molar ratio of GDL to CaCO3.

The amount of CaCO3 was adjusted to target different Ca2+ to alginate molar ratios. For example, a ratio of 1:8 (0.125) moles of Ca to alginate is abbreviated as 125X. This mixture was then drawn down into a thin film using a wire-wound stainless steel Mayer rod (size 75) and left to dry overnight at room temperature. To create gradient crosslinked films, the alginate premix was drawn down and dried. These non-crosslinked films were dipped into a 0.1 M CaCl2 crosslinking solution for 3, 30 or 300 seconds. Additionally, to make a uniformly crosslinked film with Sr2+ instead of Ca2+ the process is the same as described previously. The only difference is that instead of CaCO3, SrCO3 was used.

To simulate film degradation, 2 x 2 cm films were placed into a solution containing either DI or 3.5 wt% NaCl in water (saltwater), which is equivalent to the salinity of the ocean. The solutions were then placed on a shaker table at 100 rpm to mimic ocean waves. Three samples of each crosslink density were submerged in different solutions. Samples of these solutions were pulled periodically over five days. 1.0 M HNO3 was added to the samples prior to using ion-coupled plasma optical emission spectroscopy (ICP-OES) to ensure no undissolved alginate remains in the solution. ICP-OES measures the concentration of the crosslinking ion, and this technique is used to monitor its release, which is an indicator of film degradation.

Figure 1: Simplified representation of the “egg-box” model with Ca2+ crosslinking.

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3. RESULTS & DISCUSSION To test whether the gradient crosslinked films exhibit better degradation than the uniform films, two features are needed. First, the gradient needs to decompose faster in saltwater than the uniform. Second, the gradient should decompose more slowly than the uniform in DI water.

Figure 2.a shows that the 3-and 30-second gradient samples release about two times more Ca2+ once averaged after five days in solution. Additionally, the films degrade faster than the uniform samples.

Additionally, Figure 2.b shows that the 3-and 30-second gradient samples experience near zero degradation in the presence of DI, while the uniform samples release about five times the amount of Ca2+ after five days.

Thus, these two pieces of data show that the gradient crosslinked films exhibit improved degradation properties than the uniformly Ca2+ crosslinked films.

Figure 2: Ca2+ ion concentration versus time used to track film degradation in (a) saltwater and (b) DI water. Ca2+ concentration is normalized by alginate sample mass versus time. Filled symbols represent uniformly crosslinked while unfilled represent the gradient crosslinked samples.

Uniformly crosslinked film density is abbreviated with an “X” and the length of time a film spent being gradient crosslinked is displayed with its time (ex. 3 seconds).

Furthermore, different crosslinking densities result in different degradation characteristics. For example, in Figure 2.b, it is observed that the 125X released about 20% more Ca2+ in DI water than 375X over the first day.

This can be explained by the 375X having a stronger crosslinked structure. This stronger crosslinking prevents the Ca2+ from diffusing into solution, resulting in degradation. However, 500X released about 10% more Ca2+ than the 125X and 30% more than 375X in the same time period. This can be explained by the alginate being over crosslinked. When this occurs, the structure of alginate becomes brittle. This weakness of the 500X results in it more easily degrading in the DI, despite being more heavily crosslinked than the 125X. In addition to gradient versus uniform crosslinking, we also tested whether Sr2+ ions also experience triggerable dissolution, similarly to Ca2+ ions. Figure 3 shows the rate of Sr2+ release from a Sr2+-alginate film.

The samples placed in the saltwater released roughly five times more Sr2+ than the samples in the DI, until five days in solution, where it became about twice as much. This proves that divalent cations other than Ca2+ also show triggerable dissolution characteristics in saltwater. To compare the degradation of Sr2+ and Ca2+ crosslinked films, the magnitude of crosslinking ion released is observed. Figure 4 shows the release rate of the crosslinking ion into the solution. The Ca2+ films released more crosslinking ions in the presence of saltwater than the Sr2+ films. The Sr2+ films released less crosslinking ion in the presence of DI water than the Ca2+ films. This shows that the Ca2+ crosslinked films are more suited to decomposing in oceanic conditions, but the Sr2+ crosslinked films are better suited to withstand benign conditions. Additionally, a possible reason why Sr2+ films degrade slower than Ca2+ films is because Sr2+ has a higher affinity for alginate than Ca2+, which has been suggested by other sources as well [8].

Figure 3: Sr2+ ion concentration versus time used to track film degradation in saltwater and DI water. Sr2+ ion concentration in solution normalized by alginate sample mass versus time.

The samples are uniformly crosslinked at a crosslink density of 250X. The filled symbol represents the sample in saltwater while the unfilled represents the sample in DI water.

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REFERENCES [1] Y. Chen, A. K. Awasthi, F. Wei, Q. Tan, and J. Li, “Single-use plastics: Production, usage, disposal, and adverse impacts,” Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2020.141772 [2] Q. A. Schuyler, C. Wilcox, K. Townsend, B. D. Hardesty, and N. J. Marshall, “Mistaken identity?

Visual Similarities of marine debris to natural prey items of sea turtles,” BMC Ecology, https://doi. org/10.1186/1472-6785-14-14 Figure 4: Calcium and Sr2+ ion concentration versus time, which is used to track film degradation in saltwater and DI water. Ca2+ / Sr2+ concentration in solution normalized by alginate sample mass versus time. The samples are uniformly crosslinked at a crosslink density of 250X.

4. CONCLUSION This study compares the degradation of uniform and gradient crosslinked alginate films in salt water and DI water, and the results show that the gradient crosslinked samples exhibited improved degradation characteristics compared to uniform samples.

Additionally, Ca2+ is not the only ion that provides triggerable dissolution in salt water. Films crosslinked with Sr2+ ions also degrade in saltwater and experience little degradation in DI water. However, Ca2+ films are better suited for decomposing in saltwater while the Sr2+ films are better at withstanding degradation in benign conditions. Future experiments will focus on the gradient samples, because of their improved triggerable dissolution properties. More specifically, water vapor barrier tests and engineering the hydrophobicity of the films will be pursued.

5. ACKNOWLEDGMENTS Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh.

[3] V. G. Mason, M. W. Skov, J. G. Hiddink, and M. Walton, “Science of The Total Environment, https:// doi.org/10.1016/j.scitotenv.2022.157362 [4] T. P. Haider, C. Völker, J. Kramm, K. Landfester and F. R. Wurm, “Plastics of the Future? The Impact of Biodegradable Polymers on the Environment and on Society,” Angewandte Chemie, https://doi. org/10.1002/anie.201805766 [5] P. Matricardi, F. Alhaique, and T. Coviello, “Polysaccharide hydrogels: Characterization and biomedical applications,” CRC Press, 2016 [6] O. Smidsrød, R. Glover, and S. G. Whittington, “The relative extension of alginates having different chemical composition,” Carbohydrate Research, https://doi.org/10.1016/S0008-6215(00)82430-1 [7] G. T. Grant, E. R. Morris, D. A. Rees, P. J. Smith and D. Thom, “Biological interactions between polysaccharides and divalent cations: The egg-box model,” FEBS Letters, https://doi.org/10.1016/00145793(73)80770-7 [8] A. Haug and O. Smidsrød. “The effect of divalent metals on the properties of alginate solutions.” Acta chem. scand 19.2 (1965): 341–351.

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Materials for Solar Energy Technology: Cation Distribution and Behaviors of Spinel Oxide MnFe2O4 Hannah Levine1, Guofeng Wang1, Ying Fang1, Boyang Li1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 1

Hannah Levine

Boyang Li

Hannah Levine is a Junior from Montclair, New Jersey. She recently switched her major from Engineering Science to Computer Engineering and is pursuing the Innovation, Product Development, and Entrepreneurship Certificate. She is currently considering graduate school programs as well as working in industry.

She is excited to see what the world of engineering has to offer.

Boyang Li is a full-time research assistant at the University of Pittsburgh.

He received his Bachelor’s degree from the University of Science and Technology of China, then came to Pittsburgh and pursued his PhD in Materials Science at the University of Pittsburgh, which he finished in 2023.

Dr. Guofeng Wang is an Associate Professor in the Department of Mechanical Engineering and Materials Science. He has been developing and applying first-principles density functional theory-based multi-scale approach and novel computational algorithms to solve problems related Guofeng Wang to a broad range of materials, which include catalysts, nanostructured metals, and hightemperature alloys, as well as advanced manufacturing of these materials.

Ying Fang

Ying Fang is a fourth-year Ph.D. student in the Department of Mechanical Engineering and Materials Science at Pitt. She graduated with a Master’s in Computational Materials from Carnegie Mellon University in 2020. She has published several papers on machinelearning force fields, battery electrodes, and ferrite oxides.

Significance Statement

This study enhances our understanding of solar energy conversion catalysts by investigating the crystal structure and behavior of spinel oxides, focusing on MnFe2O4. By predicting their behavior under varying conditions using computational techniques and machine learning, this

Ingenium 2024

ABSTRACT This study provides insights into MnFe2O4 as an efficient catalyst for solar energy conversion through an investigation of the crystal structure of spinel oxides and their behavior under varying conditions.

The chemical stability, magnetic properties, and costeffectiveness of spinel oxides, particularly MnFe2O4, position them as promising candidates for sustainable energy solutions. After optimizing lattice parameters and creating various spinel structures with different degrees of inversion and cation distributions, we were then able to conduct Density Functional Theory calculations. This resulted in the equilibrium of energy values. We were also able to use MATLAB code to calculate the bond counts for each structure. The bond counts and equilibrium energy values were then used as inputs for the machine learning model and the Monte Carlo simulation. The machine learning code was developed based on a linear regression model, which gave predictions about the most likely equilibrium energy values. The Monte Carlo simulation works by switching two random cations in a structure and comparing its new equilibrium energy to that of the original structure. These simulations provided predictions about the behavior of MnFe2O4 at certain temperatures, which is crucial for its implementation as a catalyst for solar energy conversion.

1. INTRODUCTION Spinels with the chemical formula of AB2O4, otherwise known as spinel oxides, are known to be effective catalysts for solar energy conversion [2] [3] [5] [7].

This is largely due to their chemical stability [12] [1], narrow band gaps [5], magnetic properties [13], and low cost [2]. To use them effectively, scientists need to know how the molecules will behave in certain conditions, especially in high-temperature settings.

Many experiments have been performed with different kinds of spinel oxides (including spinel ferrites such as NiFe2O4, CoFe2O4, and Fe3O4 [3]), as well as computational simulations [8] [4]. Spinel ferrites refer to spinel oxides where the divalent cation is Iron (Fe).

These spinels are made up of unit cells with 32 oxygen atoms in cubic close packing, 16 octahedral sites and 8 tetrahedral sites occupied by the two different cations [12] [6]. In this paper, the specific spinel oxide that has been examined is Manganese Ferrite (MnFe2O4).

These nano-ferrites have a crystal structure with alternating tetrahedral and octahedral units that can accommodate Fe cations and Mn cations on the corners [15]. This arrangement allows the molecule to be more suitable as a catalyst for solar energy conversion.

Category: Computational Research

Keywords: Manganese Ferrite, Density Functional Theory, Cation Distribution, Linear Regression

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2. UNDERSTANDING & CREATING STRUCTURES: LATTICE PARAMETER OPTIMIZATION To create different configurations of the structures, the lattice parameter first needs to be calculated and optimized for each degree of inversion. To do this, one structure is created for each degree of inversion other than the Normal structure, which only has one possible cation distribution (pictured in Figure 1). The same calculations were done on the identical structures for each DOI to calculate the equilibrium energy value for the structure with various lattice parameters. The energy values that were calculated were compared, and the lattice parameter with the lowest equilibrium energy value was taken as the “optimized” value. Figure 2 shows the lattice parameter optimization for DOI = 1 (inverse structure). It is clearly shown that a lattice parameter of 8.55 yielded the lowest energy value, so all structures made for DOI = 1 were made with a lattice parameter of 8.55.

Figure 1. Cation Distribution for Normal Structure of MnFe2O 4 (purple represents Mn and gold represents Fe)

Figure 1 illustrates the unit cell for MnFe2O4 in its normal state (with a degree of inversion equal to 0).

This structure can have various degrees of inversion (DOI) that affect the structure in different ways [13] [14].

In the normal structure, the Manganese cations occupy only the tetrahedral sites, and the Iron cations occupy only the octahedral sites. This is clearly shown in Figure 1, where the Manganese cations are surrounded by four oxygen ions, and the Iron cations are each bonded to six oxygen ions. However, in the inverse structure (notated as DOI = 1), the Manganese cations and half of the Iron cations fill the octahedral sites, and the rest of the Iron cations are in the tetrahedral sites. These degrees of inversion change based on temperature and can drastically alter how the molecule behaves. To accurately predict how the degree of inversion changes with temperature, a data set of values with calculations (such as equilibrium energy and bond counts) was made. These calculations were done based on Density Functional Theory calculations [10] using the VASP software [11], as well as a MATLAB code to calculate the bond counts. These values created the data set used for a machine learning model and a Monte Carlo simulation to predict the molecule’s behavior in different temperatures.

Figure 2. Lattice Parameter vs Equilibrium Energy value for Inverse structure (DOI = 1). Lattice Parameter = 8.55 yields the lowest equilibrium energy value, making it the most stable.

3. DATASET BUILDING 3.1 DFT Based Calculations Once the lattice parameter values were set, the rest of the structures could be made. Using Python code, we made 15 different structures for DOI = 0.25, 0.5, 0.75, and 1. Since there is only one possible structure for DOI = 0 (Normal), the code used to create the other structures is not used for the Normal structure. The code was able to randomly distribute the cations to tetrahedral and octahedral sites (with many restrictions to ensure that the structure is stable). The output of the code is a file that is compatible with the VASP software that was then used to calculate the equilibrium energy values of each structure. These calculations were done based on Density Functional Theory (DFT). Figure 3 shows the average equilibrium energy values that were calculated for DOI = 0.25, 0.5, 0.75, and 1. The standard

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deviation values for each degree of inversion show that the values were close together, and Figure 8 shows that they were randomly distributed too. This randomness helps to confirm that each structure is unique, with its distinct cation distribution. Figure 1 shows a visual representation of the cation distribution for the only possible structure of DOI = 0.

Figure 3. Table shows the optimized lattice parameter, average equilibrium energy, standard deviation for energy values, and number of structures for each degree of inversion.

3.2 Calculating Bond Counts Along with the DFT calculations, the bond counts were also calculated. This represents the number of bonds that exist between certain permutations of the atoms.

The bonds are always cation - oxygen - cation, where either cation can be either Mn cation or Fe cation.

There are 23 different possible bonds, and the table in Figure 4 shows examples of what the bond counts could look like for different degrees of inversion.

The 0 values mean there is no bond in that structure.

Although there are two 0 values for DOI = 0.25, this is not always the case. In the structures that were calculated, some that were 0.25 degrees of inversion had 0, 1, or 2 Mn_O_Fe_7 bonds and had 0, 2, or 6 Mn_O_Mn_4 bonds. However, in every Inverse (DOI = 1) structure, every number of bonds would alter except for those marked as zeroes; those were consistently zero. The Normal spinel only has one possible structure, so that is the structure that is shown above.

The bond counts were calculated from a MATLAB code that takes each structure and counts the specific bonds.

It then outputs a 1x23 matrix, with the bond counts given in a predetermined order.

Figure 4. Bond counts of one structure for each degree of inversion (zero values highlighted)

4. PREDICTIONS USING MACHINE LEARNING With a combination of the data from the DFT calculations and the bond counts, the Monte Carlo simulations and machine learning models were able to make predictions.

4.1 Linear Regression Machine Learning Model The machine learning code was developed to create a linear regression model that took the information about each structure, calculated the most likely energy value, and compared it with the equilibrium energy value that was calculated with the DFT calculations. As well as giving a sanity check about the possibility of the calculated values, this also showed how possible these structures were to exist in real life. These clusters are clearly shown in Figure 5, which is the output of the linear regression simulation. These clusters of data were likely caused by repetitions in the bond count values, which will be explored in the Discussion section.

To avoid overfitting, machine learning code usually removes pieces of the dataset to use as “test data” and “validation data”. Although this can be effective, splitting the data into 3 parts reduces the sample amount for each set which can cause issues for smaller sets of data.

To solve this problem, a method called “crossvalidation” is used to validate the data. In cross validation, a test set is still held to the side, but a validation set is no longer needed. The basic approach (K-fold cross-validation) splits the data into some number K “folds”, where K depends on the size of the data. The model is trained for each fold K-1 as the training set for each fold K. This allows each set to be validated by the set before it. The resulting model is validated from the remaining data and the test set.

Figure 6 shows this process. This can be repeated for as many “folds” are necessary for the dataset, eliminating the issue of having the datasets be too small to return 49

accurate results. By using K-fold cross-validation, our linear regression model can be validated and accurate without the risk of improper data due to the size of the dataset.

For our Monte Carlo simulation, we were able to calculate the predicted degrees of inversion for MnFe2O4 at certain temperatures. This is incredibly important, as it allows us to see how the molecule will behave at certain temperatures (which is vital for solar energy conversion work). Figure 7 shows the output from the Monte Carlo simulation. It is clear from this graph that there is a distinct trend. This trend is similar to those that have been previously calculated.

Figure 5. Machine Learning Results: Linear Regression predicted values vs DFT calculated energy values.

Figure 7. Monte Carlo Simulation results (filled in) compared with results from previous research [4] (open circles and x marks).

5. DISCUSSION The calculated lattice parameter values were very close to, if not identical to, previously calculated and optimized lattice parameter values for MnFe2O4. There are many inconsistencies in publications when it comes to lattice parameter values.

Figure 6. Steps for K-Fold technique to validate results from machine learning.

4.2 Monte Carlo Simulation The Monte Carlo simulation works by taking the given information and accounting for the random variable, as well as previously given data (in this case, temperature).

Our code takes a given cation distribution and switches to random cations (in a way that is possible for the given temperature). It then compares what the new equilibrium energy of this structure would be to the original structure. If the new equilibrium energy value is less than the original, then that new value is kept.

If not, then the probability p=min[1, exp(-E/kBT)] is calculated to determine the acceptance or rejection of the new structure. If it is proven to be better, then the old one is tossed and the new one is kept as the standard. Otherwise, the old structure is used as the standard.

The calculated equilibrium energy values were very similar to calculations from published papers. Since the magnetic moment was fixed to be 40.00 J, the equilibrium energy values fell around -400 eV. Figure 9 shows the degree of inversion vs the change in equilibrium energy value concerning the normal. This increase is expected and aligns with past calculations of similar types [8]. These values were used for the DFT and bond count calculations, and the Monte Carlo simulation and linear regression model.

The calculations of the bond lengths had many repetitions. As stated earlier in the section that discussed the bond count calculations, there were 15 different structures for each DOI = 0.25, 0.5, 0.75, and 1.

Despite this, each DOI only had 10, 14, 12, and 8 distinct bond count permutations, respectively. For example, if each of the 15 structures were numbered 1-15, then for DOI = 1 structures numbered 6 and 9 were the same, as well as numbers 2, 3, 4, 11, and 14. Structures numbered 5 and 13 are the same, as well as 1 and 15.

This lack of diversity in the values was most likely the main contribution to the clusters of data in the linear regression model since the inputs consisted of the bond counts and the equilibrium energy.

50 Undergraduate Research at the Swanson School of Engineering

Ingenium 2024

The linear regression model showed distinct clusters of data, which could have been a result of the repetitions in the bond length data.

Although the bond counts for many of the structures were identical, the cation distributions were all different. Despite this, there is a clear correlation between the identical bond counts and their equilibrium energy values. Figure 8 shows the equilibrium energy values for the normal structure.

Any points that are colored the same had identical bond counts. Structures with the same bond count will yield similar equilibrium energy values. However, many energy values are close and give very different bond counts, showing that the correlation goes one way but not the other. In other words, any structures with the same bond counts are going to have similar energy values, but energy values that are close in value do not necessarily imply that the bond counts are the same.

For the Monte Carlo simulation results, it was mentioned that the trend that occurred was similar to those in results from other simulations in the past.

Figure 7 shows the results calculated by the simulation above compared to previously calculated values.

Although the trend follows previous values, there is a clear gap between the two calculations. This could be due to the repetitive nature of the values from this study.

6. CONCLUSION This study highlights the effectiveness of MnFe2O4 as a catalyst for converting solar energy. By exploring the unique crystal structure of spinel ferrites and their behavior in different conditions, valuable insights into the role of manganese ferrites in solar energy applications have been gained. The chemical properties and cost-effectiveness of these spinel oxides make them a viable option for sustainable energy solutions.

Through optimization of lattice parameters, creation of spinel structures for various degrees of inversion, and use of advanced computational techniques like Density Functional Theory (DFT), the molecule’s changes with temperature were able to be predicted and compared to previously calculated values. This knowledge is crucial for understanding how MnFe2O4 behaves under different circumstances. The machine learning models and Monte Carlo simulations further confirmed these predictions, providing a comprehensive understanding of the behavior and stability of MnFe2O4. This research not only contributes to the basic understanding of spinel oxides but also paves the way for future improvements in solar energy conversion systems. The ability to tap into solar energy using efficient catalysts like MnFe2O4 offers more possibilities for clean and renewable energy sources. With this computational and analytical approach, significant progress in uncovering the potential of spinel oxides in sustainable energy technologies has been made. These findings open up new avenues for further exploration and optimization of spinel oxides as catalysts, bringing us closer to a greener and more sustainable future.

Figure 8. Scatter plot for 15 different Normal structures of MnFe2O 4. The same colors show identical bond counts from MATLAB code.

Figure 9. Change in average equilibrium energy value (compared to the Normal) vs Degree of Inversion. Shows a clear parabolic path.

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7. ACKNOWLEDGMENTS Thank you to the MEMS FIRE program for allowing me to participate in undergraduate research through the Mechanical Engineering and Materials Science department at the University of Pittsburgh. Thank you to Dr. Whitefoot, Dr. Wang, Ying Fang, and Boyang Li for helping me through the research process.

REFERENCES [1] Long, Xing-Yu, et al. “Spinel-Type Manganese Ferrite (MnFe2O4) Microspheres: A Novel Affinity Probe for Selective and Fast Enrichment of Phosphopeptides.”

Talanta, vol. 166, 2017, pp. 36–45, https://doi. org/10.1016/j.talanta.2017.01.025.

[2] Malaie, Keyvan, et al. “Spinel Nano-Ferrites as LowCost (Photo)Electrocatalysts with Unique Electronic Properties in Solar Energy Conversion Systems.”

International Journal of Hydrogen Energy, vol. 46, no. 5, 2021, pp. 3510–3529, https://doi.org/10.1016/j. ijhydene.2020.11.009.

[3] Fegade, Umesh, et al. “ZnFe2O4 Spinel Oxide Incorporated Photoanodes as Light-Absorbing Layers for Dye-Sensitized Solar Cells.” Optical Materials, vol. 134, 2022, p. 113064, https://doi. org/10.1016/j.optmat.2022.113064.

[4] Jirák, Z., and S. Vratislav. “Temperature Dependence of Distribution of Cations in MNFE2O4.”

Czechoslovak Journal of Physics, vol. 24, no. 6, 1974, pp. 642–647, https://doi.org/10.1007/bf01587300.

[5] Ismael, Mohammed. “Ferrites as Solar Photocatalytic Materials and Their Activities in Solar Energy Conversion and Environmental Protection: A Review.” Solar Energy Materials and Solar Cells, vol. 219, 2021, p. 110786, https://doi.org/10.1016/j. solmat.2020.110786.

[10] Orio, M., Pantazis, D.A. & Neese, F. Density functional theory. Photosynth Res 102, 443–453 (2009). https://doi.org/10.1007/s11120-009-9404-8 [11] Liyanage, PhD, Laalitha S. I. “Running a DFT Calculation in VASP” ICME Fall 2012 Laalitha Liyanage. 2012, https://icme.hpc.msstate.edu/ mediawiki/images/d/d2/LS14\_VASP.pdf [12] Bragg, W.H. “XXX. The Structure of the Spinel Group of Crystals.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 30, no. 176, Aug. 1915, pp. 305–315, https://doi.org/10.1080/14786440808635400.

[13] Nepal, Roshan, et al. “Observation of Three Magnetic States in Spinel MnFe2O4 Single Crystals.”

Journal of Magnetism and Magnetic Materials, vol. 497, 16 Oct. 2019, www.osti.gov/pages/ servlets/purl/1802920, https://doi.org/10.1016/j. jmmm.2019.165955.

[14] Mounkachi, O., et al. “Origin of the Magnetic Properties of MnFe2O4 Spinel Ferrite: Ab Initio and Monte Carlo Simulation.” Journal of Magnetism and Magnetic Materials, vol. 533, Sept. 2021, p. 168016, https://doi.org/10.1016/j.jmmm.2021.168016.

Accessed 12 May 2022.

[15] Agusu, L., et al. “Crystal and Microstructure of MnFe2O4 Synthesized by Ceramic Method Using Manganese Ore and Iron Sand as Raw Materials.” Journal of Physics: Conference Series, 2019, www.semanticscholar.org/paper/Crystaland-microstructure-of-MnFe2O4-synthesizedAgusu-Alimin/81dd9ffb6ce20bf466dde8be091 2de390bb3b00f, https://doi.org/10.1088/17426596/1153/1/012056.

[6] “8.7: Spinel, Perovskite, and Rutile Structures.”

Chemistry LibreTexts, 28 Sept. 2021, https://www. chem.libretexts.org [7] Gobara, Heba M., et al. “Nanocrystalline Spinel Ferrite for an Enriched Production of Hydrogen through a Solar Energy Stimulated Water Splitting Process.” Energy, vol. 118, 2017, pp. 1234–1242, https://doi.org/10.1016/j.energy.2016.11.001.

[8] Wang, Guofeng, et al. “Relation between Cation Distribution and Chemical Bonds in Spinel NiFe2O4.” SSRN Electronic Journal, 2022, https://doi. org/10.2139/ssrn.4135347.

[9] Cuemath. “Standard Deviation - Formula, Definition, Methods, Examples.” Cuemath, www.cuemath.com/ data/standard-deviation/.

52 Undergraduate Research at the Swanson School of Engineering

Determining the effects of amino acid composition on the activity of laccasemimicking bionanozymes Zhehao Lia, Meng Wang b Department of Mechanical and Material Science, University of Pittsburgh, Pittsburgh, PA b Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA a

Zhehao Li is a senior Material Science and Engineering student at the University of Pittsburgh. His research interest involves chemical synthesis of functional materials. He plans to study material chemistry in his graduate school.

Zhehao Li

Meng Wang

Meng Wang is an assistant professor in the Department of Civil and Environmental Engineering. He received a PhD in environmental engineering from UCLA in 2018 and a BS in environmental science from Nanjing University in 2012.

His research interests include environmental biotechnology, biocatalysis, and synthetic biology.

Significance Statement

The challenge of efficiently degrading pollutants in the environment could be addressed by exploring novel amino acid-based bionanozymes. This research reveals that amino acid composition significantly impacts catalytic activity of bionanozyme, providing valuable insights into the design of biocatalysts for environmental applications.

Category: Experimental Research

Keywords: bionanozyme, nanomaterials, enzymatic activity, biodegradation

Ingenium 2024

ABSTRACT In the pursuit of effective biodegradation solutions, natural enzymes have gained attention for their catalytic abilities. However, their limitations, including cost and vulnerability, have led to the exploration of novel materials, such as amino acidbased bionanozymes. This study focuses on laccasemimicking bionanozymes, synthesized by selfassembling copper ions and three types of amino acids represented in laccase active center with different proportion. We investigate their catalytic activity on common substrates catalyzed by laccase including 2,6-dimethoxyphenol (2,6-DMP), 2,2’-azino-bis(3ethylbenzothiazoline-6-sulfonic acid) (ABTS), and 1-hydroxybenzo-triazole (HBT). Varying amino acid composition significantly impacts 2,6-DMP degradation, but it doesn’t predict their degradation on ABTS and HBT. This study encourages further exploration into the mechanisms of bionanozyme and the potential to advance biocatalysis for environmental applications.

1. INTRODUCTION Biodegradation has become a prominent approach for environmental engineers to address various environmental issues. In recent times, natural enzymes have emerged as invaluable biocatalysts in numerous degradation processes [1]. They offer distinct advantages, such as operating under milder reaction conditions, exhibiting good selectivity, and posing low toxicity risks [2]. Nevertheless, enzymes face limitations, including high production costs and susceptibility to extreme environmental conditions.

Thus, the development of novel materials remains essential to overcome these constraints.

One promising solution lies in drawing inspiration from protein structures, leading to the creation of amino acid-based bionanozymes. Compared to other catalysts, these bionanozymes have garnered significant attention due to their inherent enzymatic activity, enabling them to catalyze the conversion of substrates effectively, all while maintaining an environmentally friendly profile [3].

This study builds upon previous research of the laccase-mimicking bionanozyme by Makam et al. [4].

Laccases are a class of natural multicopper oxidases known for their ability to catalyze one-electron oxidation reactions on a wide range of substrates, including phenolic compounds, ABTS, and HBT [5]. To mimic activity of laccase, Makam et al. synthesized a 2D layered stacked bionanozyme named F-Cu, through the self-assembly of a specific type of amino acid phenylalanine (F) and copper ions. The resulting bionanozyme demonstrated impressive laccase-mimicking activity in the oxidation of phenolic pollutants and catecholamine neurotransmitters.

However, this bionanozyme failed to catalyze the oxidation of ABTS, which means it only has partial laccase-mimicking activity [4].

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Based on the structural study of laccase from Jones and Solomon, we found that additional amino acids, such as histidine (H) and cysteine (C), are also present within the active site of laccase [6]. We hypothesized that all amino acids within the active site collectively contribute to the laccase activity on various substrates. Our research aimed to synthesize copper complexes bionanozymes with varying proportions of phenylalanine, histidine, and cysteine, then explore their activity with 2,6-DMP (a phenolic compound), ABTS, and HBT.

2. METHODS Materials. L-phenylalanine (F), L-cysteine (C), L-histidine (H), copper chloride dihydrate, and 2,6-dimethoxyphenol (2,6-DMP) were purchased from Thermo Scientific. 2,2’-azino-bis(3ethylbenzothiazoline-6-sulfonic acid, (ABTS) was from Sigma. 1-hydroxybenzo-triazole (HBT) was from Chem-IMPEX International. Sodium chloride, potassium chloride, and potassium phosphate monobasic were purchased from Fisher. Disodium phosphate was from Sigma. Deionized water was used to prepare all the buffers and solutions.

Repeating the synthesis of F-Cu. Follow the steps in Makam’s paper [4].

Prepare Cu complexes bionanozymes. One volume of 5 mM CuCl2 was mixed with 2 volumes of 10 mM amino acid mixture with varying proportions of F, H, and C, including 100%F, 67%F+33%H, 67%F+33%C, 50%F+50%C, 50%F+50%H, 50%C+50%H, 33%F+67%C, 33%F+67%H, 33%F+33%C+33%H, 33%C+67%H, and 33%H+67%C (% represents molar percentage).

Fig. 1. UV-vis absorption spectra of 2,6-DMP with different amino acids composition in the bionanozyme.

3. RESULTS The degradation of 2,6-DMP revealed a clear dependence of the activity of copper complexes bionanozyme on the proportion of phenylalanine present, while the introduction of histidine and cysteine led to a decrease in the activity of bionanozyme. Eight samples exhibiting more pronounced color changes within an hour were selected for UV-vis absorption analysis (Fig. 1). The top three with the most significant color changes were collected to do the catalytic assay by Nanodrop spectrophotometer, and the kinetic information of these samples was presented in Fig. 2 and Table 1.

Activity test on ABTS. Firstly, ABTS (3 μL of 5 mg/mL stock) was added to the phosphate-buffered saline (1X, pH 7.4, 294 μL) and mixed with 3 μL bionanozyme solution, followed by monitoring the color change of the solution.

Activity test on HBT. First, 50 μL HBT stock was mixed with the PBS buffer. Then 8 μL bionanozyme was added and observed the color change.

Catalytic kinetic parameter. Different concentrations of 2,6-DMP were mixed with 0.0167 mM bionanozyme.

The reaction process was monitored at 469 nm using a Nanodrop, and absorbance values versus time were recorded for each substrate concentration. To convert the absorbance values (A) to concentrations (C), the Beer-Lambert Law was applied (A = εbC, molar absorptivity ε=27,500 M -1cm-1, path length b=1cm).

Concentration versus time plots were then generated, and linear fitting was performed on the data. The slope of the line obtained from the linear fit represents the initial velocity (V0) of the reaction. Next, plot the V0 versus different starting substate concentration and fitted to Michaelis-Menten equation to obtain the value of the maximum rate (Vmax) and Michaelis constant (KM) through prism. Then enzymatic kinetics (kcat) was calculated using formula kcat=Vmax ⁄ [E].

Fig. 2. The fitting enzymatic kinetic curve of the three samples with the best catalytic performance.

54 Undergraduate Research at the Swanson School of Engineering

Ingenium 2024

Catalyst

Vmax (mM s-1) KM (mM) kcat (mM-1 s-1)* kcat/KM

100%F

9.2x10 -5

2.6

5.5x10 -3

2.1x10 -3

67%F 33%C

2.5x10 -5

0.86

1.5x10 -3

1.7x10 -3

50%F 50%C

1.2x10 -5

0.72

7.2x10 -4

1.0x10 3

Table 1. Kinetic parameters of the three samples with the best activity for oxidizing 2,6-DMP. % represents the proportion of the amino acid in the bionanozyme. *The concentration of the catalysts is decided by the concentration of copper ions in the solution ([E] = 0.0167 mM).

In the case of the degradation of ABTS and HBT, none of the samples displayed any activity on these two substrates. This outcome suggests that the combination of amino acids and copper ions did not fully achieve the activity exhibited by natural laccases.

4. DISCUSSION We expect that by incorporating multiple amino acids found in the active center of laccase into our bionanozyme, it will closely resemble natural laccase, thereby inheriting its catalytic properties. But our experimental findings indicate that the catalytic performance of the laccase is not solely dependent on the presence of amino acids (F, H, and C) and copper ions. Enzymes possess a more intricate spatial structure, suggesting that the different peptide structures within enzymes may also contribute to their laccase activity on substrates such as ABTS and HBT. Additionally, during the catalysis of 2,6-DMP, a formation of red needle-like crystals at the bottom of the solution was observed, which deserves further exploration.

Our endeavor to synthesize bionanozyme crystals, with the aim of characterizing their inner structure

by Single Crystal X-Ray Diffraction, then figuring out how copper ions coordinate with the added amino acids through the crystal structure generated by the diffraction equipment, presents a challenging undertaking. Regrettably, we failed to crystallize the bionanozymes involved in the kinetic experiment. We think the introducing of multiple types of amino acids results in a more chaotic system in the bionanozyme, making it more challenging for crystallization compared to a system with a single amino acid. Given the complexity of achieving uniformity with multiple components in solution, this diversity significantly hinders the crystallization process. Furthermore, the functional groups present in a single amino acid are densely packed, leading to significant steric hindrance and resisting the formation of larger reticular structures, also inhibiting crystallization.

Consequently, the crystallization of our bionanozymes,

the copper complexes with multi-amino acids, become increasingly difficult. Due to the crystallization challenges associated with our bionanozyme, we are limited to studying it in a solution state. This constraint complicates the investigation of detailed structure and catalytic mechanisms. We only successfully obtained a new crystal with copper and 100% cysteine. However, this crystal demonstrated no laccase-mimicking activity on any substrates used in this study.

5. CONCLUSION We observed that variations in the composition of amino acids within the laccase-mimicking bionanozyme significantly influence its activity in the degradation of 2,6-DMP. The nanozyme composed entirely of phenylalanine exhibits the highest enzymatic activity, with a kinetic rate of approximately 5.5x10 -3 mM -1 s-1. However, these alterations do not determine the activity of our bionanozymes in the degradation of other substances that laccase can catalyze, such as ABTS and HBT.

6. ACKNOWLEDGMENTS We gratefully acknowledge the funding provided by the Swanson School of Engineering and the SURI project.

REFERENCES [1] S. J. Benkovic, “A Perspective on Enzyme Catalysis,” Science, vol. 301, no. 5637, pp. 1196–1202, Aug. 2003.

[2] M. A. Rao, R. Scelza, F. Acevedo, M. C. Diez, and L. Gianfreda, “Enzymes as useful tools for environmental purposes,” Chemosphere, vol. 107, pp. 145–162, Jul. 2014.

[3] H. Wei et al., “Nanozymes: A clear definition with fuzzy edges,” Nano Today, vol. 40, p. 101269, Oct. 2021.

[4] P. Makam, S. S. R. K. C. Yamijala, V. S. Bhadram, L. J. W. Shimon, B. M. Wong, and E. Gazit, “Single amino acid bionanozyme for environmental remediation,” Nature Communications, vol. 13, no. 1, Mar. 2022.

[5] E. I. Solomon, U. M. Sundaram, and T. E. Machonkin, “Multicopper Oxidases and Oxygenases,” Chemical Reviews, vol. 96, no. 7, pp. 2563–2606, Jan. 1996.

[6] S. M. Jones and E. I. Solomon, “Electron transfer and reaction mechanism of laccases,” Cellular and Molecular Life Sciences, vol. 72, no. 5, pp. 869–883, Jan. 2015.

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Generation of Fat-Cartilage Microphysiological Model as a New Tool to Study ObesityAssociated Osteoarthritis Celeste E. Lintza,b, Katelyn E. Lipaa,b, Meagan J. Makarczyka,b, Sophie E. Hinesa,b, Hang Lina,b Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA b Departments of Orthopaedic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA a

Celeste Lintz

Katelyn Lipa

Katelyn Lipa is a senior bioengineering student from Doylestown, PA. Her passions include tissue engineering, with a focus in mechanobiology and in vitro modeling. After graduation, she will be attending graduate school and pursuing a Ph.D. in Biomedical Engineering, to further her research and knowledge.

Dr. Hang Lin is an Associate Professor in the Department of Orthopaedic Surgery & Bioengineering at the University of Pittsburgh. He received his BS in Biochemistry from Nanjing University and his Ph.D. in Cell Biology from the Institute of Genetics and Developmental Biology of the Chinese Hang Lin Academy of Sciences. He has published over 90 peer-reviewed articles (H-Index: 43). Dr. Lin’s research goal is to apply the latest biological knowledge and state-of-art technology in orthopaedic research and translate the research findings into effective treatments for joint diseases. There are three integrated projects ongoing in his lab (https:// www.linlab.pitt.edu/): investigating the association between aging and osteoarthritis; establishing an in vitro microphysiological model for OA pathogenesis study and drug development; testing regenerative therapy for treating cartilage injury. Dr. Lin is the Grants & Program Director of the Orland Bethel Family Musculoskeletal Research Center (BMRC). He serves as the co-chair of the Orthopaedic Research Society (ORS) Biomaterials Topic and is a member of the Osteoarthritis Research Society International (OARSI) Communication Committee.

He is also an associate editor in Frontiers in Cell and Developmental Biology and an editorial board member of Osteoarthritis and Cartilage Open. He has received funding from the NIH, DoD, PA-CURE, and foundations to support his research.

Significance Statement

Meagan Makarczyk is a fourth-year Ph.D. student in the Department of Bioengineering. She completed her bachelor’s degree at Texas A&M University in Biomedical Engineering in 2020 and aspires to pursue a postdoctoral position and run her own lab studying musculoskeletal pain following graduation.

Recent findings support biochemical correlation between obesity and osteoarthritis. However, no current in vitro models recapitulate in vivo interaction between obese fat tissues and tissue elements, including cartilage. We demonstrate initial results for a novel fat-cartilage microphysiological system to investigate biochemical correlations between obesity and osteoarthritis and to test therapeutics.

Sophie Hines is a first-year bioengineering Ph.D. student at the University of Pittsburgh, with a specific interest in tissue engineering and regenerative medicine. Before her enrollment in the Ph.D. program, she completed her Bachelor’s at the Indiana University of Pennsylvania Sophie Hines in biology. After graduation, she started working as a lab tech in the Lin Lab while working towards a Master’s in biomedical sciences at the University of Pittsburgh School of Medicine. Sophie’s research interests involve understanding the differences between healthy and osteoarthritis-like aging of the different joint tissues, and how these changes can be used to create a better in vitro model for drug screenings.

Keywords: Osteoarthritis (OA), obesity, mesenchymal stem cells (MSCs), microphysiological system (MPS), miniJoint, adipose tissue (AT), cartilage tissue (CT), sodium palmitate, Urolithin A (UA)

Meagan Makarczyk

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Celeste Lintz is a senior undergraduate student majoring in Bioengineering with a concentration in Cellular Engineering. She is also obtaining minors in Chemistry and Public Service. Her research interests are in tissue engineering with an emphasis in in vitro disease modeling and regenerative medicine. Postgraduation, she will be pursuing a Ph.D. in Biomedical Engineering with continued work in the tissue engineering space.

Ingenium 2024

Category: Experimental Research

Abbreviations: Osteoarthritis (OA), human bone mesenchymal stem cells (MSCs), microphysiological system (MPS), adipose tissue (AT), cartilage tissue (CT), palmitic acid (PA), Hematoxylin and Eosin staining (H&E), Safranin O–Fast Green staining (SafO-FG), Urolithin A (UA)

Ingenium 2024

ABSTRACT Osteoarthritis (OA) is a debilitating joint disease leading to functional limitations and loss of patient independence. There are many risk factors for disease progression, including trauma, age, and obesity. Recent studies suggest there is a biochemical correlation between obesity and osteoarthritis mediated through the secretion of proinflammatory adipokines by hypertrophic obese adipose tissue; however, there are no current in vitro models that recapitulate in vivo interactions between obese fat tissues and other tissue elements. To investigate the joint microenvironment, the Lin Lab has developed the first in vitro multicomponent microphysiological system (MPS) to model OA pathology (miniJoint). In this study, we utilized a modified miniJoint to study the direct effect of obese-like adipose tissue on cartilage cytokine production. Cartilage and adipose dualflow bioreactors were connected for tissue-tissue crosstalk and supplemented with sodium palmitate, forming dysfunctional adipose. After “obese” MPS establishment, Urolithin A (UA), a promising agent in OA treatment, was introduced to elucidate the in vitro impact of therapeutics on an obese-osteoarthritic system. Preliminary results indicated a potential, but insignificant, increase in articular cartilage degradation markers, including matrix metalloproteinase-13 and a disintegrin and metalloproteinase with thrombospondin motifs-4 and 5, as well as proinflammatory cytokines interleukin-6 and 8, in cartilage bioreactors linked to dysfunctional adipose.

Interestingly, UA treatment partially reversed the dysfunctional fat-induced OA-like changes in cartilage, although no significant difference was found. These results provided initial information of the development of a novel fat-cartilage MPS for disease modeling, which will inform the future development of MPS to screen drugs for OA treatment.

1. INTRODUCTION As the most common chronic degenerative joint disease, osteoarthritis (OA) is a debilitating condition that alters total joint pathology [1]. OA progression leads to pathogenic changes to all joint elements, including synovitis, cartilage degradation, bone remodeling, and osteophyte formation [1]. Despite social prevalence, direct causation is unknown, and no disease modifying OA drugs have reached FDA approval [2]. There are many risk factors for disease progression, including trauma, age, and namely, obesity (Body Mass Index (BMI) ≥ 30).

Defined as a state of excess body fat associated with low-grade inflammation, obesity is a systemic disease impacted by hypertrophic adipose malfunction and proinflammatory adipokine secretion [3]. By 2030, roughly one in two United States citizens will be obese, and with the current obesity epidemic, there

is increasing interest in comorbidities with obesity [4]. Recent studies suggest a biochemical correlation between obesity and OA, in which proinflammatory adipokines secreted from hypertrophic adipose are also seen in osteoarthritic joints, including leptin, interleukin-8 (IL-8), and IL-6 [5].

In vitro models and microphysiological systems (MPSs) have emerged as methods for the development of physiologically-relevant systems that attempt to recapitulate disease progression [6]. Compared to current in vivo studies, MPSs can serve as a progressive measure of human tissues outside of animal models to investigate disease pathology and test therapeutics with higher throughput and shorter culture times, a major advantage to drug development [8]. Due to a lack of in vitro models focusing on obesity-associated OA, there is a clinical need to establish physiologically relevant MPSs that accurately mimic joint-tissue crosstalk in OA pathology to study disease progression.

The Lin Lab has developed the miniJoint – a multicomponent MPS that utilizes human bone mesenchymal stem cells (hBMSCs) to create joint tissues [7]. We hypothesize that modifications of the miniJoint can be used to establish a fat-cartilage MPS that focuses on the effect of obese-like adipose tissue on cartilage cytokine secretion. The objective of this study is to define the biochemical correlation between obesity and OA and to test for potential therapeutics, specifically Urolithin A (UA). UA is a natural metabolite previously shown as a promising therapeutic agent in OA treatment [8]. In this study, sodium palmitate, a salt of palmitic acid (PA), is hypothesized to promote obese-like fat modeling due to its ability to induce hypertrophy and increase lipid droplet size in hBMSCs [9].

2. METHODS 2.1 Differentiation of hBMSCs into adipose and cartilage The process of fabricating the dual-flow bioreactor of the MPS was from the previous methods in generating the miniJoint [11]. To culture the tissues, passage 5 (P5) hBMSCs were encapsulated in 15% methacrylated gelatin (GelMA) scaffolds and photocrosslinked with UV light at 395nm for two minutes. Inserts were placed in the bioreactors supplemented with differentiation mediums (Figure 1a). Mediums were delivered via syringe pumps at a pulse flow rate of 20s fast flow at 12 µL s−1 and 3580s slow flow at 1/60 µL s−1, provided by a programmable syringe pump (Lagato210P, KD Scientific, Holliston, MA). Adipogenic medium consisted of DMEM/F12 (Fisher Scientific, St. Louis, MO), 10% FBS (Sigma-Aldrich, St. Louis, MO), 1% Antibiotic-Antimycotic (Sigma-Aldrich), 0.1% IBMX (Sigma-Aldrich), 0.1% Dexamethasone (Sigma-Aldrich), and 0.1% ITS+ (Fisher Scientific). Chondrogenic medium was made with DMEM/F12 (Fisher Scientific) supplemented with 1%

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FBS (Sigma-Aldrich), 1% 1µg/mL ITS+ (Fisher Scientific), 0.1% proline (Sigma-Alrich), and 0.01% 0.25µM Dexamethasone (Sigma-Aldrich). Initial differentiation lasted for 28 days with media change periods of 3 days.

2.2 Supplementing Adipogenic Medium with Sodium Palmitate After initial differentiation, adipose bioreactors were supplemented with sodium palmitate to induce obeselike changes in fat for 14 days. The PA, 200mM Sodium Palmitate (Sigma-Aldrich), was made using a 70% methanol solvent, which was dissolved in a 50°C oven for an hour. The concentration of sodium palmitate was 0.2% added to the adipogenic medium with 1g/100mL bovine serum albumin (BSA) (Sigma-Aldrich), with the mixture being placed in an incubator and shaken at 37°C until homogenously dissolved, then filtered.

2.3 Assembly of the fat-cartilage MPS Two bioreactors were connected in series to establish unidirectional crosstalk from the adipose bioreactor to the cartilage (Figure 1b). Shared bottom flow between adipose and cartilage tissues occurred for 28 days. The shared medium was used to simulate the “synovial fluid” (SF) and consisted of phenol-red free DMEM (Fisher Scientific) supplemented with 1% AntibioticAntimycotic (Sigma-Aldrich), 1% ITS+ (Fisher Scientific), 1% 100 mM Sodium Pyruvate (Fisher Scientific), and 0.01% proline (Sigma-Aldrich). To maintain the cartilage phenotype in the cartilage tissue, cartilage bioreactors were also provided with chondrogenic medium as the top-flow medium source. Adipose tissue bioreactors received either control or PA-added medium.

2.3 Assess Impact of Experimental Therapeutic Target

2.4 Methods for Analysis Tissues were collected for cytokine analysis via RNA extraction with the purpose of reverse transcriptionquantitative polymerase chain reaction (RT-qPCR) and for histological analysis. RNA extraction was completed using a Qiagen RNeasy Plus Universal Kit (Qiagen, Germantown, MD). qRT-PCR was completed with a QuantStudio 5 Real-Time PCR System (Applied Biosystems, Foster City, CA), using 384-well plates with SYBR green chemistry, GAPDH housekeeping gene, and 2-ΔΔCt (with Shapiro-Wilks Normality and Student’s T-Test).

Adipose tissue was collected for 4% paraformaldehyde fixed, sucrose-cryoprotected tissue freezing, and the cryosection samples were stained via Hematoxylin and Eosin staining (H&E). Cartilage tissue was collected for ethanol dehydration and paraffin embedding, and the paraffin sections were stained via Safranin O–Fast Green staining (SafO-FG). Images were taken using an Olympus SZX16 Stereo Microscope (Olympus, Waltham, MA).

3. RESULTS The presented findings are preliminary and represent initial results to a novel fat-cartilage MPS to study obesity-associated OA. To development of obese phenotype in the adipose treated with PA, adipose tissues (AT) was first analyzed via H&E histology and qRT-PCR. As visualized in the H&E staining, PA-exposed AT displayed developmental changes, including increased lipid droplet diameter and increased quantity of lipid droplets. This indicates potential adipocyte hypertrophy and hyperplasia, two characteristics of dysfunctional obese fat.

To test the therapeutic efficacy of this system, an MPS with an PA-added adipose tissue group was supplemented with UA on day 63 of the study and treatment lasted until day 70. The UA was administered to all medium streams to mimic the systemic drug delivery commonly associated with in vivo transmission of the drug to knee joints.

Figure 1: (a) Dual Flow Bioreactor for 4hBMSC gel inserts. (b) Microfluidic syringe flow showing the crosstalk being adipose (AM) and cartilage (CM) via common medium simulating “synovial fluid” (SF)

Figure 2: G1 – Adipose: G2 – Adipose + PA; G3 – Adipose + PA +UA (a) H&E Staining of Adipose Tissue Samples at 20x. (b) RT-qPCR results of Top-Flow Adipose Tissue Samples.

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Representative qRT-PCR analysis of the obese-like AT suggested an influence in key markers of adipose dysfunction seen in obese individuals (Figure 2b).

PPARγ is the master regulator of adipogenesis, which is seen to increase in obese AT, while adiponectin is a regulator of inflammation that enhances cellular response to insulin, which decreases in obese AT. At first glance, graphical representation of genetic markers showed a trend of upregulation of PPARγ and downregulation of adiponectin, hinting that the sodium palmitate induced an obese-like phenotype. However, our results showed insignificant correlation (α > 0.05), as well as inconclusive results with the regulation of leptin, the master regulator of energy expenditure in adipose tissue. This in an indication of error and confirms the need for additional testing.

Next, we determined if there were any changes in the cartilage tissue (CT) exposed to different adipose conditions. CT connected to PA-exposed AT saw insignificant but promising cytokine changes. While SafO-FG revealed inconclusive results to cartilage degradation in the CT exposed to obese-like AT due to issues with dye saturation (Figure 3a), qRT-PCR hinted the development of OA phenotype. While correlation did not reach significance (α > 0.05), graphical trends visually suggested that CT connected to PA-exposed AT had an upregulation of articular cartilage degradation markers, including matrix metalloproteinase-13 (MMP13), a disintegrin and metalloproteinase with thrombospondin motifs-4 (ADAMTS4), and ColX, and of proinflammatory cytokines, including interleukin-6 (IL-6) and IL-8. Treating the MPS with UA graphically appeared to lower the presentation of OA phenotype, displaying gene expression levels and cartilage formation similar to those without PA exposure.

However, this was also insignificant and displayed high variability.

Figure 3: G1 – Cartilage (connected to Adipose); G2 – Cartilage (connected to Adipose + PA); G3 – Cartilage + UA (connected to Adipose + PA + UA) (a) SafO-FG Staining of Cartilage Tissue Samples at 20x. (b) RT-qPCR results of Bottom-Flow Cartilage Tissue Samples.

4. DISCUSSIONS Preliminary results from the creation of a fat-cartilage MPS utilizing unidirectional crosstalk and sodium palmitate demonstrates the potential to study obesity-associated OA in vitro. There were high levels of variability in cytokine expression, but overall graphical representations and visuals reflected recent studies of obesity-associated OA. Recent studies show that regulation of markers of low-grade inflammation are seen in obese AT with high amounts of PA, which is consistent with upregulation of markers like PPARγ, IL-6, and leptin and downregulation of markers like adiponectin [10]. Further study of hBMSC adipogenesis with PA-supplemented adipogenic medium is needed to confirm statistically significant gene modulation in our MPS. In a recent study, pro-MMP13 levels in overweight (BMI>25) females undergoing primary total knee arthroplasty due to OA tended to be higher in synovial fluid in the more advanced stages of disease progression (p = 0.0882) [11]. Another study observed a mediating relationship of leptin and obesity in the activation of cartilage degradation markers, markedly ADAMTS4 and ColX [12]. Our initial results imply this relationship visually with increase of these markers, but additional quantitative analysis to confirm statistical significance is necessary.

UA’s potential for an anti-inflammatory impact in inflammation is reflected in recent studies with mice models of obesity and colitis, demonstrating a decrease in IL-1β, IL-6, and tumor necrosis factor-alpha (TNF-α) [13]. In our study, while insignificant, the UA-treated MPS visually appeared to regain control expression levels, but no significance was reached.

While this MPS was cultured for a lengthy 70 days, there is proven utility of this MPS. Previous models of obesity-related post-traumatic OA have been mediated through mice models, with current methodologies lasting up to 22 weeks [14]. In vitro MPSs have the potential to cut this experimentation timeline in half for pre-clinical study of therapeutics, allowing for a higher throughout system.

Additional engineering of the fabrication of the bioreactor model should be conducted to confirm statistical significance in results. For instance, there were heavy concerns of media leakage through the bioreactors during experimentation, which potentially altered the variability in results.

Troubleshooting of bioreactor-insert ‘fit’ should be conducted to ensure media leakage does not occur.

This involves modification to the computer-aided design (CAD) modeling of the bioreactor system and fabrication methods of the miniJoint. Once biochemical correlations are confirmed in the MPS, future iterations include the introduction of immune cells such as macrophages to understand the effects of immune cell-mediated inflammation on the CT 59

consistently seen in the OA pathology. Additionally, mechanical compression and loading of the system should occur to represent weight fluctuation in obesity and bidirectional crosstalk between tissue types for compounding cytokine interaction.

[8] Y. He, L. Yocum, P. G. Alexander, M. J. Jurczak, and H. Lin, “Urolithin A Protects Chondrocytes From Mechanical Overloading-Induced Injuries,” Front Pharmacol, vol. 12, p. 703847, 2021, doi: 10.3389/ fphar.2021.703847.

5. CONCLUSIONS

[9] G. Pratelli et al., “Hypertrophy and ER Stress Induced by Palmitate Are Counteracted by Mango Peel and Seed Extracts in 3T3-L1 Adipocytes,” Int J Mol Sci, vol. 24, no. 6, Mar 12 2023, doi: 10.3390/ ijms24065419.

In this study, we attempted to engineer a fat-cartilage MPS to study obesity-associated OA in vitro with preliminary results. With future iterations of this MPS, we can elucidate the biochemical correlations between obesity and OA. The creation of an obese fat-cartilage MPS can serve as a tool to screen drugs for the treatment of these comorbidities with systemic drug delivery modeling.

6. ACKNOWLEDGEMENTS This work was carried out with the support, funding, and resources at the Swanson School of Engineering at the University of Pittsburgh and the Lin Laboratory.

REFERENCES [1] Y. He et al., “Pathogenesis of Osteoarthritis: Risk Factors, Regulatory Pathways in Chondrocytes, and Experimental Models,” Biology (Basel), vol. 9, no. 8, Jul 29 2020, doi: 10.3390/biology9080194.

[2] M. J. Makarczyk et al., “Current Models for Development of Disease-Modifying Osteoarthritis Drugs,” Tissue Eng Part C Methods, vol. 27, no. 2, pp.

124–138, Feb 2021, doi: 10.1089/ten.TEC.2020.0309.

[3] M. S. Ellulu, I. Patimah, H. Khaza’ai, A. Rahmat, and Y. Abed, “Obesity and inflammation: the linking mechanism and the complications,” Arch Med Sci, vol. 13, no. 4, pp. 851–863, Jun 2017, doi: 10.5114/ aoms.2016.58928.

[4] Z. J. Ward et al., “Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity,” N Engl J Med, vol. 381, no. 25, pp. 2440–2450, Dec 19 2019, doi: 10.1056/NEJMsa1909301.

[10] T. Qiu et al., “Obesity-induced elevated palmitic acid promotes inflammation and glucose metabolism disorders through GPRs/NF-kappaB/KLF7 pathway,” Nutr Diabetes, vol. 12, no. 1, p. 23, Apr 20 2022, doi: 10.1038/s41387-022-00202-6.

[11] J. Jarecki et al., “Concentration of Selected Metalloproteinases and Osteocalcin in the Serum and Synovial Fluid of Obese Women with Advanced Knee Osteoarthritis,” Int J Environ Res Public Health, vol. 19, no. 6, Mar 16 2022, doi: 10.3390/ ijerph19063530.

[12] A. Fowler-Brown et al., “The mediating effect of leptin on the relationship between body weight and knee osteoarthritis in older adults,” Arthritis Rheumatol, vol. 67, no. 1, pp. 169–75, Jan 2015, doi: 10.1002/art.38913.

[13] A. M. Toney, R. Fan, Y. Xian, V. Chaidez, A. E. RamerTait, and S. Chung, “Urolithin A, a Gut Metabolite, Improves Insulin Sensitivity Through Augmentation of Mitochondrial Function and Biogenesis,” Obesity (Silver Spring), vol. 27, no. 4, pp. 612–620, Apr 2019, doi: 10.1002/oby.22404.

[14] T. Xiong et al., “N-3 polyunsaturated fatty acids alleviate the progression of obesity-related osteoarthritis and protect cartilage through inhibiting the HMGB1-rage/tlr4 signaling pathway,” International Immunopharmacology, vol. 128, p.

111498, Feb 2024. doi:10.1016/j.intimp.2024.111498

[5] T. Wang and C. He, “Pro-inflammatory cytokines: The link between obesity and osteoarthritis,” Cytokine Growth Factor Rev, vol. 44, pp. 38–50, Dec 2018, doi: 10.1016/j.cytogfr.2018.10.002.

[6] G. Saorin, I. Caligiuri, and F. Rizzolio, “Microfluidic organoids-on-a-chip: The future of human models,” Semin Cell Dev Biol, vol. 144, pp. 41–54, Jul 30 2023, doi: 10.1016/j.semcdb.2022.10.001.

[7] Z. Li et al., “Human Mesenchymal Stem Cell-Derived Miniature Joint System for Disease Modeling and Drug Testing,” Adv Sci (Weinh), vol. 9, no. 21, p. e2105909, Jul 2022, doi: 10.1002/advs.202105909.

60 Undergraduate Research at the Swanson School of Engineering

Cognitive Perseveration Is Not Associated with TrainingInduced Improvements in Motor Perseveration In Older Adults Shaoyi Liu1,2, Shuqi Liu1,3, Gelsy Torres-Oviedo1,3 Sensorimotor Learning Laboratory, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 2 Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 3 Center for the Neural Basis of Cognition, Pittsburgh, PA 1

Shaoyi Liu

Shaoyi Liu is a junior at the University of Pittsburgh, majoring in neuroscience with a minor in bioengineering. His passion lies in decoding biological data to uncover valuable insights. Postgraduation, Shaoyi plans to pursue graduate studies, focusing on system neuroscience, computational neuroscience, or neural engineering.

Shuqi Liu received her undergraduate degree in Mathematics and Computer Science from Centre College. Shuqi is pursuing her Ph.D. in Bioengineering with a focus on the Neural Engineering track. Shuqi is interested in understanding the impact of aging on motor learning, gait Shuqi Liu automaticity, and mobility. Her current project involves using functional near-infrared spectroscopy (fNIRS) to study prefrontal cortex engagement during walking.

She is interested to see if interventions like the splitbelt treadmill can improve community mobility in older adults.

Gelsy Torres-Oviedo

Prof. Torres-Oviedo started her faculty position in the Department of Bioengineering at the University of Pittsburgh in 2012. Prior to that, she completed her postdoctoral training in Neuroscience at the Johns Hopkins School of Medicine and the Kennedy Krieger Institute. She received her PhD in Biomedical Engineering at Georgia Tech and Emory University in 2007 and she graduated from the University of Texas at Austin in 2001 with a B.S. in Physics.

Dr. Torres-Oviedo is interested in understanding learning mechanisms underlying the adaptation of gait and how to stimulate them to rehabilitate the gait of patients with cortical lesions. She uses psychophysical experiments and computational tools for investigating how prior motor experiences influence how we learn and how we generalize new motor patterns to novel situations.

Ingenium 2024

Significance Statement

Overlap of neural structures that control cognitive and motor actions increases in older adults. Prior study has shown that older adults have correlated motor and cognitive perseverations. However, older adults remain plastic and can improve their motor perseveration with training. Yet it remains unknown if the same neural structures controlling cognitive and motor perseveration also contribute to training-based motor perseveration improvements. Investigating this is meaningful because the cognitive task could be a potentially cost-effective alternative to assess the potential of older adults to improve motor perseveration with training such that the training could be targeted to older adults who are most likely to benefit.

Category: Experimental Research

Keywords: Motor perseveration, Cognitive perseveration, Aging, Locomotion, Switching, Splitbelt Treadmill

ABSTRACT Perseveration is the inability to switch between tasks and could occur in humans’ motor and cognitive functions. Greater motor and cognitive perseverations are commonly observed with aging. A prior study has demonstrated that motor and cognitive perseverations were correlated in older adults, which implies that the neural processes responsible for motor and cognitive perseverations may overlap with aging. Motor perseveration could improve through training utilizing devices like a split-belt treadmill, where each leg moves at a different speed. However, it is unknown if traininginduced improvement in motor perseveration is also associated with cognitive perseveration as people age. There is interest in exploring the relationship between motor perseveration, its improvement, and cognitive perseveration. This is because cognitive assessments offer a simple and cost-effective means of predicting motor perseveration and the effectiveness of training, eliminating the need to invest in costly and complex devices. We hypothesize that cognitive perseveration would be positively correlated with motor perseveration and its improvements with training. To test the hypothesis, we evaluated cognitive perseveration using a card matching task, and measured motor perseveration before and after exposures to a switching training where participants repeatedly changed walking patterns between a splitbelt treadmill and overground walking contexts. We found that training improved motor perseveration in older adults, but neither motor perseveration nor its improvement was significantly correlated with cognitive perseveration. The results imply that the neural processes responsible for improving motor perseveration may not be unified with those involved in cognitive perseveration.

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1. INTRODUCTION

2. METHOD

Aging results in declined motor and cognitive functions in humans, including greater cognitive and motor perseveration [1-3]. Cognitive perseveration occurs when an individual is “stubborn” and unable to switch from a previous thinking strategy when a new one is required [2]. For example, if a person were to play two different games of poker and cannot switch the rule out of the first game when playing the second one, the person would be perseverating cognitively. Motor perseveration occurs when an individual fails to switch between distinct motor patterns to account for changes in the environment (e.g., when switching from walking on an icy to a concrete surface), which could contribute to an increased risk of falls [2].

We evaluated cognitive and motor perseveration in 22 older adults (12 females and 10 males; age: 71.88 ± 3.06 years old). All the 22 participants were free of neurological diseases. One participant dropped out after the first session, resulting in n=21 for the analysis of the change in motor perseveration.

A previous study showed that there is an association between age-related cognitive perseveration and motor perseveration [2], suggesting an overlap in the neural processes responsible for motor and cognitive perseverations with aging. Furthermore, switching training (repeated transitions between two different walking contexts) has been shown to improve motor perseveration [2]. However, it is unknown if the improvement in motor perseveration is also related to cognitive perseveration in older adults. Measuring motor perseveration and performing switching training requires the application of costly laboratory-based devices such as a split-belt treadmill, a treadmill that could drive the two legs at different speeds. The split-belt treadmill is effective at evaluating motor perseveration because it can impose a change in the walking context (e.g., two legs moving at different speeds on a treadmill vs overground walking) and require participants to learn and switch their walking patterns. Establishing a relationship between improvement in motor perseveration and cognitive perseveration could be beneficial. This is because the cognitive perseveration test, such as a simple card matching task that only requires a computer or laptop with a screen and keyboard, is an easy-toadminister and cost-effective alternative to evaluate older adults’ motor perseveration and its potential to improve through training. We hypothesize that the neural processes controlling cognitive perseveration, motor perseveration, and the improvements in motor perseveration from switching training are unified in older adults. We expect that cognitive perseveration will be positively correlated with training-induced improvements in motor perseveration.

The study included four visits, each separated by one or two days (Figure 1). The first visit evaluated cognitive perseveration, the second and fourth sessions measured motor perseveration pre- and post-training, and the third visit included switching training.

Figure 1: Experimental paradigm. There are four visits in total for each participant. The first session measured cognitive perseveration. The third session is the switching training where participants experience multiple transitions between split-belt treadmill (grey) and overground (white) walking.

The second and the fourth sessions are pre- and posttraining motor perseveration assessment sessions. Motor perseveration (red box) was measured when each participant experienced context changes from split-belt treadmill (context 2) to overground (context 1) in visits 2 and 4.

Cognitive perseveration on the first visit was measured by a modified computer-based Wisconsin Card Sorting Test. During each trial, a single reference card was shown at the top of the screen and three answer cards were displayed below. Participants were asked to select one of the three cards to match to a reference card based on either the color (red, blue, green, or yellow), shape (squares, circles, triangles, or plus signs), or number of objects (1, 2, 3, or 4 items). Participants were instructed to guess a matching rule that changes randomly. Participants were only informed about the correctness of their choice. They should switch their matching rule if their answer was wrong and retain the same rule if it was correct. Cognitive perseveration errors were computed as the number of times participants persisted using a wrong matching rule (i.e., keep using the previous matching rule even after being told it was incorrect).

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Motor perseveration was evaluated in a locomotor task through which participants were forced to transition between two walking contexts: overground (Context 1, 1st panel of Fig. 2C) when the legs moved at the same speeds, or a split-belt treadmill (Context 2, 2nd panel of Fig. 2C) where legs moved at different speeds. Switching training on visit three consisted of five consecutive switches from context 1 to context 2 and then back to context 1, which has been shown to robustly improve motor perseveration within a single visit in young adults [5].

The primary outcome measure of motor perseveration was step length asymmetry (SL asym) during the transition from context 2 (split-belt treadmill) to context 1 (overground) (Figure 2D). SL asym is defined as the difference between step lengths (i.e., the distance between two ankles at heel strike) normalized by the sum of the step lengths [4] (Figure 2A). SL asym is a clinically relevant and robust measure of the ability to transition between split-belt and overground walking [6]. The improvement in motor perseveration was quantified by ΔSL asym, defined as the change in SLasym pre- and post-switching training. Specifically, ΔSL asym = | Post SL asym | - | Pre SL asym |. The absolute value is needed because a SL asym value of zero represents no perseveration and deviation from zero in any direction corresponds to error in motor perseveration.

Two additional gait parameters were included as secondary outcomes to characterize the spatial and temporal components of SL asym because spatial and temporal gait features could show distinct motor perseveration patterns [1]. Spatial gait feature is represented by leading leg asymmetry (Leadasym).

Leadasym is defined as the normalized difference in the ankle positions at the heel strike of the leg in front of the body with respect to the averaged hip position (Figure 2B, 1st panel) [4], which represents asymmetry in the position where you put your feet down. The temporal gait feature is represented by trailing leg asymmetry (Trailasym), defined as the difference in ankle positions of the leg behind the body [7], and it represents asymmetry in the timing of the steps (Figure 2B, 2nd panel).

Figure 2: Motor perseveration outcome measures. (A) Definition of step length asymmetry (SL asym). SLfast and SL slow are the step lengths, defined as the distance between the ankle positions of the two legs, at the fast and slow heel strike respectively. SL asym is defined as the normalized difference between the step length of the two legs (SLfast – SL slow). (B).

Secondary outcomes: leading leg asymmetry (Leadasym) and trailing leg asymmetry (Trialasym). Leadfast is the distance between the ankle of the fast-moving leg and the average hip position at heal strike of the last leg. Trialfast is defined as the distance between the average hip position and the ankle position when the fast leg is behind the body. Leadslow and Trialslow are defined similarly. (C). Two walking contexts used to measure motor perseveration. Walking context 1: both legs move at the same speed with SL asym equal to zero. Walking context 2: a split-belt treadmill where each leg is driven at a different speed with SL asym not equal to zero. (D). Motor perseveration is measured during the transition between walking contexts (from 2 to 1), which is quantified by SL asym in context 1 right after experiencing context 2.

To evaluate the effect of training, a paired-t test was performed to compare SL asym pre- and post-training.

To make sure the cognitive perseveration errors measured in this study are comparable with the prior study [2], a two-sample Kolmogorov-Smirnov test was performed. To investigate the relationship between cognitive perseveration and training-induced improvements in motor perseveration errors, we performed Pearson’s correlation because all of our parameters were normally distributed. The normality of the outcome measures was evaluated with a Kolmogorov-Smirnov test. A significance value of α = 0.05 was used for all analyses. To compare our findings with the previous study where no training was present, a post-hoc analysis was performed to quantify the correlation between cognitive perseveration and motor perseveration pre-training.

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3. RESULT Cognitive perseveration errors ([min, max] = [1,43], median = 9) were comparable with the previous study (D11,22 = 0.32, p=0.38). We found that the switching training was effective. Switching training significantly reduced motor perseveration (Figure 3, t (20) = 4.27, p < 0.001).

Figure 3: Change in motor perseveration pre (green) and post (orange) switching training. Switching training is effective to reduce the motor perseveration in older adults (p<0.001). The heights of the bars represent the average motor perseveration errors measured by SL asym. The error bars represent the standard deviations. Grey dots represent individual participants, and the same participant is connected by the line.

Contrary to our expectation, we found no significant correlation between cognitive perseveration errors and the reduction in motor perseveration as measured by the main outcome measure ΔSL asym (r=0.16, p=0.48, Figure 4A). Furthermore, there was no significant correlation between cognitive perseveration errors and improvements in motor perseveration in either the spatial (ΔLeadasym, r=-0.41, p=0.07, Figure 4B) or temporal domain (ΔTrailasym, r=0.16, p=48, Figure 4C).

Notice that the correlation between cognitive and spatial motor perseveration errors was trending (r= -0.41, p=0.07, Fig 4B), and the strength of the correlation was moderate [8].

Figure 4: Correlations between cognitive perseveration and reduction in motor perseveration in (A) step length asymmetry, (B) leading leg asymmetry, and (C) trailing leg asymmetry. None of the correlation is significant, even though the correlation is trending between cognitive perseveration and reduction in Leadasym (B).

To validate if the result of the previous study can be replicated, we also ran a post-hoc analysis between cognitive perseveration error and baseline motor perseveration error prior to any training (visit 2). No significant correlation was observed between cognitive perseveration and any of the motor perseveration outcome measures (SLasym, r=-0.23, p=0.33, Figure 5A; Leadasym, r=-0.03, p=0.88, Figure 5B; Trailasym, r=0.33, p=0.14, Figure 5C).

Figure 5: Correlations between cognitive perseveration and pre-training motor perseveration in (A) step length asymmetry, (B) leading leg asymmetry, and (C) trailing leg asymmetry. No significant correlation was found between cognitive perseveration error and pre-training motor perseveration error in all outcome measures.

4. DISCUSSION Aging results in declined motor and cognitive functions, accompanied by structural and physiological changes in the brain [9][10]. Examples include the age-related motor perseveration (i.e., decreased ability to switch between distinct motor contexts) and cognitive perseveration (i.e., decreased ability to change strategies in a cognitive task) mentioned above [1-3].

We showed that switching training that was effective in young adults [2] also led to improvements in motor perseveration in older adults, but we found no correlation between the improvement in motor perseveration and cognitive perseveration. The results imply that the neural processes responsible for improving motor perseveration may not be unified with those involved in cognitive perseveration.

We also found no correlation between cognitive and baseline (i.e., pre-training) motor perseveration, in contrast to a previous study that identified a positive correlation [2]. The difference in the findings could be attributed to three reasons. First, there is a difference in the mastery of the task. The previous study recruited older adults who had prior exposures to split-belt treadmill walking, while our participants were naïve to the task. Second, the positive correlation reported in the prior study could stem from inadequate statistical power due to a smaller sample size (n=11 in [2] versus n=22 in the current study), leading to a different conclusion from our study. Third, in the previous study, participants took more breaks while walking on the split-belt treadmill before transitioning to overground walking compared to the participants in our study. It

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is known that forgetting of the newly learned motor patterns occurs during resting breaks [1][2]. Therefore, the different number of breaks could result in different levels of learning of the split-belt walking pattern before switching to overground walking where the motor perseveration error was evaluated.

5. CONCLUSION In conclusion, aging contributes to greater cognitive and motor perseveration. We demonstrated that motor perseveration could be improved with training in older adults. We also found that motor perseveration and its improvement were not correlated with cognitive perseveration, which indicates that distinct neural processes regulate cognitive and motor perseveration and training-induced improvements. Our current results suggest that cognitive perseveration evaluated by the modified Wisconsin Card Sorting task would not be able to provide insight into motor perseveration and training-induced improvement of motor perseveration in older adults. The Wisconsin Card Sorting task, as a conventional neuropsychological test, has been suggested to be a less sensitive approach to measure cognitive function [11]. Nevertheless, it is unknown if the result will hold true for more sensitive cognitive perseveration assessments such as functional magnetic resonance imaging (fMRI). A study that utilizes fMRI to study cognitive function has shown increased activity in regions such as the prefrontal cortex and basal ganglia in individuals exhibiting perseveration, which warrants future studies to explore if activities in these brain regions hold the key to predicting motor perseveration and the effect of training [12].

6. ACKNOWLEDGMENT This work was supported by NSF Career Award 14847891, Pittsburgh Pepper Center P30AG024827, and FHC Research Fellowship provided by the Honor College at the University of Pittsburgh. Also great thanks to all members of Sensorimotor Learning Laboratory at the University of Pittsburgh for the help that they offered.

REFERENCES [1] C. J. Sombric, H. M. Harker, P. J. Sparto, and G. Torres-Oviedo, “Explicit Action Switching Interferes with the Context-Specificity of Motor Memories in Older Adults,” Frontiers in Aging Neuroscience, vol.

9, pp. 40, 2017.

[4] Y. Aucie, H. Harker, C. Sombric, and G. TorresOviedo, “Older adults generalize their movements across walking contexts more than young during gradual and abrupt split-belt walking,” bioRxiv, 2021.

[5] K. A. Day, K. A. Leech, R. T. Roemmich, and A. J. Bastian, “Accelerating Locomotor Savings in Learning: Compressing Four Training Days to One,” Journal of Neurophysiology, vol. 119, no. 6, pp.

2100–2113, 2018.

[6] D. S. Reisman et al., “Repeated split-belt treadmill training improves poststroke step length asymmetry,” Neurorehabilitation and Neural Repair, vol. 27, no. 5, pp. 460–468, 2013.

[7] D. M. Mariscal, P. A. Iturralde, and G. Torres-Oviedo, “Altering attention to split-belt walking increases the generalization of motor memories across walking contexts,” Journal of Neurophysiology, vol. 123, no.

5, pp. 1838–1848, 2020.

[8] H. Akoglu, “User’s guide to correlation coefficients,” Turkish Journal of Emergency Medicine, vol. 18, no.

3, pp. 91–93, Aug. 2018.

[9] N. A. Bishop, T. Lu, and B. A. Yankner, “Neural mechanisms of ageing and cognitive decline,” Nature, vol. 464, no. 7288, pp. 529–535, 2010.

[10] L. Zapparoli, M. Mariano, and E. Paulesu, “How the motor system copes with aging: a quantitative metaanalysis of the effect of aging on motor function control,” Communications Biology, vol. 5, pp. 79, 2022.

[11] F. Barceló and R. T. Knight, “Both random and perseverative errors underlie WCST deficits in prefrontal patients,” Neuropsychologia, vol. 40, no.

3, pp. 349–356, 2002.

[12] A. Anticevic et al., “Global resting-state functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder,” Biol. Psychiatry, vol. 75, no. 8, pp. 595–605, 2014.

[2] C. J. Sombric and G. Torres-Oviedo, “Cognitive and Motor Perseveration Are Associated in Older Adults,” Frontiers in Aging Neuroscience, vol. 13, pp.

610359, 2021.

[3] K. Y. Haaland, L. F. Vranes, J. S. Goodwin, and P. J. Garry, “Wisconsin Card Sort Test Performance in a Healthy Elderly Population,” Journal of Gerontology, vol. 42, no. 3, pp. 345–346, 1987.

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Density variations in large Binder Jet 3D Printed Inconel 625 Parts for Mechanical Testing Sampling Jose Morales1, Pierangeli Rodriguez De Vecchis1, Zachary Harris1, Markus Chmielus1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 1

Jose Morales

Pierangeli Rodriguez

Zachary Harris

Markus Chmielus

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Jose Morales was born in Guatemala and moved to the United States at a young age. He is a junior pursuing an undergraduate degree in materials science and engineering along with a minor in mechanical engineering. His research interests are memory shape alloys and additive manufacturing.

Pierangeli Rodriguez is a PhD student in Prof. Markus Chmielus’s lab in the Mechanical Engineering and Materials Science Department at the University of Pittsburgh. Her research interests focus on Binder Jet 3D printing and characterization of metallic materials for various applications including Nibased superalloys, tungsten carbide and magnetic shape memory alloys.

Zachary Harris is an Assistant Professor in Mechanical Engineering & Materials Science Department at the University of Pittsburgh, where he also serves as the Director of both the Materials Metrology and Characterization Laboratory (MMCL) and Environmental Fatigue Fracture Laboratory (EFFL). His research centers on understanding the fatigue, fracture, and mechanical behavior of structural materials exposed to aggressive environments through high-fidelity mechanical testing and state-of-the-art characterization.

Dr. Markus Chmielus is an Associate Professor and Materials Science and Engineering Program Director in the Mechanical Engineering and Materials Science Department of the University of Pittsburgh. His areas of research focus on advanced manufacturing of metals, carbides, and functional magnetic materials. The combining umbrella of his research is quantitative, correlative characterization of microstructure, defects, mechanical, electrical, magnetic, and thermal properties over several length scales.

Ingenium 2024

Significance Statement

Binder jet printing is predominantly used for small scale prototyping due to print size limitations.

Exceeding limitations leads to heterogeneity within the samples. Expanding upon the capabilities of binder jet printing and the subsequent effects of larger printed samples can allow for reliable larger scale additive manufacturing of complex geometries, while still maintaining homogenous microstructures and mechanical properties.

Category: Experimental Research

Keywords: Binder Jet Printing, large samples, image analysis

ABSTRACT Additive manufacturing is a topic of interest for Inconel 625 due to the unique advantages that it provides; mainly its ability to create complex geometries without the need of post-printing machining. Traditionally, smaller (less than 10-millimeter dimensions) Inconel 625 is binder jet printed for research purposes. In this investigation, the objective is to understand how manufacturing large Inconel 625 blocks affects the microstructure along with its mechanical and chemical properties. It was discovered that printing large blocks leads to a nonuniform microstructure after sintering.

This was determined through an analysis of the pore concentration within the vertically and horizontally cut sections of the large Inconel 625 block. The statistically significant difference of the pore concentrations between the sections implies nonuniform mechanical properties.

1. INTRODUCTION Inconel 625 is a nickel-based superalloy with extensive applications ranging from aerospace to marine environments. Alloy 625’s versatility is mainly due to its high temperature strength and corrosion resistance.

Because alloy 625 derives its strength from its alloying elements, namely molybdenum and niobium, it can be used after annealing without the need of precipitation hardening; this contributes to its high temperature strength and its high creep resistance [1]. At the same time, Inconel 625 tends to be difficult to machine because of high temperature strength. To overcome this, additive manufacturing is used as an alternative to traditional manufacturing techniques. Additive manufacturing (AM) is a process that can create complex geometries without the need of machining through selectively adding material layer by layer.

AM’s high customizability allows for rapid prototype production along with less waste emission than subtractive manufacturing techniques with various materials including metals, polymers, and ceramics [2].

Ingenium 2024

Powder bed binder jet printing (BJP) is a low-cost AM process that deposits powder layer by layer while selectively adding an adhesive binder between layers at room temperature. Manufacturing at room temperature allows for more thermal homogeneity, which eliminates the thermally induced internal stresses post-printing, unlike laser-based AM methods. After the printing process, the as-printed samples, or green parts, generally have a bulk density ranging from 50-60% depending on the powder type [3]. The green parts are then cured and sintered in order to densify and homogenize the parts [1], [2], [3], [4]. Generally, additively manufactured samples provide similar properties to traditionally manufactured samples, with the exception of a general decrease in overall strength [4]. Further research is being conducted to improve the mechanical properties of AM alloy 625 to match, and eventually surpass, traditionally manufactured Inconel 625 [4].

Figure 1: Binder jet printing process [5]

A critical factor that affects the mechanical properties of BJP Inconel 625 is the pore concentration within the sample [2]. Although selective defects can be advantageous to mechanical properties, such as the alloying elements mentioned above, others can have detrimental effects. Pores tend to be high stress concentration sites, which promote the initiation of cracks and lead to crack growth within the microstructure [2]. Microstructural cracks are detrimental to mechanical strength and can induce premature failure of Inconel 625 in any given application [6]. Therefore, it is critical to reduce the porosity within printed samples [2]. Generally, this is conducted through specified sintering techniques, which promote densification and the inherit reduction of internal porosity [2], [7].

Powder type has a clear effect on printed samples; it mainly affects final density, ideal sintering temperatures, and final mechanical properties after sintering. There are two main methods of producing alloy 625 powders: water atomization (WA) and gas

atomization (GA). Mostafaei et al. demonstrated that GA powders retain better overall properties, such as higher sphericity, packing density, microhardness, and strain. Even though WA powders properties do not surpass GA, they have better shape retention at a lower cost [3].

The main objective of this project is to analyze the microstructure of large BJP and sintered Inconel 625 blocks to determine whether the blocks contain microstructural uniformity, and thus fit for mechanical testing samples from all block locations. It is expected to find that the large BJP blocks will not contain an entirely uniform microstructure because of difficulty in providing equivalent heating to the entire structure with the current equipment.

2. EXPERIMENTAL METHODS The water atomized powders were acquired from Carpenter Technology Corporation and HAI Advanced Material Specialists Inc., respectively [8]. All the samples used in the experiments were manufactured using an ExOne Innovent powder bed binder jet printer with the following parameters: drying time of 40 s, binder saturation of 60%, and layer thickness of 100 µm. The block was printed with the dimensions of 26.87×101.89×20.30 mm3. Samples were subsequently cured and sintered in a Thermo Scientific Lindberg/Blue M box furnace. Sintering for the WA block contained a de-binding step at 600˚C and was then heated to 1285˚C and held for 12 h.

After sintering, ten cross sections were cut from the block—five vertically and five horizontally, as shown in Figure 2, which were subsequently mounted in epoxy, grinded, and polished. Samples were observed through optical microscopy using a Zeiss Axio Lab A1 microscope, in which 11 high-contrast images were taken per sample in specified locations. Images were later analyzed using a MATLAB pore analysis code.

Porosity data includes pore areas, centroids, major and minor axes, circularity, equivalent diameters, perimeters, and porosity density and percentage.

Graphs and statistical analysis were later performed in R Studio using the stats package (Kruskal-Wallis rank sum test and Wilcoxon rank sum tests).

Vertical sections (xz)

Horizontal sections (xy)

Figure 2: Vertical (1-5) and horizontal (A-E) cut sections from WA block.

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3. RESULTS AND DISCUSSION A representative micrograph of each cross-section taken at the same magnification is shown in Figure 3, where the porosity, pore size, and pore distribution can be visually compared.

Figure 4: Probability density over pore area. This graph displays the distribution of the pore area sizes for each cut for both vertically (A) and horizontally (B) cut specimens.

Figure 3: Micrographs of the cut samples from sections 1-5 on the left and sections A-E on the right.

Pore analysis was performed on the same four locations for every sample to acquire porosity results for the different sections. Figure 4 shows the pore densities distributions based on the areas of the pores for the vertical and horizontal cuts.

Even though the distributions appear to be qualitatively different based on the graphs, the results still need to be statistically verified to determine whether the data is significantly different. For both the Kruskal-Wallis and Wilcoxon rank sum tests, shown in Table 1, if the calculated p-values are below 0.05, then the values are significantly different and the null hypothesis is rejected. The difference between the two tests is that the Kruskal-Wallis rank sum test determines whether any of the specimens are significantly different by only determining if any of the nonparametric results are significantly different from any other without specifying which are significantly different. The Wilcoxon rank sum test specifies which tests are significantly different from each other by comparing each data set to each other.

For example, sample A is tested against samples B, C, D, and E. Then B is compared with C, D, and E and so on until every sample is systematically compared with each other. Therefore, the Kruskal-Wallis test is used first, and if the null hypothesis is refuted by having p-values below 0.05, then the Wilcoxon test is used to

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determine which samples are significantly different.

Because the Kruskal-Wallis p-value was 7E-10 and 2E-16 for the vertical and horizontal sections respectively, the null hypothesis is rejected and there is at least one sample with a significantly different mean pore area. The Wilcoxon test p-values are shown in Table 1. According to Table 1 (A), sample 1 is significantly different from the rest of the vertically cut samples.

From Table 1 (B), samples A and B appear to be significantly different from the C, D, and E. Wilcoxon Rank Sum Test A (vertical) 1

2

3

4

2

5.90E-05

-

-

-

3

1.80E-07

0.138

-

-

4

7.90E-08

0.074

0.761

-

5

5.40E-07

0.213

0.777

0.557

Wilcoxon Rank Sum Test B (horizontal) A

B

C

D

B

2.00E-16

-

-

-

C

8.20E-08

9.30E-12

-

-

D

2.20E-12

1.70E-07

0.078

-

E

9.80E-11

5.10E-05

0.059

0.668

Table 1: Wilcoxon rank sum tests determine whether data are significantly different from each other by proving or disproving the null hypothesis. (A) provides a comparison between the vertically cut samples while (B) compares the horizontally cut samples for both tests.

According to the distribution of the graphs shown in Figure 4, there is a higher concentration of small pores (smaller than 1000 µm2) for both the vertically and horizontally cut samples. This is evident based on the peaks of the graphs, which are all around 1000 µm2. That being said, graph (B) in Figure 4 has a higher concentration of larger pores compared to graph (A). This means that the horizontally cut sections of the large block contain higher concentrations of larger pores which implies that the block will have less mechanical strength in the horizontal direction compared to the vertical section [2]. Sample 1 is significantly different to the rest of the vertically cut sections because its section likely partially liquified during sintering. Liquid state sintering causes faster densification than solid state sintering, which allows for a lower concentration of pores. Additionally, the remaining pores are smaller in size. After considering that sample A had a wider spread than the rest of the horizontally cut samples, it can be inferred that sample A has a larger number of large pores.

Since sample A is located at the bottom of the cube, this is likely due to the insulation provided by the

ceramic platform that the block was placed on while sintering, which hindered proper heat distribution when sintering, leading to less densification and larger pores. Sample B is also significantly different but contains smaller pores. The reasons for this are still unclear, so more data needs to be collected to draw prevalent conclusions. The vast pore differences show that there is significant heterogeneity within the microstructure of the horizontally cut sections of the WA block. Microstructural heterogeneity suggests the possibility of mechanical heterogeneity within the WA block, but further data needs to be gathered on large BJP blocks to confirm the hypothesis. That being said, mechanical heterogeneity within the blocks would lead to heterogeneity within the tensile bars cut from the blocks. Finding ways to provide more uniform heating within the entirety of the large block while sintering will help reduce the overall pore concentrations within the sample. Future development of the sintering method will hopefully provide a more uniform pore distribution.

A proposed future experiment would be to compare the mechanical and microstructural differences between large-print-cut-out tensile bars and printedto-size tensile bars in order to confirm the proposed hypothesis [6].

The future direction of this project is to print large and microstructurally uniform Inconel 625 blocks for the purpose of making dog bone tensile bars, which will subsequently be mechanically tested. This is not possible without being able to produce mechanically uniform large Inconel 625 blocks.

4. CONCLUSION Due to the microstructural heterogeneity of the binder jet printed Inconel 625 blocks, caused by the varying concentrations of pore size and concentration within the cross sections of the samples, the mechanical properties of the large blocks are expected to be impacted. The microstructural nonuniformity, which is determined by the pore concentration analysis shown by the Wilcoxon test results, show that Sample 1 was significantly different than the rest of the vertical samples, and that Samples A and B were significantly different than the rest of the horizontal samples. Along with these findings, it is implied that the large block will exhibit less mechanical strength in the horizontal direction because of the larger concentration of larger pores in the horizontally cut sections. The differences in pore size and concentrations, which provide differences in mechanical weaknesses within the sample, will be subsequently reduced when future sintering methods are developed. The method of sintering large blocks must be further developed by gaining additional understanding of large Inconel 625 blocks and the differing microstructure that was observed.

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REFERENCES [1] T. K. Shoemaker, Z. D. Harris, and J. T. Burns, “Comparing Stress Corrosion Cracking Behavior of Additively Manufactured and Wrought 17-4PH Stainless Steel,” Corrosion, vol. 78, no. 6, pp. 528– 546, Jun. 2022, doi: 10.5006/4064.

[2] A. Mostafaei, E. L. Stevens, E. T. Hughes, S. D. Biery, C. Hilla, and M. Chmielus, “Powder bed binder jet printed alloy 625: Densification, microstructure and mechanical properties,” Mater Des, vol. 108, pp. 126– 135, Oct. 2016, doi: 10.1016/j.matdes.2016.06.067.

[3] A. Mostafaei, J. Toman, E. L. Stevens, E. T. Hughes, Y. L. Krimer, and M. Chmielus, “Microstructural evolution and mechanical properties of differently heat-treated binder jet printed samples from gasand water-atomized alloy 625 powders,” Acta Mater, vol. 124, pp. 280–289, Feb. 2017, doi: 10.1016/j. actamat.2016.11.021.

[4] M. Cabrini et al., “Stress corrosion cracking of additively manufactured alloy 625,” Materials, vol.

14, no. 20, Oct. 2021, doi: 10.3390/ma14206115.

[5] T. Moritz and S. Maleksaeedi, “Additive manufacturing of ceramic components,” Additive Manufacturing: Materials, Processes, Quantifications and Applications, pp. 105–161, Jan. 2018, doi: 10.1016/B978-0-12-812155-9.00004-9.

[6] Z. D. Harris, S. K. Lawrence, D. L. Medlin, G. Guetard, J. T. Burns, and B. P. Somerday, “Elucidating the contribution of mobile hydrogen-deformation interactions to hydrogen-induced intergranular cracking in polycrystalline nickel,” Acta Mater, vol. 158, pp. 180–192, Oct. 2018, doi: 10.1016/j. actamat.2018.07.043.

[7] S. K. Lawrence et al., “Effects of grain size and deformation temperature on hydrogen-enhanced vacancy formation in Ni alloys,” Acta Mater, vol. 128, pp. 218–226, Apr. 2017, doi: 10.1016/j. actamat.2017.02.016.

[8] R. Jiang, L. Monteil, K. Kimes, A. Mostafaei, and M. Chmielus, “Influence of powder type and binder saturation on binder jet 3D-printed and sintered Inconel 625 samples,” doi: 10.1007/s00170-02107496-3/Published.

70 Undergraduate Research at the Swanson School of Engineering

The Development of PLCL and Polyurethane Small Diameter Vascular Grafts of Varying Compliance Trin R. Murphy1, David R. Maestas, Jr.1, Katarina Martinet1, William R. Wagner1, Sang-Ho Ye1, Jonathan P. Vande Geest1 1

McGowan Institute for Regenerative Medicine, Pittsburgh, PA Trin Murphy is a sophomore bioengineering student minoring in chemistry at the University of Pittsburgh with an interest in tissue engineering.

Trin R. Murphy

David R. Maestas, Jr.

Jonathan Vande Geest

Dr. David R. Maestas, Jr. is a recent doctoral graduate from Johns Hopkins University and is a post-doctoral associate working in the laboratory of Dr. Jonathan Vande Geest. His work is focused on the design of biomechanically compliance matched anti-thrombogenic vascular grafts capable of inducing neo-artery formation in pre-clinical animal models.

Dr. Jonathan Vande Geest is a professor in the Department of Bioengineering, Mechanical Engineering and Materials Science, Department of Ophthalmology, the McGowan Institute for Regenerative Medicine, the Louis J. Fox Center for Vision Restoration, and the Vascular Institute at the University of Pittsburgh.

Significance Statement

To improve patency in small diameter coronary artery bypass grafts, tissue engineered vascular grafts must be compliance matched to the native artery.

Elastomeric biopolymer based trilayered grafts can be tuned to produce compliance matched grafts, wherein the properties can be tuned by varying graft wall thickness.

Category: Experimental Research

Keywords: Tissue Engineered Vascular Graft, Compliance, Poly(Lactide-co-caprolactone), Poly(ester urethane)urea

Ingenium 2024

ABSTRACT Heart disease remains the leading cause of mortality in the United States [1]. One of the treatment options for coronary artery disease (CAD) is a coronary artery bypass graft (CABG), which continues to have a high failure rate [3]. Previous work has shown that providing a compliance matched small diameter tissue engineered vascular graft (TEVG), would result in better patient outcomes [4]. In order to manufacture compliance matching grafts, trilayered small diameter grafts were electrospun from biocompatible materials.

Four graft designs were produced, a poly(ester urethane)urea (PEUU) based thick and thin walled graft containing PEUU elastomer and porcine derived gelatin, and a thick and thin walled poly(Lactide-cocaprolactone) (PLCL) based graft containing PLCL and gelatin. These were then mechanically tested to determine the compliance values of the grafts and compared to the compliance values of native rat aortas.

The native rat aorta had compliances ranging from 0.008977 mmHg-1 to 0.001779 mmHg-1, while the thin and thick PEUU designs had an average of 0.00196 mmHg-1 and 0.00114 mmHg-1. The thin and thick PLCL designs had an average of 0.00136 mmHg-1 and 0.000741 mmHg-1, demonstrating both PEUU and PLCL trilayers were able to be tuned to produce compliance matched TEVGs. The compliance of both grafts was also affected by varying the wall thicknesses, with thinner walled grafts producing more hypercompliant results.

This property is also dependent on the materials used to create the graft, as PLCL and PEUU grafts with similar wall thicknesses showed different compliance values.

PEUU and PLCL polymers show promise for CABG design and production due to their ability to produce compliance matched, tunable grafts.

1. INTRODUCTION Heart disease remains the leading cause of mortality in the United States, with coronary artery disease (CAD) resulting in 928,741 deaths in 2020 [1]. One of the treatment options for CAD is a coronary artery bypass graft (CABG). Approximately 400,000 patients undergo CABG each year [2], though these have a high failure rate of 42.8% [3]. It is hypothesized that this is due to a biomechanical compliance mismatch between the native blood vessel and the CABG, resulting in intimal hyperplasia and thrombosis [4]. Providing a compliance matched small diameter tissue engineered vascular graft (TEVG), would result in better patient outcomes.

In this work, we sought to develop a small diameter compliance matched TEVG. Two compliance matched TEVG designs were developed: a poly(ester urethane) urea (PEUU) based graft containing PEUU and gelatin, and a poly(Lactide-co-caprolactone) (PLCL) based graft containing PLCL and gelatin. A modified PEUU with sulfobetaine groups (PESBUU-50) was selected for the inner layer of the PEUU based graft due to its anti-thrombogenic properties [5]. PLCL was selected

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as the primary material for the second graft due to its high elasticity and lack of toxic byproducts. Both grafts utilized a substantial proportion of gelatin in the outer layer in order to encourage cellular infiltration and remodeling. Cellular remodeling is vital to ensure long term patency of TEVG designs. A challenge when creating the trilayer grafts is balancing materials which provide mechanical support with materials such as gelatin that encourage cellular remodeling but provide less support. The goal is to create a graft that has enough mechanical support to be compliance matched while still being attractive to cells. When producing the grafts, a trial-and-error method was used to produce then test grafts to determine if they fell within the compliance range. Once compliance matched grafts were created, the wall thicknesses were then varied to produce either a more hypocompliant or hypercompliant graft by varying the material dispensed into the inner two layers of the grafts. Using this method, four TEVG designs were created: thin and thick PEUU based TEVGs, and thin and thick PLCL based TEVGs.

2. METHODS 2.1 GRAFT FABRICATION Our chosen TEVG polymer mixtures were composed of gelatin isolated from porcine skin (MilliporeSigma), PEUU, PESBUU-50, 60:40 PLCL (MilliporeSigma). PEUU trilayered grafts were fabricated by electrospinning solutions of 8% PESBUU-50, 10% 80:20 PEUU:Gelatin, then 10% 20:80 PEUU:Gelatin. The solutions were dissolved in 1,1,1,3,3,3-Hexafluoro-2-propanol (HFP).

The solutions were electrospun at a voltage difference of +20 kV, and -4 kV, at a working distance of 10 cm.

Solutions were dispensed onto a rotating target mandrel of 1.1 mm diameter.

PLCL trilayered grafts were fabricated by electrospinning solutions of 10% PLCL, 10% 80:20 PLCL:Gelatin, then 10% 20:80 PLCL:Gelatin. The solutions were dissolved in HFP. The solutions were electrospun at a voltage difference of +15 kV and -0.5 kV, at a working distance of 10 cm. Solutions were dispensed onto a rotating target mandrel of 1.1 mm diameter. Both trilayered grafts were crosslinked for 24 hours in 0.5% genipin in 200 proof ethanol at 37°C. After crosslinking, the grafts were washed 3 times using 200 proof ethanol.

Figure 1: Electrospinning device

2.2 BIOMECHANICAL TESTING AND DATA ANALYSIS Four TEVG design groups were tested: thin and thick PEUU based TEVGs, and thin and thick PLCL based TEVGs. The grafts were then mechanically tested using a custom microbiaxial optomechanical device (CellScale) (Figure 2) that pressurized the samples from 5 mmHg to 120 mmHg in a 37°C heated water bath across 10 cycles, imaging the TEVG outer diameters at a frequency of 5 Hz.

Figure 2: Biomechanical testing device and the software image tracking of the TEVG outer diameter.

The images taken throughout the mechanical tests were used to track the outer diameter of the TEVG. The first 9 cycles of the testing were used to pre-condition the graft, and the 10th cycle was the testing phase.

Compliance was calculated using the outer diameters at 70 mmHg and 120 mmHg [4] (Figure 3). The resulting compliance values were compared to the range of those obtained from native rat aortas. Compliance matched values were those that fell within the range of

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native aortas. Grafts with values above this range were defined as hypercompliant (excessive elasticity), and grafts with values below this range were considered hypocompliant (stiff). Compliance values were evaluated in comparison with native rat aorta explants.

Graft diameters and thicknesses were measured by cutting a cross section and measuring using an Olympus Surgical Microscope. Statistical significances were assessed using ordinary one-way ANOVA with a post-hoc Tukey test for multiple comparisons.

Figure 4: Compliance values of native rat aorta explants in comparison to trilayered TEVG designs, n = 3-5. Dashed lines represent the compliance range of native rat aortas.

Figure 3: Equation used to calculate TEVG compliance with OD representing the outer diameter of the TEVG at each pressure.

3. RESULTS The native rat aorta compliance values ranged from 0.008977 mmHg-1 to 0.001779 mmHg-1. The average compliance values of both thick and thin PLCL trilayers were lower than native rat aortas and PEUU trilayers.

The average compliance value of thin PEUU trilayers was higher than native rat aortas (Table 1). There is a statistical difference (p < 0.05) in the compliance of the thin PEUU trilayer compared to the thick PEUU trilayer, thin PLCL trilayer, and thick PLCL trilayer. (Figure 4). All TEVG designs had a higher average wall thickness than native rat aortas. There was a statistical difference (p < 0.05) between the native rat aorta wall thickness and the wall thickness of all other designs (Figure 5).

Graft Design

Average Compliance (mmHg-1)

Average Wall Thickness (µm)

Native Rat Aorta

0.00139

89

PEUU Thin

0.00196

126**

PEUU Thick

0.00114

167****

PLCL Thin

0.00136

159****

PLCL Thick

0.000741*

175****

*/**/***/**** indicate statistical significance at the 5%/1%/0.1%/<0.01% in comparison to native rat aortas Table 1: Average TEVG compliance and wall thickness measurements

Figure 5: Wall thickness values of native rat aorta explants in comparison to trilayered TEVG designs, n = 4-5.

4. DISCUSSION The goal of the study was to develop two compliance matched TEVG designs, and tune the compliance measurements of these designs by varying the wall thicknesses of the grafts. The average values of both the thick PEUU trilayers and thin PLCL trilayers falls within the range of native rat aorta compliance, making them both compliance matched. The average compliance value of the thin walled PEUU based trilayers falls above the range of compliance values of native rat aortas, making it hypercompliant. The average compliance value of the thick walled PLCL based trilayer falls below the range of native rat aorta compliance, making them hypocompliant. The difference in the average compliance values of the thick and thin PEUU trilayers shows that by varying the thicknesses of the trilayers, compliance can be modulated, with thicker TEVGs producing more hypocompliant results. The same can be said of the PLCL trilayers, the average compliance value of the

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thin trilayers is compliance matched compared to the average compliance of the thick trilayers which are hypocompliant. This demonstrates that the compliance of PLCL trilayers can also be modulated by varying the thickness of the trilayer. All trilayered wall designs were thicker than the native rat aorta wall thickness. Thicker designs tended to be more hypocompliant, while thinner designs tended to be more hypercompliant.

The thick PEUU, thin PLCL, and thick PLCL grafts all have relatively similar wall thickness but different compliance values. Compliance is not only impacted by the wall thickness of the grafts but the materials used in the grafts as well.

5. CONCLUSIONS These results showed that small diameter, biocompatible, compliance matched trilayered TEVGs can be fabricated using both PEUU or PLCL polymer with gelatin. Furthermore, these grafts’ compliance values can be modified by varying the thicknesses of various graft layers. The thicker grafts tended to have more hypocompliant values, whereas the thinner grafts tended to have more hypercompliant values. This property is also dependent on the materials used to create the graft, as PLCL and PEUU grafts with similar wall thicknesses showed different compliance values.

In the future, computational analysis can be utilized to quantify the relationship between wall thickness, biomaterial mixture ratios, and compliance. Future directions also include in vitro and in vivo studies to assess the extent of cellular infiltration and remodeling in both PEUU and PLCL TEVGs when utilized as an interpositional vascular graft. Anti-thrombogenicity of the inner PESBUU-50 layer of the PEUU trilayers should also be verified. PEUU and PLCL polymers show promise for CABG design and production due to their ability to produce compliance matched, tunable grafts.

REFERENCES [1] C. W. Tsao, et al., “Heart Disease and Stroke Statistics – 2023 Update: A Report From the American Heart Association,” Circulation, vol. 147, January, 2023, doi: 10.1161/CIR.0000000000001123. [Accessed Aug. 5, 2023].

[2] B. J. Bachar and B. Manna, “Coronary Artery Bypass Graft,” StatPearls, 2023. [Online]. Available: https:// pubmed.ncbi.nlm.nih.gov/29939613/. [Accessed Aug. 5, 2023].

[3] C. N. Hess and M. P. Bonaca, “Contemporary Review of Antithrombotic Therapy in Peripheral Artery Disease,” Circulation, vol. 13, 2020, doi: 10.1161/ CIRCINTERVENTIONS.120.009584. [Accessed Aug. 6, 2023].

[4] Y. Jeong, Y. Yao, and E. K. Yim, “Current Understanding of Intimal Hyperplasia and Effect of Compliance in Synthetic Small Diameter Vascular Grafts,” Biomaterials science, vol. 8, no. 16, pp. 43834395, 2020, doi: 10.1039/d0bm00226g. [Accessed Aug. 6, 2023].

[5] SH. Ye, Y. Hong, H. Sakaguchi, V. Shankarraman, S. Luketich, A. D’Amor, and W. R. Wagner, “Nonthrombogenic, biodegradable elastomeric polyurethanes with variable sulfobetaine content,” ACS Applied Materials & Interfaces, vol. 6, no. 24, pp.

796-806, 2014, doi: 10.1021/am506998s. [Accessed Aug. 7, 2023].

6. ACKNOWLEDGEMENTS This research was funded by NIH award R01HL157017 to JPVG.

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Ingenium 2024

Anticipatory licking and dopamine release in the monkey striatum Raymond Murray1, Jiwon Choi1, Usamma Amjad1, Helen Schwerdt1 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 1

Raymond Murray

Raymond is a fourth-year Bioengineering undergraduate student from Warren, Ohio. He is interested in understanding how dopamine release can modulate internal state variables and hopes to relate this to dopamine’s roles in reinforcement, learning, and motivation.

Usamma is a third-year neural engineering PHD student in the Schwerdt lab. He is interested in the role of striatal dopamine in sequence and skill learning.

Usamma Amjad

Jiwon is interested in learning about the relationship between dopamine and emotionally regulated learning of skills, as well as its association to different neural disorders.

Jiwon Choi

Helen Schwerdt

Helen Schwerdt is an Assistant Professor in the Department of Bioengineering at the University of Pittsburgh. She is interested in developing and applying tools to identify the neural mechanisms of learning in health and disease, with a current focus on the role of dopamine signaling and the striatum.

Significance Statement

Little information about the neurotransmitter dopamine’s (DA) role during the intertrial interval (ITI) of a behavioral task. This work investigated the potential location-specific role of DA release during the ITI via analysis of a monkey’s anticipatory licking response.

Category: Computational Research

Keywords: intertrial interval dopamine release, differential dopamine release, dopamine and reinforcement, behavioral analysis

ABSTRACT Extensive research has been done explaining the theoretical role of dopamine (DA) in movement, reward learning, and motivation when the DA release occurs within a single trial of a behavioral task. However, little is known about the role of DA release that occurs during the intertrial interval, the portion of a behavioral task which does not contain any reward or reward predictive cues. This work sought to provide novel insight into the role of ITI DA release via analysis of a monkey’s anticipatory licking response during a behavioral task. Initial analysis of the monkey’s anticipatory licking response showed that licking response distributions were statistically different from trials that resulted in either big or small reward delivery, validating its use as an indicator of motivation and learning. Further analysis of the monkey’s licking response also showed an unexpected observation that the magnitude of the response varies according to the previous trial’s outcome, calling for future analysis of non-neurological physiological responses for this same trial history effect. Extracting DA concentration recordings during the ITI before trials with the top and bottom 25% of licking responses demonstrated that licking-grouped ITI DA differed more frequently in the caudate nucleus than in the putamen. This could indicate that ITI DA release in the caudate nucleus may regulate an animal’s motivation and learning of a behavioral task.

1. INTRODUCTION The neurotransmitter dopamine (DA) has long been shown to play a role in movement, reward learning, and motivation regulation [1]. Specifically, great evidence shows how DA release that occurs within a given trial, the portion of a behavioral task which contains reward predictive cues and actual reward presentation following the completion of a desired behavioral response to the reward predictive cues, act as a teaching signal known as a reward prediction error (RPE) [2]. Mathematically, an RPE is computed as the difference between the expected value of a given state and the true value of a given state and acts to reinforce the association between the stimuli or actions that lead to reward presentation and the reward itself [3].

In neuroscience, a newer research question has emerged revolving around how neural activity occurring during the intertrial-interval (ITI), the time between individual trials of a behavioral task which does not contain any reward or reward predictive information, may impact future trial performance. Research in human subjects has shown that increased cortical activity measured using electroencephalogram occurs following error trials in comparison to successful trials, indicating the ITI may be a period where active learning occurs [4]. Little work has been done to define the role of ITI DA release during a behavioral task compared to the extensive work explaining within-trial DA release.

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In a study released in Schwerdt et al. Sci. Adv. 2020, DA release, local field potentials, and non-neurological physiological responses were simultaneously recorded from monkeys during a visually guided reward-biased saccade task [5]. Anticipatory licking, which has been seen to serve as an indicator for learning and motivation [6], was one of the physiological responses recorded where a weak but significant correlation to peak within-trial DA release was observed [5]. However, this work did not analyze ITI DA release or how any of the physiological responses related to ITI DA release.

Via secondary data analysis of a monkey’s anticipatory response from this original study, this research sought to gain further insight into ITI DA release’s potential roles in motivation and learning.

2. METHODS 2.1: Task description The monkey (subject M1) used in the original study was trained on a visually guided, reward-biased saccade task [5]. The monkey would first fixate on a central target for 1.6 seconds then saccade to a peripheral target where it would maintain fixation for an additional four seconds before receiving a liquid food reward. The location of the peripheral target (left or right of the central target) would inform the monkey if successful completion of the trial would result in a big or small liquid food reward.

2.2: Recording DA and licking DA was recorded utilizing surgically implanted silica-based carbon fiber and microinvasive carbon fiber electrodes into the striatum of the two rhesus monkeys used in the original study [5]. Fast-scan cyclic voltammetry was employed to induce a reductionoxidation (redox) reaction of DA molecules near the electrodes via an applied triangle voltage ramp of -0.4 V up to +1.3 V and back down to -0.4 V lasting 8.5 ms occurring once every 100 ms. The recorded current located at DA’s redox potentials of -0.2 V and 0.6 V was then used to estimate DA concentration changes utilizing principal component analysis [7]. DA concentrations were background-subtracted relative to the start of the current trial when the central target was displayed. Licking was recorded as the summed magnitude of a three-axis accelerometer (SparkFun, MMA8452Q) attached to the monkey’s food delivery tube [5].

2.3: Within-trial licking All data were processed and analyzed in MATLAB (R 2022b). Online licking traces were converted into a scalar value for analysis by taking the area under the curve (AUC) of the accelerometer output during the first two seconds after the peripheral target was displayed. Licking scalar values were first compared from successful trials that resulted in either a big or small reward delivery. These trials followed other previously successful trials that resulted in either a big or small reward delivery. Using this method of trial selection, an average of 420 ± 61 trials were used for analysis for each recording session. These scalars were then plugged into a two-sample t-test to determine statistical significance.

2.4: Licking “history effect”

Figure 1: Graphical task description. Cartoon illustrating the steps of the task: central target fixation followed by peripheral target fixation and the resulting reward if completed successfully. Modified from Schwerdt et al. [5] with permission from author.

Data from a total of ten individual recording sessions were used throughout the completion of this work.

Individual recording sessions would occur once per day where the monkey would perform an average of 1,102 ± 172 trials of the task described above.

Licking responses were separately extracted for trials that resulted in big reward delivery that followed trials that resulted in either a big reward, small reward, or no reward due to error (when the monkey breaks fixation with either target). This trial selection resulted in an average of 320 ± 60 trials being used for analysis for each recording session. Licking traces were visually compared during the first two seconds after the peripheral target was displayed.

2.5: ITI DA analysis Licking scalars were extracted from trials with a leftward peripheral target that resulted in big reward delivery that followed previously successful trials.

This trial selection resulted in an average of 125 ± 24 trials being used for analysis for each recording session. These scalar values were then divided into four equivalently sized quartiles using the boundaries output from MATLAB’s “quantile” function. ITI DA traces for the trials with the most licking (fourth quartile) and the least licking (first quantile) were then extracted from one site in the caudate nucleus and

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putamen for qualitative analysis. The ITI over which DA concentrations were analyzed was defined to begin two seconds after the previous trial’s reward delivery until the start of the current trial. DA traces were considered different from one another if the across-trial mean plus or minus standard error traces were visually distinct from each other (no or minimal overlap) for approximately half of the defined ITI or more.

3.2: Licking “history effect” Further analysis of the monkey’s licking response uncovered an unexpected trend: the magnitude of the monkey’s anticipatory licking response varies according to whether the previous trial resulted in a big reward (Figure 3, red), small reward (blue), or was an error trial (green). This trend was observed in all ten recording sessions analyzed by visual inspection.

3. RESULTS 3.1: Licking as an indicator of motivation.

Licking scalars were first compared from trial that resulted in either big or small reward delivery to determine if licking could be used as a quantitative indicator of a monkey’s motivation and learning during the given task. Figure 2 shows the average accelerometer output across trials (left) and the distribution of licking scalars (right) for an example recording session. The increased voltage outputted from the accelerometer during trials that resulted in big reward delivery and the subsequent higher AUC value compared to trials that resulted in small reward delivery in this recording session reflected a trend present in all analyzed recording sessions: the monkey’s anticipatory licking response was quantifiably higher when the monkey was expecting to receive a big reward. Plugging the scalar distributions into a two-sample t-test showed that the distributions were statistically different from one another in all ten recording sessions analyzed.

Figure 3: Trial history effect on licking. Licking traces from an example recording session from trials that resulted in a big reward that occurred after a big reward trial (red), small reward trial (blue), or was an error trial (green). Licking traces are plotted as the mean ± the standard error about the mean.

The grey shaded region indicates a period of noise from the accelerometer unrepresentative of true behavior and was not analyzed.

3.3: Licking-grouped ITI DA release

Figure 2: Within-trial licking comparisons. Licking traces (left) from an example recording session from trials that resulted in either a big reward (red) or small reward (blue) delivery and the resulting AUC distributions (right). Licking traces are plotted as the mean ± the standard error about the mean. Red and blue vertical lines reflect mean AUC values for trials that resulted in either a big or small reward, respectively.

After validating that anticipatory licking could be used as a quantitative indicator for a monkey’s motivation, this licking response was then used to investigate if ITI DA release could play a role in modulating future anticipatory licking responses. Qualitative analysis of ITI DA traces in both the putamen and caudate nucleus during ten separate recording sessions showed that five of the ten recording sessions analyzed displayed licking-grouped differing ITI DA release dynamics in the caudate nucleus while only two sessions displayed differing dynamics in the putamen.

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Figure 4: Licking-grouped ITI DA release. ITI DA concentration computations from an example recording session extracted from trials with the greatest (red) and smallest (blue) licking response recorded from sites in the putamen (left) and caudate nucleus (right). DA traces are plotted as the mean ± the standard error about the mean.

Based on the previous observation that the monkey’s anticipatory licking response may vary depending on whether or not the previous trial resulted in big or small reward delivery, this analysis was repeated in a reduced sample size containing only trials with a leftward peripheral target that resulted in big reward delivery in which the previous trial resulted in only big reward delivery, whereas the previous analysis contained trials that occurred after trials that resulted in either big or small reward delivery. Further restricting the types of trials allowed for analysis meant an average of 88 ± 22 trials were selected for analysis for each recording session. This repeated analysis showed a similar trend as four of the ten analyzed recording sessions showed differing licking-grouped ITI DA dynamics in the caudate nucleus, and only one session showed differing dynamics in the putamen.

4. DISCUSSION Initial comparison of the measured anticipatory licking responses during successful trials demonstrated that how much the monkey licked its reward delivery tube consistently differed according to whether the monkey was expecting to receive a big or small reward upon completion of the trial. It is important to note that during the time when online licking traces were converted to a scalar value, there was no reward delivery occurring; therefore, all licking recorded during this period was purely in anticipation of future reward. As stated in Methods 2.1, the monkey is aware of whether completion of the trial will result in big or small reward delivery during this portion of the task.

This observation of differing licking distributions in

anticipation of different rewards during all recording sessions analyzed supports previous research and the assumption for this research that a monkey’s licking response can be used as an indicator of motivation.

Analyzing the licking responses of the monkey more closely demonstrated that previous trial history may play a role in how much the monkey licks its reward delivery tube in anticipation of future reward. Across all sessions, differing licking responses were observed during the first second after the peripheral target was displayed depending on whether the previous trial resulted in big reward delivery, small reward delivery, or reward omission due to the monkey making an error during the trial by breaking fixation with either of the targets. In the original study in which this data was obtained, a similar effect was observed with DA traces during this period in which DA release was greatest when the previous trial was an error trial and least when the previous trial resulted in a big reward, with trials that followed a small reward falling in between [5]. Interestingly, the effect trial history had on licking dynamics appeared to be the opposite of the effect trial history had on DA release: the monkey licked the reward delivery tube most when the previous trial resulted in a big reward and least when the previous trial was an error trial. This observation calls for investigation into if the other non-neural physiological responses also display a similar effect due to trial history.

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Finally, analysis of DA release during the ITI showed that licking-grouped DA release displayed differing dynamics more often from sites recorded in the caudate nucleus of the monkey striatum than in sites recorded from the putamen. These sites that displayed differing DA release dynamics also help to support DA’s canonical role in reinforcement as ITI DA release was recorded in higher relative concentrations when the upcoming trial had the most relative licking than when the upcoming trial had the least relative licking. These results postulate that it is more likely that motivationregulating ITI DA release during a behavioral task occurs in the caudate nucleus than in the putamen.

5. CONCLUSIONS By first comparing distributions of a monkey’s anticipatory licking response during trials that resulted in either a big or small reward delivery, the use of anticipatory licking as an indicator of motivation was confirmed, allowing for future analysis of DA release grouped according to the monkey’s licking response.

Analyzing licking-grouped DA traces during the ITI showed that differing DA release dynamics occurred more often in the caudate nucleus than in the putamen, implying both that ITI DA release may play a role in regulating future motivation of an animal to complete a behavioral task and that this motivation-regulating ITI DA release occurs in the caudate nucleus.

6. ACKNOWLEDGEMENTS Funding was provided by the Office of the Provost, the Swanson School of Engineering, and the Bioengineering department at the University of Pittsburgh in addition to the following grants: NIH/ NINDS R00 - 5R00NS107639-04 and Michael J. Fox Foundation (MJFF) Aligning Science Across Parkinson’s (ASAP) - ASAP-020-519.

REFERENCES [1] R. A. Wise, “Dopamine, learning and motivation,” Nature Reviews Neuroscience, vol. 5, no. 6, pp.

483–494, Jun. 2004. doi:10.1038/nrn1406 [2] C. D. Fiorillo, P. N. Tobler, and W. Schultz, “Discrete coding of reward probability and uncertainty by dopamine neurons,” Science, vol. 299, no. 5614, pp.

1898–1902, Mar. 2003. doi:10.1126/science.1077349 [3] N. D. Daw, P. N. Tobler, P. W. Glimcher, and E. Fehr, “Value Learning through Reinforcement: The Basics of Dopamine and Reinforcement Learning,” in Neuroeconomics, Second., Cambridge, MA: Academic Press, 2014, pp. 283–298 [4] R. J. Compton, D. Arnstein, G. Freedman, J. Dainer-Best, and A. Liss, “Cognitive control in the intertrial interval: Evidence from EEG Alpha Power,” Psychophysiology, vol. 48, no. 5, pp. 583–590, Sep. 2010. doi:10.1111/j.1469-8986.2010.01124.x [5] H. N. Schwerdt et al., “Dopamine and beta-band oscillations differentially link to striatal value and motor control,” Science Advances, vol. 6, no. 39, Sep. 2020. doi:10.1126/sciadv.abb9226 [6] S. Kobayashi and W. Schultz, “Influence of reward delays on responses of dopamine neurons,” The Journal of Neuroscience, vol. 28, no. 31, pp. 7837– 7846, Jul. 2008. doi:10.1523/jneurosci.1600-08.2008 [7] H. N. Schwerdt et al., “Long-term dopamine neurochemical monitoring in primates,” Proceedings of the National Academy of Sciences, vol. 114, no. 50, pp. 13260–13265, Nov. 2017. doi:10.1073/ pnas.1713756114

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Effects of Commensal Respiratory Bacteria on The Persistence of Influenza A Virus in Droplets Daniel S. Nolan1, Shannon C. David2, Tamar Kohn1 Environmental Chemistry Laboratory, School of Architecture, Civil and Environmental Engineering EPFL, Lausanne, Vaud, Switzerland 1

Deparment of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 2

Daniel Nolan

Dan Nolan was raised in Media, Pennsylvania. He applied his background in environmental engineering to a completely novel field, environmental virology. Through this research, he found a passion for the microbial world and its interactions with other organisms.

He plans to continue to study and utilize microbial communities in water treatment.

Shannon David originates from Australia, where her fascination with human health projects led her to study infectious diseases and vaccine design. She now works as a post-doc in Switzerland with a team studying respiratory virus transmission in air.

Shannon David

Tamar Kohn

Tamar Kohn is a native of Switzerland, though she has spent 10 years of her life in the U.S. She first started to work with viruses during her postdoc at UC Berkeley, and she has been fascinated by their environmental fate ever since.

Her research group currently focuses on both waterborne and airborne viruses, in particular their mechanisms of inactivation.

Significance Statement

Infection with Influenza A virus (IAV) impacts a multitude of species. This study investigates the interaction between microbiota in the respiratory tract and IAV within respiratory emitted droplets. This research provides insight into the microbial interactions within these droplets. This paper identifies microbiota as having a potential role to play in increasing transmission risk between people.

Category: Experimental Research

Keywords: Influenza A Virus, Respiratory Microbiota, Respiratory Droplets.

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ABSTRACT Understanding the symbiotic relationship between commensal respiratory bacteria and respiratory viruses, specifically IAV, is critical to the development of mitigation strategies for disease transmission.

Stabilizing viral-bacterial relationships have been seen in other biomes within the human body, particularly in the gastrointestinal system. The purpose of the study was to investigate if a similar relationship existed in the respiratory space. IAV was suspended in a simple saline matrix alone or was co-suspended with a variety of commensal respiratory bacteria. Afterwards, these viral mixtures were deposited as stationary droplets and exposed to indoor room air conditions. The decay in viral infectivity was enumerated via plaque assay on MDCK cells. This study also examined the particular gram status of bacteria (gram positive vs. negative) and compared respiratory bacteria to non-respiratory bacteria to create a hierarchy of viral protective capability.

This study found that the gram-positive commensal respiratory bacteria (Streptococcus pneumoniae and S. aureus) were most protective for IAV, retaining nearly 2-log10 more of the infectious virus within droplets compared to when IAV was deposited alone after one hour of drying. The gram-negative bacteria tested here (Moraxella catarrhalis, Haemophilus influenzae, and Pseudomonas aeruginosa) showed minimal protection of IAV. Similarly, examination of two nonrespiratory bacterial species (Pseudomonas syringae and Escherichia coli) showed little to no protection, with no experiments exceeding 0.5-log stabilization of IAV relative to droplets of virus alone.

A single protective bacterium was then selected to investigate which physical properties of the bacterial cells were associated with the observed viral stabilization. This bacterium of interest was inactivated via a short heat-shock (eliminating cell viability but retaining cell morphology) or was completely lysed (eliminating both cell viability and morphology). Using these preparations, bacterial viability was found to not be required for viral stabilization in droplets due to the retained protection of IAV by heat-inactivated bacteria compared to live cells. Instead, data suggest that the bacteria needed to be intact to exert their protective effect for the virus. Additionally, these inactivation treatments appeared to differentially alter the morphologies of the drying droplets in which the bacteria and virus were contained. Bacterial preparations that caused the drying droplets to retain the largest diameters also appeared to retain the most viral infectivity. This suggests that the viral protection may rely more on the physical behavior of the droplet as it dries than simply the total organic composition.

Identification of commensal bacteria species that are more protective than others here indicates that an individual’s respiratory microbiota could be a novel factor contributing to the efficacy of person-person

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viral spread. The identification of such factors and their contribution to viral stabilization in emitted droplets will assist in limiting transmission of IAV in the future.

1. INTRODUCTION Seasonal IAV infections reach all corners of the globe and create major health and economic burdens. IAV causes serious illness in 3–5 million people every year, with 290,000–650,000 deaths [1]. Cowling et al. examined the most prominent forms of IAV transmission between humans, and the results indicate that small aerosols and droplets are important transmission vectors [2]. It is important to reduce and ultimately prevent airborne transmission of this recurrent virus, although there are numerous transmission-associated factors for which we lack proper understanding.

Influenza A is not alone in the respiratory tract. The former scientific dogma surrounding respiratory bacteria maintained that lungs were healthiest when they contained no bacteria, but this is now accepted as false [3]. There is a plethora of bacteria that coexist within the lungs, nose, and other locations in the respiratory system of a healthy human body. A study

by Santacroce et al. noted that in the lower respiratory tract alone there are at least 17 genera of bacteria and fungi detected [4]. While these bacteria are omnipresent in the respiratory system, these bacteria have the potential to be detrimental to the host, establishing a significant role in the respiratory health of the individual [3].

Commensal bacteria have been shown to aid infectivity of viruses in other body niches, particularly the gastrointestinal tract. For example, direct binding of commensal gut bacteria stabilized poliovirus against various environmental stressors and facilitated entry of norovirus into B-cells [5]. The aim of this project was to determine whether IAV could be similarly stabilized by members of the respiratory microbiome when the virus was in stationary particles representative of expelled respiratory droplets.

2. MATERIALS /METHODS Cells. Madin-Darby Canine Kidney (MDCK) cells (ThermoFisher) were grown at 37oC in an incubator with 5% CO2 content. Cells were cultured in Dulbecco’s modified Eagle’s medium (Gibco) and supplemented with 5% fetal bovine serum and 1% penicillinstreptomycin.

Microorganisms. Bacterial strains used in this study were Streptococcus pneumoniae (strain D39V, serotype 2), Staphylococcus aureus (strain NCTC 8325), Moraxella catarrhalis (strain Nell, DSM 9143), Haemophilus influenzae (strain 572, DSM 11969), and Pseudomonas aeruginosa (strain CCEB 481, DSM 50071), which represent 5 of the most common bacteria present in the human respiratory tract. S. aureus

and S. pneumoniae were kind gifts from Professor Jan Wilhelm-Veening (DMF, University of Lausanne, Switzerland). M. catarrhalis, P. aeruginosa, and H. influenzae were obtained from DSMZ (Germany). Nonrespiratory bacteria Pseudomonas syringae (DSMZ, DSM 21482) and Escherichia coli (Stock 3300141, ATCC 19853) were also tested to determine if the protection was limited to solely respiratory microbes. All bacterial stocks were streaked on agar plates overnight. Then, healthy colonies were extracted from the plates and liquid cultures were grown to mid-log phase (OD600 = 0.2-0.4). Once the required bacterial density was reached, the liquid cultures were supplemented with 15–20% glycerol, frozen in growth media, and stored at -80ºC until use. When the bacterial stock was used in an experiment, it was thawed on ice and washed 3x in Phosphate Buffered Saline (PBS) to remove residual glycerol and growth media components. The cells were then resuspended in fresh PBS at 108 colony forming units (CFU) /mL for immediate use. Virus strain A/ WSN/33 (a laboratory-adapted version of circulating H1N1 human IAV) was used in this study and was spiked into bacteria suspensions or plain PBS at 5 x 107 plaque forming units (PFU )/mL final concentration.

Where required, bacteria were physically inactivated with two heat-based treatments prior to viral spiking.

The first treatment was heat shock (95ºC treatment for 15 minutes followed by immediate cooling), and the second was lysis (95ºC treatment of smaller volumes for 30 minutes to progressively remove all water from the cells, followed by cooling, rehydration and resuspension in the same original volume). Both methods were confirmed to render the bacterial preparations sterile via CFU plating on agar, and the morphology was confirmed to be altered as required via oil immersion microscopy.

Humidity Chamber Droplet Experiments. IAV stocks were diluted in PBS alone or PBS containing individual bacterial strains immediately prior to use. Pathogen mixtures were then vortexed briefly to mix and deposited as 1 µL droplets onto a 96well polystyrene non-binding plate within a sealed humidity and temperature-controlled chamber.

Representative indoor air conditions of 40% relative humidity (RH) and 22°C were used, with temperature and RH monitored for the duration of all experiments via a portable hygrometer placed inside the chamber.

At time-points 0, 15-, 30-, 45-, and 60-minutes postdeposition, triplicate droplet samples were collected by resuspension in 300 µL PBS for subsequent virus titration.

Droplet Diameter Analysis. The droplets were prepared and deposited as above, then filmed during 30 minutes of drying via a custom 3D-printed stand housing a Raspberry Pi (Raspberry Pi 4 model B Rev. 1.4, with ARMv7 Processor rev 3 v7l) paired with a mounted Sony camera (model: IMX477R, 12.3 megapixels, specifications available here: https://www.

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raspberrypi.com/products/raspberry-pi-high-qualitycamera/). The camera’s distance from the non-binding plate’s surface was 15 cm. The images were acquired at intervals of 16 seconds for a total of 30 minutes.

Next, the images were processed through a droplet diameter analysis software called Cellpose that uses a custom trained algorithm to recognize the droplet size. Following image collection, the images were then processed through Image-J to quantify the droplet diameters.

B

3. RESULTS The first series of experiments were performed using IAV mixed with individual respiratory and nonrespiratory bacteria, to assess whether bacterialmediated stabilization of IAV could occur in each case. Data show that respiratory bacteria were in fact stabilizing to IAV in droplets, although this was a strainspecific phenomenon. For example, S. pneumoniae and S. aureus were highly protective for IAV, retaining nearly 2-log10 more infectious virus within droplets compared to IAV alone over a one-hour period of drying (Figure 1A). Conversely, M. catarrhalis, H. influenzae, and P. aeruginosa offered no protection against viral decay, despite being used at comparable CFU between groups (Figure 1B).

In general, gram-positive bacteria showed protection for IAV from 30 minutes onwards, whereas the gramnegative bacteria did not show any protection at any tested time-point. To assess whether stabilization was limited to respiratory bacteria, we also tested two bacteria isolated from alternative sites (E. coli isolated from the gastrointestinal tract, P. syringae isolated from plants). Similar to the gram-negative respiratory bacteria, these non-respiratory bacteria showed minimal protection at the 60-minute mark compared to the group of IAV alone (Fig. 1C).

A

C

Figure 1: Decay of IAV in droplets occurs at standard environmental conditions (40% RH, 22ºC). IAV was deposited in droplets alone, or co-deposited with A) gram positive respiratory bacteria, B) gram negative respiratory bacteria, or C) non-respiratory bacteria. Data is presented as mean ± SD, n = 3 or 6 individual droplets tested per group.

The next experiment series examined which properties of the bacteria were contributing to viral stabilization, with focus on the structural integrity and bacterial viability. This series of experiments involved one protective bacterium, S. pneumoniae, that was treated by 2 different physical inactivation methods. Bacteria were inactivated either via heat shock, which retained cellular morphology but removed the bacterium’s ability to replicate (i.e., the cells were no longer viable), or bacteria were treated via lysis, which removed both cellular morphology and cell viability. After the samples were treated, they were then added to IAV to assess if either of the affected bacterial properties were important for viral stabilization.

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The change in droplet diameters were investigated during drying at the same indoor conditions (40% RH, 22ºC) over a 30-minute period. Figure 3 illustrates the shrinking of the droplets containing IAV only, or IAV mixed with live S. pneumoniae, lysed S. pneumoniae, or heat-inactivated S. pneumoniae. Data shows that the live and heat-inactivated bacteria both retained a large droplet diameter during drying, whilst the lysed bacteria lost this ability. When compared with viral stabilization data in Figure 2, the droplet with the higher viral stability also exhibited a larger droplet diameter while drying. The same was true for the lower viral stability and lower droplet diameter.

DISCUSSION

Figure 2 details the findings of the physically inactivated S. pneumoniae. The heat-inactivated bacteria showed a very minimal reduction in the protective capabilities when compared to live, untreated bacterial cells.

On the other hand, the lysed bacteria showed a more significant decrease in protective capabilities, now showing similar infectivity results to droplets containing IAV only. During these experiments, it was observed that the most protective bacteria also caused the droplets to retain a larger diameter during drying at 40% RH (data not shown). It was therefore of interest to assess if the physical inactivation methods of the bacteria similarly affected droplet behavior.

Respiratory bacteria, particularly S. pneumoniae, have a symbiotic relationship with IAV. For example, IAV infection increases the susceptibility of a patient to secondary bacterial infection, whilst the presence of S. pneumoniae increases viral titers in mice during acute IAV infections [6]. We have observed here that this beneficial relationship has potential to extend beyond the host and into virus transmission. We found that viral stabilization in stationary droplets can be mediated by the presence of respiratory bacteria. This phenomenon was also species-specific , with a distinct difference in viral stabilization between gram-positive and gram-negative bacteria. This species-specific difference in relation to viral stabilization has been seen previously with murine norovirus (MNV). Budicini & Pfeiffer found that MNV could bind to both grampositive and negative bacteria, but the gram-positive bacteria showed the most pronounced stabilization for the virions during treatment with heat [7], indicating that the viral stabilization was gram-specific and heatstable, similar to observations made here.

Figure 3: 1 μL droplets filmed drying at 40% RH, 22ºC. The droplets are imaged every 16 seconds, beginning at 2 minutes post-deposition. Data is presented as mean (solid line) ± SD (shaded areas), n = 6 individual droplets tested per group.

Here, heat inactivation of S. pneumoniae did not significantly reduce the bacteria’s protective capacity, suggesting the viability of the bacteria was not important for viral stabilization (Fig. 2). However, the lysis treatment did have an effect. To investigate this effect, the droplets’ diameters were also analyzed.

Figure 3 shows that droplets containing IAV alone progressively shrank with time until diameter stabilization occurred at approximately 20 minutes.

This diameter stabilization occurs when the droplets crystalize (i.e., effloresce) and therefore can no longer physically shrink. When compared to this behavior, we can see that the presence of intact bacteria causes the droplet to stay remarkably close to its initial radius during drying. This is because the bacteria caused the droplet to dry as a flatter round disk, rather than the standard half-sphere morphology seen for deposited droplets of PBS and of virus alone. Due to this change in morphology, bacteria-containing droplets have a higher surface area to volume ratio, therefore evaporating faster and crystalizing sooner. Salt concentrations in respiratory droplets are detrimental to IAV, and the

Figure 2: Decay of IAV in droplets occurs at standard environmental conditions (40% RH, 22ºC). IAV was deposited in droplets alone, or co-deposited with live S. pneumoniae, treated S. pneumoniae (lysed and heat inactivated). Data is presented as mean ± SD, n = 3 or 6 individual droplets tested per group.

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rate of viral decay increases linearly with increasing salt concentrations when the virus is not supplemented with proteins [8]. As a result, the co-localized virus is removed from a high liquid salt environment sooner, halting inactivation of the virus and allowing higher persistence.

We observed that the heat inactivated bacteria conferred the same effect on both droplet diameter and viral stabilization. The heat inactivated bacteria retain their morphology, but the cell viability is destroyed by the heat. This data therefore indicate that bacteria do not have to be alive, to be stabilizing.

Conversely, the lysing of the bacteria eliminated both the effect on the droplet diameter and the protective capacity for the virus. Much like droplets containing IAV alone, diameters of droplets containing IAV plus lysed bacteria slowly shrank until the 20-minute mark (Figure 3). The lysed bacteria offered no protection to the virus (Figure 2), despite the lysed bacteria droplets containing the same total amount of organic matter as the live and heat-inactivated bacteria conditions.

This indicates that the morphology of the bacteria has a more significant role in the stabilization of the virus compared with the total organic content of the surrounding droplet. Future work in this project will continue to investigate the structural integrity’s role in viral stabilization and protection, and will investigate progressive removal of isolated bacteria portions, such as the lipopolysaccharide layer or the protein fraction, in an attempt to isolate which individual bacterial component(s) in the whole cell are most responsible for providing viral protection. Ongoing work will also aim to characterize the most protective bacteria and will assess whether the composition of an individual’s respiratory microbiome may be a previously underappreciated factor contributing to super-spreader events.

ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. I would like to thank Dr. Shannon David and Prof. Tamar Kohn for their mentorship and welcoming me to the project. I would also like to thank the Ingenium team for their help in putting together this manuscript.

REFERENCES [1] Li et al. Viruses. 15(1), 116 2023.

[2] Cowling et al. Nat Commun 4, 2013.

[3] Yagi et al. Int J Mol Sci 22(19), 2021 Oct. [4] Santacroce et al. MDPI Biology. 2020 [5] Karst, S. Nat Rev Microbiol 14, 197–204, 2016.

[6] Smith et al. PLOS Pathogens 9(3) : e1003238.

[7] Budicini, Pfeiffer. ASM Journals 7(3). 2022 [8] Yang et al. PLOS ONE. 2012 Oct.

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Binder Jet Printing and Permeability Testing of Porous Metallic Shapes For Filtration Applications Steven Panico1, Pierangeli Rodriguez1, Markus Chmielus1 Chmielus AM3 Lab, Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 1

Steven Panico

Pierangeli Rodriguez

Steven Panico is a junior undergraduate Materials Science and Engineering student. He assists in research in Dr. Chmielus’s Lab and Dr. Jacobs’s Lab in the Mechanical Engineering and Materials Science Department. Post graduation he plans to pursue a doctorate in Materials Science and Engineering.

Pierangeli Rodriguez is a PhD student in Prof. Markus Chmielus’s lab in the Mechanical Engineering and Materials Science Department at the University of Pittsburgh. Her research interests focus on Binder Jet 3D printing and characterization of metallic materials for various applications including Nibased superalloys, tungsten carbide and magnetic shape memory alloys.

Dr. Markus Chmielus is an Associate Professor and Materials Science and Engineering Program Director in the Mechanical Engineering and Materials Science Department of the University of Pittsburgh. His areas of research focus on advanced manufacturing of metals, carbides, Markus and functional magnetic materials.

Chmielus The combining umbrella of his research is quantitative, correlative characterization of microstructure, defects, mechanical, electrical, magnetic, and thermal properties over several length scales.

Significance Statement

The work presented in this paper provides information on the resulting porosity and air permeability of Binder Jet Printed powder feedstock as affected by various sintering treatments and sample thicknesses.

Category: Experimental Research

Keywords: Binder Jet Printing, Darcy Flow, Porosity, Permeability

Ingenium 2024

ABSTRACT Due to a need to characterize Binder Jet printed porous filters in a non-destructive manner, an in-house method based on pressure drop measurements for the printed samples was devised. Once the system was tested and the results validated, the effects of sintering time and sample thickness on porosity and permeability were observed. It was found that the extent of sintering samples underwent heavily impacted permeability, with longer sintering times leading to denser and less permeable samples. Sample thickness had a less significant effect on permeability, but greatly affected the minimum required flow speed to be within the range of Darcy Flow.

1. INTRODUCTION Metallic foams can be described as a metal with a highly porous metal matrix. Depending on the quantity, size, production method, and interconnectivity of the pores in the foam, fluids such as air and water are able to flow through the foam with varying degrees of permeability. The porosity and permeability afforded by metal foams holds applications in a multitude of fields, but predominantly metal foams have found a niche in filtration, catalysts, cooling devices, and lightweight material applications [1, 2].

Most traditional casting methods for metallic foams can only make solid structures with isolated pores. There exists a barrier of difficulty in creating interconnected porous matrices in metal foams that cannot be overcome when utilizing traditional casting methods [1]. In response to this, Additive Manufacturing (AM), specifically Binder Jet Printing (BJP) has been used to fabricate metal foams with a more interconnected metal matrix, similarly to Powder Injection Molding (PIM) [2]. BJP is a non-laser method of AM that lays layers of metal powder and a binding solution in specific patterns which can then be cured and sintered into a solid metal component. Through much trial and error and depending on the design of the print and sintering regime [3], the porosity of the final foam can be controlled and accurately predicted.

As briefly mentioned earlier, a relevant property of metal foams is permeability. Non-destructive testing of many metal foam properties proves to be rather difficult when attempting to use the present examination methods. Current procedures for microstructural examination include 2D sectioning and imaging of samples, and use of costly Micro-computed X-ray tomography. 2D sectioning, while able to provide accurate information regarding the properties of a metal foam, requires the sample to be mounted in resin as well complete destruction of the metal sample.

Micro-computed X-ray tomography allows the sample to remain intact, however as mentioned earlier, this method is quite costly and not ideal for mass testing purposes. Because of this, characterizing metallic foam samples non-destructively can lead to easier and faster 85

data collection with respect to permeability dictated by connected porosity.

This paper seeks to examine a non-destructive method of characterization for BJP samples following these restrictions and utilizing a pressure drop set up that enables the calculation of sample permeability using Darcy’s Flow equation. Coinciding with testing the validity of the system, an examination into how the BJP assembly and sintering process affect the calculated permeability, porosity, and ideal Darcy region flow speeds will occur. It should be noted that non-destructive characterization is not a novel advancement, and labs globally use their own in-house systems.

2. MATERIALS AND METHODS

The large output data in the computed tables were processed in R Studio to perform line fitting and calculate the breakoff points of the three-regime-flow piecewise function. Once the Darcy flow region was found, the upper and lower bounds of Darcy flow region speed were measured from the line fitting and put back into the original corresponding data tables.

The permeabilities that had corresponding flow speeds in the Darcy flow region were identified and averaged to achieve an average permeability value for each sample. The permeability in the Darcy flow region was calculated using Equation 1, where k is permeability in m2, µ is dynamic viscosity in Pa-s, q is the flow rate in m3/s, and p is inlet-outlet pressure drop in Pa, in this case, for a compressible fluid (air).

(1)

Shown in Figure 1 is the pressure drop testing setup. A disc-shaped sample is placed into the metal chamber and bolted down between two rubber disks until an airtight environment is achieved. Three different nominal sample thicknesses (1 mm, 3.2 mm, and 5 mm) were binder jet printed with gas atomized Inconel 625 powder and tested utilizing this set-up. Coinciding with the three different sample thicknesses, three different heat treatments were tested as well. Samples were sintered at 1270°C for 0.5 h and 2 h, were not sintered and left “green” (as printed and cured). Sample density and porosity were measured using Archimedes density calculations.

An Arduino is present to catalogue the instantaneous downstream flowrate and send the data as a text file to a MATLAB script. The inlet and outlet pressures are measured using sensors and sent to another MATLAB script (Figure 2 is a flow chart representing this process). The recorded data was then used to study the pressure gradient response to the different applied flow rates, estimate flow regimes, calculate sample permeability, and compare amongst samples.

The flowrate of the system is controlled by a power supply, with the flowrate being altered in correlation to voltage changes in a range from 5 – 10 V. Each sample was tested at 26 different voltages, starting from 5 V to 10 V with a 0.2 V step increase in each test, and each test was repeated three times to evaluate the consistency of the testing setup. Using this data, tables were computed comparing the flowrate, flow speed, and pressure drop for the 0.5 h, 2 h, and sintered samples for all sizes. These tables allowed for an examination of filter efficiency and were used as the basis for examining sample permeability under Darcy and non-Darcy flow equations [4].

Figure 1: Current pressure-drop testing device with labeled parts.

Figure 2: Flowchart of the experimental setup.

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Ingenium 2024

Corresponding with the visual analysis of resulting permeability values for each condition, single factor Analysis of Variance (ANOVA) calculations were performed in Excel for the permeability of 1 mm samples, 3.2 mm samples, 5 mm samples, all 0.5 h sintered samples, and all 2 h sintered samples to verify the effect of sintering time at each sample thickness and for all the 0.5 h and 2 h samples to verify the effect of sample thickness at each sintering time.

Darcy Permeability Standard deviation [m2]

Sample description

Porosity [-]

Darcy Permeability Average [m2]

5 mm 2 h

0.36

1.47·10 -13

2.54·10 -14

5 mm 0.5 h

0.44

3.24·10 -13

5.69·10 -14

3. RESULTS AND DISCUSSION

3.2 mm 2 h

0.39

1.71·10 -13

4.34·10 -14

Results show that the testing setup is highly consistent as air flow runs at the same voltage value leading to nearly the same pressure drop reading. Data for each sample thickness and sintering time combination was first analyzed by plotting the flow speed vs. the compressible pressure gradient measurements and approximating the cutoff points where the trend starts and ends being linear. Then, using the segmented package and function in R Studio, the cutoff approximations were fed into the linear model to find the region with highest linearity (Figure 3). The first quadratic portion is labeled the pre-Darcy flow regime, the linear section is the Darcy flow regime, and the last quadratic section is identified as the Forchheimer flow regime, similar to those indicated in [5]. For each section the fit equation and R 2 value were also calculated for validation. R 2 values were above 0.97 in all calculated Darcy flow regimes.

3.2 mm 0.5 h

0.43

2.80·10 -13

5.62·10 -14

1 mm 2 h

0.35

2.57·10 -13

5.26·10 -14

1 mm 0.5 h

0.42

2.99·10 -13

6.77·10 -14

Tested green samples easily crack during air flow testing because of their low density and loosely bound particles. Shown in Figure 3 is a line fit graph of one of the flowrate experiments (sample sintered for 2 h, 1 mm thickness). In this case, the chosen region of importance for permeability calculations is the green dotted section in the middle, as it has a linear slope which is indicative of Darcy flow.

Table 1. Porosity and average permeabilities on the Darcy flow regime for each sample.

Shown in Table 1 are the average permeabilities for each sample while within the Darcy flow region [6].

Overall, all the calculated permeabilities are within expected ranges when compared to other similar permeability tests in permeability studies of additively manufactured porous metals, with literature values ranging from around 0.5E-13 to 5.0E-13 m2 and the experimentally calculated permeabilities ranging from 1.47E-13 to 3.24E-13 m2. [7, 8]. With the system validated, a proper discussion of the BJP process’s effect on sample permeability and porosity can be undertaken.

Examining the results of the average Darcy flow permeability some noticeable trends begin to form.

For each sample thickness, the permeability of the 0.5 h sintered sample was higher than the permeability of the 2 h sintered samples, as consistent with the higher porosity corresponding to lower sintering times.

Regarding sample thickness, samples sintered for 2 h had a slight permeability decreased with thickness increments, and smaller measurement variations for thicker samples. For samples sintered for 0.5 h, the permeability value was similar for all sample thicknesses.

Figure 3. Flow Speed vs. Pressure gradient for the 2 h sintered 1 mm sample, showing the three flow regimes and their line fittings (blue = pre-Darcy, green = Darcy, orange = Forchheimer).

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Figure 4. Standard Error of permeabilities and associated p-values

Shown in Figure 4 are the results of the ANOVA calculated p-values, using a significance level of 5%. The null hypothesis indicates that there is no difference between the groups being compared and the alternative hypothesis that there is a difference between the means of the groups being compared.

For the comparisons where the p-value is smaller than 0.05, the null hypothesis is rejected. Therefore, there is a significant difference between the samples sintered at 2 h, indicating the effect of sample thickness matters for such sintering conditions, but it does not when sintering for 0.5 h because of the loosely bound particles and high porosity regardless of the samples.

Lastly, for the 3.2 mm and 5 mm samples the null hypothesis is also rejected meaning there is a statistical difference when sintering such thicknesses at different times, but there is no difference for the very thin samples (1 mm).

4. CONCLUSION The data gathered within these experiments suggests that the non-destructive pressure drop based testing method works for characterizing BJP printed and sintered samples. Furthermore, the effects of sintering treatments and sample thickness were explored. It was observed that longer sintering times impacted the permeability of samples through increased density, which contributes to pore separation. Sample thickness had a smaller impact on permeability measurements and was more significant for the 2 h sintered samples.

However, thicker samples had a smaller permeability standard deviation.

5. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering, the Office of the Provost at the University of Pittsburgh. The author also acknowledges support from the MDS-Rely Center to conduct this research. The MDS-Rely Center is supported by the National Science Foundation’s Industry–University Cooperative Research Center (IUCRC) Program under award EEC-2052662 and EEC-2052776. Additional thanks to Jose Morales for assistance in sample preparation and experimentation.

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REFERENCES [1] Davies, G. J., & Zhen, S. (1983). Review Metallic foams: their production, properties and applications. In JOURNAL OF MATERIALS SCIENCE (Vol. 18).

[2] García-Moreno, F. (2016). Commercial applications of metal foams: Their properties and production.

In Materials (Vol. 9, Issue 2). MDPI AG. https://doi. org/10.3390/ma9020085 [3] Hong, G., Liu, J., Cobos, S. F., Khazaee, T., Drangova, M., & Holdsworth, D. W. (2022). Effective magnetic susceptibility of 3D-printed porous metal scaffolds.

Magnetic Resonance in Medicine, 87(6). https://doi. org/10.1002/mrm.29136 [4] Zeng, Z., & Grigg, R. (2006). A Criterion for NonDarcy Flow in Porous Media. Transport in Porous Media, 63(1), 57–69. https://doi.org/10.1007/s11242005-2720-3 [5] Q. Liu, Y. Cheng, J. Dong, Z. Liu, K. Zhang, and L. Wang, “Non-Darcy Flow in Hydraulic Flushing Hole Enlargement-Enhanced Gas Drainage: Does It Really Matter?,” Geofluids, vol. 2018, 2018, doi: 10.1155/2018/6839819.

[6] M. Dejam, H. Hassanzadeh, and Z. Chen, “PreDarcy Flow in Porous Media,” Water Resour Res, vol. 53, no. 10, pp. 8187–8210, Oct. 2017, doi: 10.1002/2017WR021257.

[7] T. Furumoto et al., “Permeability and strength of a porous metal structure fabricated by additive manufacturing,” J Mater Process Technol, vol. 219, pp. 10–16, 2015, doi: 10.1016/j. jmatprotec.2014.11.043.

[8] D. Xie and R. Dittmeyer, “Correlations of laser scanning parameters and porous structure properties of permeable materials made by laserbeam powder-bed fusion,” Addit Manuf, vol. 47, Nov. 2021, doi: 10.1016/j.addma.2021.102261.

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System-level modeling & analysis of Pennsylvania plastic waste: Implications for a circular economy Caymus R. Ruffnera, Vikas Khannaa Department of Civil & Environmental Engineering, Pittsburgh, PA a

Caymus Ruffner

Caymus Ruffner is a senior environmental engineering student.

Pursuing the sustainability certificate and with a love of the planet, he is eager to reduce the impact industry has on the environment while also recognizing and appreciating the importance of working with communities to help achieve such ends.

With a PhD in chemical engineering and MS in applied statistics, Vikas Khanna’s work now focuses on the development and application of systems-based approaches for understanding sustainability and resilience in engineered and natural systems. At multiple scales ranging from narrow Vikas Khanna processes to life cycle, economy, and ecosystem scales, his variety of projects incorporate methods from engineering, applied statistics, network theory, and economics.

Significance Statement

Plastic disposal and recycling data exist on national and state levels, but not on county or municipal levels.

These small-scale data are necessary for proper development and implementation of improved waste management policies. Recycling rates are not increasing as fast as is necessary. The results are generalized through predictive modeling.

Category: Computational Research

Keywords: Plastic, Waste management, Recycling, Predictive modeling Abbreviations: #1 Polyethylene terephthalate (PET), #2 High-density polyethylene (HDPE), #3 Polyvinyl chloride (PVC), #4 Low-density polyethylene (LDPE), #5 Polypropylene (PP), #6 Polystyrene (PS), #6 Expanded polystyrene (EPS), Mixed / Other Plastic, Film/wrap/bags (FWB), Bulky rigid plastics (Bulky), Mixed plastic packaging #3 - #7 (MP), Remainder/ composite plastics (RC), Pennsylvania (PA), Department of Environmental Protection (DEP), Federal Reserve Bank of St. Louis’s Federal Reserve Economic Data (FRED®). Metric tons of carbon dioxide equivalent (MTCO2e), Greenhouse gas (GHG), Market value (MV), U.S. Dollars (USD), Million metric tons (MMT), Petajoules (PJ)

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Ingenium 2024

ABSTRACT While plastic waste data are available on national and state levels, such data are scarce on county and municipal levels, yet it is on small scales that proper improvements in plastic waste management can be made. This study seeks to examine Pennsylvania (PA) plastic waste management, county-level trends from 2001–2020, and embodied impacts of disposed plastic waste. Generated, landfilled, and combusted plastic waste quantities have steadily increased from 2001– 2020; however, both recycled quantities and recycling rates have decreased since 2017, now only marginally above 2001 rates. In 2020, recoverability ranged from 0.2% for Bulky Rigid plastics (Bulky) to 40.4% for high-density polyethylene (HDPE). In 2020, disposed plastic waste represented a great loss in market value, greenhouse gas (GHG) emissions, energy demand, and embedded carbon. Of the 67 PA counties, 36 landfilled more than 90% of plastic waste in 2020, and 52 landfilled more than 80%. Linear regression, decision tree, and random forest models were created to predict recycled plastic waste for a county, and results indicated that they could reasonably be used to predict recycling totals for other U.S. counties. Diversion of plastic waste stands as a significant opportunity for economic growth and environmental stewardship, but increased local policy, education, and investment are needed.

1. INTRODUCTION The global production of plastics, whose use has increased substantially since first being commercialized in the 1950s, has jumped from 2 million metric tons (MMT) of plastic at said commercialization to around 360 million metric tons in 2018. Plastics are keenly pervasive, used across industries for both industrial and consumer use. Consequently, 8 billion tons have been produced since the 1950s. Furthermore, only about 9% of produced plastics have been recycled, leaving the balance to be disposed into the environment [1]. What’s more, primary plastic production is increasing while recycling rates are falling; in the United States (US), generation has increased 40% from 2000 to 2018 and recycling rates fell from 8.7% in 2018 to 5–6% in 2021 [2]. Developments in recycling, plastic waste prevention, and utilization strategies are not only necessary to reduce the detrimental impacts of their thousand-year degradation period, but also to shift their linear life cycle to a circular one. Tracing plastic waste from generation to disposal (or recycle) via systems-based analysis is essential for creating circular economy strategies for plastic waste.

Ingenium 2024

While plastic disposal and recycling data exist on national and state levels, these data are near nonexistent on a county-level basis. Meaningful change in the waste management industry can best be affected on a local level. Effective policies must be developed on small scales where local differences in waste generation, composition, and infrastructure are considered. For that to happen, municipalities must have some idea of how their management is currently fairing. The goal of this work was to estimate, on a county-basis for Pennsylvania (PA), the generation, disposal, and recycling of PA-generated plastic wastes and resins; estimate recoverability, greenhouse gas (GHG) emissions, energy demand, embedded carbon, and market value (MV) of generated and disposed plastics wastes; visualize recycling trends from 2001– 2020; and develop county-level predictive models for plastic recycling rates. All numbers henceforth are for PA only, unless stated otherwise.

2. METHODS Most of the recycling and disposal data was acquired from disposal facility [3] and recycling facility [4] reports submitted to the PA Department of Environmental Protection (DEP) on quarterly and yearly bases, respectively, as well as the latest 2021 Waste Characterization Study performed by the PA DEP [5].

Data used in the predictive models (except recycled plastic quantities and material recovery facility (MRF) numbers, which were this study’s own estimates) were obtained from the Federal Reserve Bank of St. Louis’s Federal Reserve Economic Data (FRED®) [6] and the Population Reference Bureau [7]. All analysis, modelling, and data visualization were performed in R (ver. 2023.03.1+446) except the choropleth and countyto-MRF distance relationship which were created and analyzed in ArcGIS Pro (ver. 3.1). All data wrangling was done in either R or Excel.

3. RESULTS Annual plastic waste generation (in MMT) has increased from 1.05 in 2001 to 1.72 in 2020. Of this, landfilled quantities have increased from 0.73 to 1.15, incinerated from 0.22 to 0.40, and recycled from 0.10, reaching a peak of 0.22 in 2016, then falling back down to 0.17 in 2020 as seen in Fig. 1a. The percentages of plastic waste managed by each method have remained fairly constant from 2001–2020, with landfilled and combusted percentages staying around 70% and 20%, respectively. Fig. 1b shows the change in the percentage of generated plastic waste recycled over time; there is a peak of 12.9% in 2016 before falling to 9.8% in 2020. From 2015 to 2017, plastics recycling rate fell 1.53 percentage points and weight of recycled plastics waste fell 11%.

Figure 1. a) Total plastics waste, 2001–2020 b) % of generated plastics waste recycled, 2001–2020

Three predictive models were created: linear regression, decision tree, and random forest. Nine explanatory variables were chosen (population, land area, % of population that are minorities, per capita personal income, income inequality, gross domestic product (GDP) of private service-providing industries, GDP of government and government enterprises, % of population with a bachelor’s degree or higher, and # of MRFs within county) to predict one response variable (total plastics recycled). Eleven years of data for 67 PA counties was used for a total of 737 data points. The linear regression model had an R 2 of 0.7839 and RSE of 2319. The decision tree had an R 2 of 0.8025 and root mean squared error (RMSE) of 2222.7. The random forest model had an R 2 between 0.85 and 0.88 and an RMSE between 1800 and 2100.

Resin embodied impacts were determined for 2020 alone. The recoverability of polyethylene terephthalate (PET), high-density polyethylene (HDPE), #3–#7 plastics, film/wrap/bags (FWB), and bulky rigid plastics (Bulky) were 30.4%, 40.4%, 16.3%, 1.1%, and 0.2%, respectively.

All other estimates (e.g. GHG emissions, MV) are listed in Table 1. FWB and Bulky plastics are separate from #3–#7 plastics for all estimates. Expanded polystyrene (EPS) is included in #3–#7 plastics for recoverability but excluded for Table 1 estimates. These estimates represent the GHG emissions, energy input, and embedded carbon embodied in the production of disposed plastic waste as well as the MV (using 2020 prices) that could have been obtained had the waste been recycled instead; they do not account for the embodied costs of the disposal process. Some of the largest estimates are for EPS and Bulky, with Bulky having by far the lowest recoverability. The MV of disposed PET and HDPE is significantly lower than other resin groups, primarily due to their high recoverability.

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Resin

GHG Emissions (MTCO2e)

Market Value (million USD)

Energy Demand (PJ)

Embedded Carbon (MMT)

#1 PET Plastic

391743.7

89.313

9.104

7.700

#2 HDPE Plastic

257466.7

103.110

8.956

10.548

#3–#7 Plastic

311670.3

137.408

9.142

10.587

Expanded Polystyrene (EPS)

448411.0

189.096

10.681

11.365

Film/Wrap/Bags (FWB)

269785.7

149.236

8.872

10.495

Durable/Bulky Rigid Plastics

484136.0

130.717

10.270

9.699

Remainder/ Composite Plastic

437323.9

131.658

9.874

9.726

TOTAL

2600537.3

930.538

66.898

70.119

Table 1. Embodied impacts of 2020 disposed plastic waste

It was desirable to examine the proportion of landfilled plastic waste and to investigate the relationship between a county’s distance to MRFs and its recycling rate. The SE and SC regions landfilled the least plastic waste while the NW and SW regions landfilled the most.

As can be seen in Fig. 2, York, Dauphin, Lancaster, Forest, and Delaware counties landfilled less than 15.9% of their plastic waste. Of the 67 counties, 36 landfilled more than 90% and 52 landfilled more than 80%. Linear regression analysis of a county’s distance to the nearest plastic accepting MRF and its recycling rate returned an R 2 of 0.03553 indicating no relationship between the two.

Figure 2. Plastic waste facilities and % plastic waste landfilled

4. DISCUSSION The recovery of total plastics in PA agrees with national trends for later years since PA recycles significantly more than the US average in 2001 but recycles only slightly more in later years as other states improve their recycling. US and PA recovery was 5.5% and 9.3% in 2001, respectively [8]; 7.96% and 11.2% in 2010 [9]; and 8.66% for US in 2018 and 9.8% for PA in 2020 [9].

US recovery of PET and HDPE was 29.1% and 29.3% respectively, a 10% deficit for HDPE compared to PA in 2020 [9]. The large drop in PA recycling rates and weight of plastics recycled from 2015 to 2017 agrees with similar declines experienced across the US during that time [10][11]. These atypical declines were accompanied by the announcement of China’s National Sword Policy and the continuation of a slow economy.

Due to the quantity of EPS in disposed waste, some of the greatest values for each estimate are from EPS, most notably MV. Large Bulky estimates and low recoverability are most likely due to their low recyclability and low market demand after recycling.

MV estimates should be taken with caution as MVs can vary greatly even within a single year and finding accurate unit resin prices proved difficult. 2020 PA energy demand values are in accordance with 2019 US embodied energy estimates in [12] with errors <= 4.9% except PET, which had an error of 6.5%. PA estimates were multiplied by 50 (due to 50 states) and compared with US values. Direct comparisons were possible for PET and HDPE. Remainder/composite plastics (RC) in Table 1 was compared with Other in [12]. The sum of all other Table 1 categories was compared with the sum of all other categories in [12].

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The predictive models displayed linearity in data and low influence from individual points, but also heteroscedasticity and high multicollinearity.

Heteroscedasticity only affects the linear regression model and was solved by transforming the response variable from total recycled plastics weight to per capita total recycled plastics weight; however, this returned a substantially lower R 2 of 0.1615, and the original linear regression model is recommended. Other steps can be taken to reduce multicollinearity in the future. Despite this, the R 2 values indicate that the models could reasonably be used to predict recycling totals for other US counties or other locales. The random forest model is recommended over linear regression and decision tree models.

Low landfill rates indicated in Fig. 2 are primarily due to high incineration rates. Both SE and SC regions house the only waste-to-energy (WTE) facilities in PA and are the only regions with any meaningful quantity of incinerated waste. However, they also have the 2nd and 3rd highest recycling rates respectively. Of the five counties in the 0–15.9% interval, Forest County is the only one whose low landfill rate is due to a high recycling rate. Lack of correlation between recycling rate and proximity to an MRF is most likely due to the relatively high costs of recycling, especially for a state with among the most prominent landfill infrastructure in the country. Lack of correlation may also be due to insufficient capacities of MRFs; MRFs can vary greatly in how much and what kinds of material they accept, and neither the distance relationship nor the predictive models took this variation into account.

5. CONCLUSION While plastic waste data are available on national and state levels, such data are scarce on more local levels where improvements can truly be made, thus preventing necessary advances in plastic waste management. A careful search for similar studies indicated that this study is the first to examine plastic waste management and its 20-year trends on a countylevel basis, which it does for Pennsylvania. Generated and disposed plastic waste quantities have steadily increased since 2001, however the percentage of waste managed by landfill and combustion have remained near 70% and 20%, respectively. Plastic recycling rates and quantities increased up to 2016 and have since seen a semicontinuous decline. In 2020, recoverability of PET, HDPE, #3–#7 plastics, FWB, and Bulky were 30.4%, 40.4%, 16.3%, 1.1%, and 0.2%, respectively.

Disposed EPS, FWB, and #3–#7 plastics represented the greatest loss of MV; disposed Bulky and PET, the greatest GHG emissions; and disposed EPS and Bulky, the greatest energy demand. While the data presented are for PA, the predictive models allow for similar estimates to be determined for other US counties.

Except Forest County, low landfill rates correspond to high combustion rates. Of the 67 counties, 36

landfilled more than 90% and 52 landfilled more than 80% in 2020. The recent decline in recycling is a wake-up call to domestic waste management. The US can no longer depend on the exportation of waste, and the plausibility of plastic recycling is diminishing.

With the aid of small-scale data like those presented in this paper, municipalities of all levels must first and foremost implement policies and educational campaigns to reduce the use of plastics and only lastly increase investment in proper recycling infrastructure.

6. ACKNOWLEDGEMENTS: Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. A big thanks is due to Veronica Harris, Recycling County Coordinator of Montgomery County, Pa., for her assistance with all questions. A thanks is due to Joy Smallwood of Allegheny County and John Nantz and Mindy Waltemyer of York County for their assistance.

REFERENCES [1] J. Zappitelli, et al. “Quantifying Energy and Greenhouse Gas Emissions Embodied in Global Primary Plastic Trade Network,” ACS Sustainable Chemistry & Engineering, vol. 9, no.

44, p. 14927–14936, 2021. Available: 10.1021/ acssuschemeng.1c05236. [Accessed: May 18, 2023].

[2] Beyond Plastics, “The Real Truth About the U.S. Plastics Recycling Rate,” Beyond Plastics, 2022.

[Online]. Available: www.beyondplastics.org/.

[Accessed: Jan. 6, 2024].

[3] PADEP, “DEP Bureau of Waste Management Disposal Info,” PADEP, 2023. [Online].

Available: http://cedatareporting.pa.gov/reports/ browse. [Accessed: May 26, 2023].

[4] PADEP, “Statewide Recycling Data,” PADEP, 2023.

[Online]. Available: https://files.dep.state.pa.us/.

[Accessed: Dec 26, 2023].

[5] PADEP, “Waste Characterization Study Final Report,” PADEP, 2022. [Online].

Available: https://files.dep.state.pa.us/. [Accessed: May 26, 2023].

[6] Economic Research, “FRED: Economic Data,” Federal Reserve Bank of St. Louis, [Online]. Available: https:// fred.stlouisfed.org/. [Accessed: Jul. 2, 2023].

[7] PRB, “Percent of the Population That Are Racial/ Ethnic Minorities of Pennsylvania,” PRB, 2023.

[Online]. Available: https://www.prb.org/. [Accessed: Jul. 2, 2023].

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[8] USEPA, “Municipal Solid Waste in the United States: 2001 Facts and Figures,” USEPA, 2003. [Online].

Available: https://www.epa.gov/nscep. [Accessed: Jun. 17, 2023].

[9] USEPA, “Plastics: Material-Specific Data,” USEPA, 2023. [Online]. Available: https://www.epa.gov/.

[Accessed: Jul. 12, 2023].

[10] J. Paben, “EPA: US recycled less plastic in 2017,” Resource Recycling, Inc. 2019. [Online]. Available: https://resource-recycling.com/plastics/. [Accessed: Jan. 4, 2023].

[11] American Chemistry Council and Association of Plastic Recyclers, “2017 United States National Postconsumer Plastic Bottle Recycling Report,” American Chemistry Council, 2017. [Online].

Available: www.americanchemistry.com/. [Accessed: Jan. 5, 2023].

[12] A. Milbrandt, et al. “Quantification and evaluation of plastic waste in the United States,” Resources, Conservation and Recycling, vol.

183, 2022. Available: https://doi.org/10.1016/j. resconrec.2022.106363. [Accessed: May 15, 2023].

94 Undergraduate Research at the Swanson School of Engineering

Modeling Materials and Configurations of Radioisotope Thermoelectric Generator (RTG) Heat Sources Ganesh Selvakumar1, Dr. Matthew M. Barry1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 1

Ganesh Selvakumar

Matthew Barry

Ganesh Selvakumar is a junior majoring in Engineering Science with a Mechanics concentration at the University of Pittsburgh. His academic interests include computational fluid dynamics, heat transfer, and computer science. He plans to continue his academic interests in graduate school.

Matthew Barry is an Associate Professor in the Mechanical Engineering department at the University of Pittsburgh. His research focuses on thermoelectrics and engineering education. Dr. Barry teaches several introductory engineering classes at the University of Pittsburgh.

Significance Statement

NASA JPL currently does not have the tools or models to conduct system-level trade studies of existing and novel RTGs, considering the path of heat from the Pu238 pellets all the way to the heat exchange system, which limits their ability for future deep-space and subsurface missions.

Category: Computational Research

Keywords: Radioisotope thermoelectric generator, Deep space power generation, Compact heat source, Thermal Resistance Network

Ingenium 2024

ABSTRACT The 2022-2032 Decadal Survey has demonstrated the diverse scientific interest in exploring other worlds within our solar system. A number of these missions could be enabled or enhanced with radioisotope powered systems (RPS). Such destinations range from our moon to beyond the orbit of Neptune. While production of the radioisotope Pu-238 has been restarted in the U.S. to enhance NASA’s RPS portfolio, the demand for RPS still outweighs the supply. While Pu-238 is an excellent source of thermal energy for RPS in general, the situation puts forth the opportunity to assess the theoretical usefulness of additional heat source materials where appropriate and whether such heat source materials could alleviate Pu-238 demand.

To understand these potentials, the work herein builds upon prior work utilizing fast computing parametric system-level modeling of radioisotope thermoelectric generators (RTGs). The developed solvers iteratively and automatically predict the power optimal RTG configuration given a multitude of system parameters.

To better understand the effect of new heat source materials on RTG performance, a system-level model, which models the heat source materials and configurations, thermoelectric converters, and heat exchange system, must be created. To this end, this work focuses on creating a fast-compute heat source material and configuration model that provides the necessary thermal boundary conditions for existing thermal-electric coupled thermoelectric converter models.

This study could ultimately serve as a starting point towards future pursuits toward the aim of augmenting and improving NASA’s existing RPS isotope profile both in capability and efficiency.

INTRODUCTION The National Academies of Sciences, Engineering, and Medicine 2022 decadal survey [1] has indicated that NASA “...should evaluate [P]lutonium-238 [(Pu-238)] production against the mission portfolio recommended in this report and other NASA and national needs and increase it, as necessary, to ensure a sufficient supply to enable a robust exploration program at the recommended launch cadence.” It has been previously recommended that Pu-238 production be restarted, with targeted production rates of 1.5 [kg/yr]; this goal is likely to be achieved by 2026. Currently, Pu-238 is transformed into Plutonium-dioxide (PuO2) pellet clads at a rate of 1.5 [kg] of Pu-238 to 10 PuO2 clads per year, and there exists a stockpile of approximately 200 PuO2 clads. This current rate of production “remains a significant limiting factor in NASA’s ability to develop new deep space missions.” For example, the Multimission Radioisotope Thermoelectric Generator, Next Generation Mod 1 RTG, and General Purpose 95

Heat Source (GPHS) Radioisotope Power System (RPS) require 32, 64, and 72 clads, respectively. It is noted “RPS demand far outweighs availability in the upcoming decade.”

Furthermore, it is recommended that “NASA should continue to invest in maturing higher-efficiency [RPS]... to best manage its supply of [Pu-238] fuel.” Given the planned pace of production of new PuO2 fuel clads, as well as the limited efficiency of state-of-practice RPS, addressing the strong mission pull suggests that an assessment should be done of the possible use of additional isotopes and heat source packaging configurations to further expand and enhance RPS capabilities. Assessing the potential infusion of more efficient thermal to electric conversion technologies likewise will serve to this end. Isotopes other than Pu-238 have been studied for the purpose of radioisotope powered missions in space [2]. Ambrosi et al. have evaluated feasibility of Am-241 as a potential radioisotope fuel for RTGs. Other available isotopes include, but are not limited to, Cobalt-60 (Co-60), Strontium-90, and Strontium-titanate. Additionally, PuO2 Am-241 blends have the possibility of extending the effective lifetime of the RTG while alleviating the demand on Pu-238. Coupling these alternatives with a Compact Heat Source (CPHS) configuration, higher than-conventional specific thermal powers could be achievable [3], particularly by reducing the RTG length substantially in instances of low-power density isotope fueled missions.

2. METHODOLOGY 2.1 Finite Volume Modeling To generate benchmark quality data, a threedimensional finite volume model of a PuO2 fuel pellet with iridium cladding was constructed in ANSYS CFX Version 19.2. First, a mesh was created in ANSYS ICEM CFD Version 17.2. Blocks were used to create highly orthogonal, conformal, hexahedral mesh elements.

Table I provides the mesh quality statics for the coarse mesh. It is seen that 93.835% of the mesh elements had a quality of 0.85 or higher.

Three successive meshes were created for two studies.

The coarse, medium, and fine meshes had 331,200, 3,197,440, and 25,579,520 hexahedral elements, respectively. These meshes are shown in Fig. 1. The first study was a grid independence analysis. The results of the grid independence study, as shown in Tab. II, indicate that the solution obtained was mesh independent (i.e., the values of interest monotonically approached a value). The second study quantified the numeric uncertainty due to discretization of the constitute equations in a finite volume form, as quantified by the Grid Convergence Index (GCI). The GCI was calculated by following the detailed procedure outlined by Celik et. al. [7] and Oberkampf and Roy [8]. This procedure is the ASME gold standard for quantifying numerical uncertainty.

In order to evaluate both permissible heat source materials and designs both rapidly and accurately, a fast yet robust system-level model that considers the heat source, thermoelectric converters and heat sinks must be developed. To this end, numeric and analytic models are developed and implemented to determine the thermal characteristics of the heat source materials, cladding and aeroshells. The aforementioned numeric models, although computationally expensive, are pursued to create benchmarks for the analytic models. A variety of analytic models will be developed: one-dimensional axial thermal resistance networks; one-dimensional radial thermal resistance networks; and three-dimensional thermal resistance networks.

Each analytic model will be evaluated in terms of its ability to predict the temperature distribution within the pellet, cladding, and aeroshell.

Quality

Element Count (%)

0.95 → 1.0

229,134 (62.701%)

0.9 → 0.95

64,791 (17.730%)

0.85 → 0.9

48,984 (13.404%)

0.8 → 0.85

11,578 (3.168%)

0.75 → 0.8

4,723 (1.292%)

0.7 → 0.75

3,990 (1.092%)

0.65 → 0.7

1,280 (0.350%)

0.6 → 0.65

587 (0.161%)

0.55 → 0.6

373 (0.102%)

0.0 → 0.55

0 (0.000%)

Table I: Mesh Quality Statistics For Coarse Mesh (331,200 Elements)

As seen in Tab. II, there is no difference in the maximum axial centerline temperature when comparing the medium and fine meshes. Not only was a grid independent solution found, the numeric uncertainty associated with the numeric results is zero percent.

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2.2 Analytical Modeling To reduce run-time, a one-dimensional thermal resistance network (TRN) was investigated using the fuel pellet geometry. MATLAB was used to calculate the nodal temperatures. The iridium cladding was not incorporated into the TRN, since the thickness of the cladding is 0.55 mm [6]. As shown in Fig. 2, the base case of the axial TRN contained four resistors: two resistors within the cylindrical portion of the pellet and one resistor at either end of the pellet. The cylindrical portion of the pellet is divided into four sections as denoted by the dotted lines. At T2, the line divides the portion into two sections; in the heat source model, the dotted lines at R2 and R3 further split the cylindrical portion into four sections.

Figure 1: Coarse, medium and fine meshes of PuO2 pellet with iridium cladding used within numeric model.

Mesh

SRQ

Coarse

1,262.09 [K]

Medium

1,262.01 [K]

Fine

1,262.01 [K]

Fig. 2. Base Case of the Axial Thermal Resistance Network.

Includes 4 resistors (R1, R 2, R 3, R4), 3 nodal temperatures (T1, T 2, T 3), and 3 heat sources volumes (q1, q2, q3)

As shown in Eqn. (2), thermal circuits are analogous to electrical circuits by using Q and ∆T, which are comparable to current and voltage, respectively.

Table II: Grid Independence Study Of Maximum Axial Pellet Centerline Temperature

Within ANSYS CFX, the pellet and cladding were modeled using the Thermal Energy Model.

Temperature-dependent thermal conductivity of the PuO2 was considered by providing

Beginning at the center of the pellet in Fig. 2, thermal energy from Q2 flows outward given by

The thermal conductivity as a function of temperature using CEL Expressions. The temperature-dependent thermal conductivity was expressed using the following polynomial:

The thermal conductivity of the iridium was treated as a constant with a value of 147 [W/m-K].

To model the decay heat of the PuO2, a volumetric heat generation term with a magnitude of 3.62876e6 [W/m3] was applied as a source subdomain within the pellet.

A convergence criterion of 1e−10 was set for the maximum residual of the model. Post-processing of the numeric data was done in ANSYS CFD-Post 17.2.

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Within Eq. (3), q2’’’ is the volumetric heat generation of PuO2 for the subdomain shown in Fig. 2. Heat sources and resistors that represented the interior of the pellet used the pellet’s cylindrical geometry based on its length (Lcylinder) and cross-sectional area (Acylinder). Lcylinder is divided by 2, since Q2 has a subdomain that spans half of the cylindrical portion of the pellet. The heat from Q2 moves through thermal resistors: R 2 and R 3, given as

Eqns. (4a) and (4b) use λ(Th, Tc) to represent the integral average difference of the temperature-dependent thermal conductivity function (λ(T)) using the boundary temperatures of a thermal resistor (Th,Tc). Next in the axial TRN, Q1 and Q3 generate thermal energy for the dome portions of the fuel pellet given by

In Eqns. (5a) and (5b), the heat sources and resistors on the ends used the pellet’s dome geometry based on the dome length (Ldome) and cross-sectional area (Adome).

The end heat sources’ (Q1 and Q3) subdomains span the pellet’s dome portion and one quarter section of the cylindrical portion. The final components of the axial TRN are the dome resistors: R1 and R4, given as

Within Eqns. (6a) and (6b), the dome resistors are calculated in reference to the dome geometry as used in Eqns. (5a) and (5b). When calculating the thermal conductivity, T∞,c is the pellet boundary temperature.

After defining the values of the thermal resistors (R1−4) and heat sources (Q1−3), the nodal temperatures of the TRN can be solved via Eqns (7a), (7b), and (7c):

Based on Kirchoff’s current law for heat transfer, the nodes at T1, T 2, and T 3 generated Eqns. (7a), (7b), and (7c). The equations can then be cast into matrix form to solve for the nodal temperatures within MATLAB.

Figure 3: Thermal Resistance Network Solution Algorithm

Resistors were added within the axial TRN to increase the axial centerline temperature and closely model the numeric three-dimensional fuel pellet. Generalizing the calculations within MATLAB required a solution algorithm shown in Fig. 3. Initially, the algorithm guessed the nodal temperatures of the TRN based on the set number of thermal resistors. Then, the thermal resistors were calculated by finding the temperaturedependent PuO2 thermal conductivity. A corresponding coefficient matrix was generated and solved using the thermal resistors and heat sources to calculate a new set of nodal temperatures. If the maximum residual and error were below the threshold (1e-10), the program ended, otherwise it continued by using the previous iteration’s nodal temperatures. Two resistors were added until reaching the set number of thermal resistors within the TRN.

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3. RESULTS AND DISCUSSION

REFERENCES

The centerline temperature of the analytic model remained constant regardless of the number of thermal resistances added. From the base case up to a one-hundred resistor TRN, the nodal temperature at the center of the pellet was 1380.6 [K]. A possible explanation for the constant centerline temperature can be the algorithm overpredicting by attempting to model a three-dimensional pellet through a onedimensional TRN. These preliminary results indicate the need for a two-dimensional TRN that represents the fuel pellet radially. Other factors not taken into consideration include heat dissipation from the fuel pellet to the GPHS module.

[1] “Origins, Worlds, and Life: A Decadal Strategy for Planetary Science and Astrobiology 2023-2032,” National Academies of Sciences, Engineering, and Medicine and others, (2022).

4. CONCLUSIONS Once an accurate TRN model of the fuel pellet is developed, the goal is to expand the network to the subsequent layers within the GPHS module. Following the pellet, the network will include: the cladding, two fueled clads within a graphite impact shell (GIS), and finally two GIS assemblies within the aeroshell (serves as the main structure of a GPHS). A complete heat transfer model can then be used for alternative fuels, such as Americium-241 and Strontium-90, and different brick configurations like the CPHS. The heat source model will then be incorporated into the other necessary solvers of a system-level RTG model including view factor, thermoelectric conversion, and heat dissipation.

The main next steps needed for developing an accurate TRN model of the fuel pellet is to create a twodimensional model. This model will look at the pellet radially to account for the cylindrical geometry. Then based on Table II, analyzing the model to see if the centerline temperature of the pellet converges towards a value near 1262.0 [K].

5. ACKNOWLEDGEMENTS Computational access and guidance were provided

by Dr. Matthew Barry. Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh.

[2] R. Ambrosi, D. Kramer, E. Watkinson, R. Mesalam, and A. Barco, “A Concept Study on Advanced Radioisotope Solid Solutions and MixedOxide Fuel Forms for Future Space Nuclear Power Systems,” Nuclear Technology 207, 6, pp 773–781 (2021).

[3] M. Durka et al., “A novel high-performance missionenabling multipurpose radioisotope heat source,” 2022 IEEE Aerospace Conference (AERO), 1–10, 2022, IEEE. [4] S. Riley, S. Wielgosz, K. Yu, M. Durka, B. Nesmith, F. Drymiotis, J.-P. Fleurial and M. Barry, “Optimization Methods for Segmented Thermoelectric Generators,” ASTFE Digital Library, Begel House Inc., 2022.

[5] G. Bennett et al., “Mission of daring: the generalpurpose heat source radioisotope thermoelectric generator,” 4th International Energy Conversion Engineering Conference and Exhibit (IECEC), pp.

4096, June 2006.

[6] W. Kelly, N. Low, A. Zillmer, G. Johnson, and E. Normand, “Radiation Environments and Exposure Considerations for the Multi-Mission Radioisotope Thermoelectric Generator,” AIP Conference Proceedings 813, 906 (2006).

[7] I. B. Celik, U. Ghia, P. J. Roache, Procedure for estimation and reporting of uncertainty due to discretization in CFD applications, Journal of Fluids Engineering-Transactions of the ASME 130 (7) (2008) 078001.

[8] C. J. Roy, W. L. Oberkampf, A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing, Computer Methods in Applied Mechanics and Engineering 200 (25-28) (2011) 2131–2144.

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Ingenium 2024

Cyber-Informed Engineering in the Context of Prioritizing U.S. Grid Security

Philippe Van de Putte is a sophomore information science major with a minor in business. He takes a particular interest in cybersecurity.

Isabella Hsia1, Kameren Jouhal2, Shanker Pillai2, Philippe Van de Putte2, Evan Wang3, Daniel Cole4, Brandon Grainger5,6 Department of Bioengineering, Swanson School of Engineering, Pittsburgh, PA 2 Department of Computer Science, School of Computing and Information, Pittsburgh, PA 3 College of Business Administration, University of Pittsburgh, Pittsburgh, PA 4 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 5 Energy GRID Institute, University of Pittsburgh, Pittsburgh, PA 6 Department of Electrical and Computer Engineering, Swanson School of Engineering, Pittsburgh, PA 1

Isabella Hsia is a sophomore bioengineering major with a minor in neuroscience. She takes a special interest in ethics and psychology and plans to pursue a graduate degree in the University of Pittsburgh’s very own Bioengineering in Psychiatry program.

Isabella Hsia

Kameren Jouhal is a sophomore computer science major with a minor in business. His unique interests lie in algorithm design and cybersecurity.

Kameren Jouhal

Evan Wang

Shanker Pillai is a sophomore computer science-computational biology double major. He is especially passionate about the various applications of computer science in other fields such as biology and cybersecurity.

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Significance Statement

The increasing sophistication of cyber threats and development of foreign adversaries puts U.S. electrical grid security at a critical junction. Cyber-informed engineering, championed by Idaho National Laboratory (INL), aims to restructure traditional cybersecurity methodologies by embedding cybersecurity within every level of systems manufacturing, from research, to design, to fabrication.

Category: Other—Market Research

Keywords: cyber-informed engineering, power grid, trade-offs, risk appetite Abbreviations: cyber-informed engineering (CIE), National Institute of Standards and Technology (NIST), North American Electric Reliability Corporation (NERC), information technicians (IT), Idaho National Laboratory (INL), critical infrastructure protection (CIP), Chief Operating Officer (COO), Chief Information Security Officer (CISO)

ABSTRACT Evan Wang is a sophomore finance and business analytics major with a minor in applied statistics. He is passionate about the intersection of analytics in a variety of fields and the disruptive potential of AI/ML solutions.

Shanker Pillai

Philippe Van de Putte

Cyber-informed engineering (CIE) is a methodology that seeks to mitigate high-consequence outcomes through the systematic analysis of assets and thorough integration of cybersecurity considerations into everyday engineering practices. To determine if CIE is desirable when applied in the context of the power grid, a series of 125 interviews was conducted across industry and academia to drive the validation and invalidation of newly formed hypotheses via hypothesis-driven research. This study finds that although a paradigm shift is imminent and sorely needed within the power-grid industry, the foundation for a wide-scale adoption of CIE is still in its early development and may need additional guidance. The authors therefore propose several justifications and a workshop curriculum for the implementation of CIE in grid infrastructure.

Ingenium 2024

1. INTRODUCTION As the cyber threat landscape continues to evolve, the vulnerability of the U.S. grid is becoming increasingly apparent. Threat actors from hostile nation-states possess the ability to disrupt sections of the electric grid even with regulatory institutions such as the National Institute of Standards and Technology (NIST) and the North American Electric Reliability Corporation (NERC) doubling down on grid contributors.

Consequences of a compromised grid include impacts such as financial costs to businesses and loss of human life.

Historically, responsibility for cybersecurity has fallen on the shoulders of information technicians (IT), who deal primarily within digital, or cyber, domains and consequently have minimal experience working directly with physical systems on the floor like operational technicians (OT). Critical infrastructure of the modern world is composed of both cyber and physical components, which calls for collaboration between both IT and OT professionals in order to develop a layered architecture comprised of both physical and cyber protections. This approach is defined as cyber-informed engineering (CIE) [1], first established by Idaho National Laboratory (INL) in 2017. The methodology is grounded in twelve key principles that act holistically to better buffer a vulnerable system from a foreign adversary. Though by no means an absolute solution, cyber-informed engineering aims to protect against the worst-day scenario we see reflected in crises like the 2021 Texas Power Crisis and the 2015 Ukraine Power Event. In fact, the 2015 Ukraine power event was a prime example of an event in which a direct attack on the power grid by foreign (presumably Russian) threat actors led to a loss in power for over 220,000 Ukrainians. Attackers penetrated the network via phishing emails and accessed legit credentials and were able to access and conduct an extensive reconnaissance of the system for months before the attack [2]. The attackers were thus able to infiltrate the companies’ computer networks (IT side) to access critical infrastructure in the grid (OT side) and manipulate the safety mechanisms within said infrastructure such that the entire grid was put out of commission for several hours. Had the infrastructure itself been better buffered against malicious threat actors, then perhaps the incident could have been avoided.

2. METHODS 2.1 Project Allocation The SHURE-Grid program brought together an interdisciplinary group of 8 undergraduate students from the Swanson School of Engineering, the School of Computing and Information, and the College of Business Administration to tackle a set of problem statements posed by Idaho National Laboratory related

to cyber-informed engineering. The group of 8 was randomly divided into two cohorts of 4 students each.

Each of the two cohorts chose one problem statement, with the projects chosen as follows: 1.

Cohort 1, the CIE Enthusiasts, sought to justify the use of CIE within the industry by conducting a thorough investigation of existing company culture and common practices, identifying key positions to be proponents of a top-down strategy and interfacing with both upper-level executives and lower-level personnel.

2.

Cohort 2, Team GPT, focused on methods of CIE deployment across the power grid industry, and developed a 3-day workshop curriculum intended to facilitate IT/OT interaction and introduce the concept of CIE through tabletop exercises and realworld scenarios.

Each cohort formed 3-4 initial hypotheses based upon their preconceptions of the industry, which would then drive their discovery process and outreach that would take place over the next three months.

2.2 Key Activities Over the course of 12 weeks, Cohort 1 conducted 60 interviews with 55 unique interviewees, while Cohort 2 conducted 65 interviews with 60 unique interviewees, totaling 125 interviews lasting 30 to 40 minutes each.

Each week, cohorts would review their formulated hypotheses, and, depending on the results of their interviews up to that point, would either revise, validate, or invalidate their hypotheses, and choose new hypotheses to take their place. Interviewees spanned both industry and academia, with particular emphasis placed on those who could effect change (C-suite executives, board members, etc.) and those who the implementation of CIE would most directly affect (cybersecurity analysts, electrical engineers, etc.). Hypotheses were corroborated with expertise from Eaton Corporation, GE Vernova, and Amazon to name a few, as well as reputable academic institutions like the University of Pittsburgh and Carnegie Mellon University.

Beneficiary workshops were held on a weekly basis with both cohorts to help researchers better understand their intended audience. This included the creation of user archetypes and charting organizational workflows to deduce the hierarchy of decision-making within companies, especially concerning infrastructure security. Weekly discovery reviews, intended for researchers to discuss their findings and demonstrate their progress, were subject to comprehensive review by mentors and other research faculty. At the end of the twelve weeks, personnel from Idaho National Laboratory and the University of Pittsburgh research department came to the Cathedral of Learning for a final “Lessons Learned” presentation.

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3. RESULTS 3.1 Justifying Cyber-Informed Engineering Of the hypotheses collected by Cohort 1, the most relevant are listed as follows: Hypothesis

Result

(1) Executives and higher management believe that compliance with regulations means their system is secure.

0/5 - Invalidated

(2) Companies are more inclined to secure their entire system rather than focusing on securing specific critical assets.

3/8 - Invalidated

(3) People can identify a champion within their organization that campaigns for a specific cause.

10/10 - Validated

Table 1: Cohort 1’s takeaway hypotheses.

Regarding regulatory compliance, the general consensus among cyber-informed executives is that compliance with NERC-critical infrastructure protection (CIP) and NIST regulations is not sufficient to manage modern threats as shown by Hypothesis 1. Our solution introduces the concept of developing a risk appetite; simply put, a risk appetite describes the risk tolerance of a company. A company with high risk tolerance is willing to take on the liabilities that are associated with a malicious cyber event. This ties in with Hypothesis 2, which suggests that most organizations opt to prioritize their most critical assets when working with a budget rather than applying a blanket level of security to their entire system. The importance of an internal “Champion” to advocate for the company-wide adoption of CIE was one of the main focuses of Cohort 1 as suggested by Hypothesis 3. The champion can garner support throughout the organization, thereby catalyzing rapid change in favor of CIE. Cohort 1 found that Chief Operating Officers (COOs) and Chief Information Security Officers (CISOs) can find it onerous to translate security needs into business implications; hence, Solution 1 enables executives to quantify risks and identify how a well-secured company can align with the goals of other executives. Solution 1 is an informative text for the purpose of briefing C-suite executives on CIE principles, how they can incorporate CIE principles into their organization, and how they can foster a security-minded culture. The other major area the book highlights is a list of key justifications that companies should consider when making decisions regarding system security. This book is not meant to force companies into supporting CIE but rather to aid them in rethinking their current cyber capabilities and deciding if CIE is justified based on their risk appetite.

Figure 1: CIE Manual Outline – A table of contents.

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3.2 Trade-Offs for Cyber-Informed Engineering Of the hypotheses collected by Cohort 2, the most relevant are listed as follows: Hypothesis

Result

(1) Tabletop exercises would result in mutual understanding between IT and OT and help to communicate the gravity of a worst-day scenario.

4/4 - Validated

(2) More than simply regular compliance (NIST, NERC-CIP), critical infrastructure engineers concern themselves with OT cyber.

13/14 - Validated

(3) CIE workshops will have long lasting effects; workshop curriculum will be integrated into workers’ practices.

7/8 - Validated

The disconnect that exists between IT and OT is painfully prevalent, as evidenced by Hypothesis 2.

Typically, when an electrical component is put out to market, manufacturing happens separately from the deployment of security measures; a sort of “hand-off” takes place, which greatly impedes the collaborative environment necessary for cyber-informed engineering to survive. Thus, Cohort 2 seeks to introduce cyberinformed engineering to the power grid industry via 3-day in-person workshops, intended to both inform and facilitate meaningful interactions between the IT and OT sectors. Hands-on learning experiences in the form of table-top exercises will be integrated into the curriculum, drawing from nationwide cybersecurity conferences and energy sector exercises. Allowing parties to work through the anatomy of previous catastrophes like the 2021 Texas Power Crisis and the 2016 Ukraine Power Event, as evidenced by Hypotheses 1 and 3, will bring awareness to the industry, encourage needed collaboration between independent sectors, and bring to light the current state of affairs within the power-grid industry.

Table 2: Cohort 2’s takeaway hypotheses.

Figure 2: CIE Workshop Curriculum – A breakdown of key activities.

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4. DISCUSSION

REFERENCES

Interviews yielded insight into how CISOs continue to prioritize compliance with CIP-005-6 [3] and NIST 800-53 [4] regulations over other practices in terms of spending; however, the majority of cybersecurity experts interviewed were unsatisfied with current regulations and policies. In a perfect scenario, a target utility would first evaluate their organization’s stance on CIE by reading the book proposed by Cohort 1 as seen in Figure 1. After a comprehensive analysis of said organization’s cyber culture and risk appetite, the utility would transition into Cohort 2’s workshop outlined in Figure 2 to actualize CIE principles in their own infrastructure. Together, the book coupled with the workshop creates a powerful strategy when considering the unique circumstances of a given utility.

[1] U.S. Department of Energy, “National CyberInformed Engineering Strategy,” U.S. Department of Energy, June 2022. [Online]. Available: www.energy. gov/sites/default/files/2022-06/FINAL%20DOE%20 National%20CIE%20Strategy%20-%20June%20 2022_0.pdf. [Accessed: Dec. 24, 2023].

5. CONCLUSIONS This study concludes that while cyber-informed engineering is certainly a desirable future for the U.S. electric grid, current standards and practices, while insufficient, are perhaps too firmly ingrained within the industry to make way for a paradigm shift without proper and thorough integration. The potential of CIE to revolutionize U.S. grid infrastructure security is evident, but the interconnected nature of the nation’s electric grid necessitates majority or total endorsement of CIE to ensure the necessary level of protection in today’s cyber threat environment.

[2] CISA, “Cyber-Attack Against Ukrainian Critical Infrastructure,” CISA, July 20, 2021. [Online].

Available: Cyber-Attack Against Ukrainian Critical Infrastructure | CISA. [Accessed: Jan. 23, 2024].

[3] NERC, “CIP-005-6 – Cyber Security – Electronic Security Perimeter(s),” NERC, April 17, 2017. [Online].

Available: www.nerc.com/pa/Stand/Reliability%20 Standards/CIP-005-6.pdf. [Accessed: Jan. 8, 2024].

[4] NIST, “Security and Privacy Controls for Information Systems and Organizations,” U.S. Department of Commerce, September 2020. [Online.] Available: SP 800-53 Rev. 5, Security and Privacy Controls for Information Systems and Organizations | CSRC (nist. gov). [Accessed: Jan. 8, 2024].

6. ACKNOWLEDGEMENTS Special thanks to Dr. Daniel Cole and Dr. Brandon Grainger for their guidance throughout the research process, as well as Dr. Brett Say for his coordination of the SHURE-Grid program. Additional thanks to Virginia Wright of INL for her continued support and insight, Idaho National Laboratory for funding this incredible opportunity, and to the many individuals who contributed their time and expertise.

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Characterization of High-Temperature Magnetic Stability for an ExtremeTemperature Inductor Application Zisong Wanga , Tyler William Paplhama, Lauren Wewera, Paul Richard Ohodnickia Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA a

Zisong Wang is a senior material science and engineering student with an interest in nanocrystalline and ferrite magnetic materials. He is pursuing a further education by attending graduate school.

Zisong Wang

Lauren Wewer is a second-year material science PhD student in the Mechanical Engineering and Materials Science Department at the University of Pittsburgh. Her research interests include soft magnetic materials, alloy design, and materials characterization.

Lauren Wewer

Tyler Paplham

Paul Ohodnick Jr.

Tyler is a third-year PhD student in Materials Science and Engineering.

His research involves the use of electromagnetic fields and other advanced processing techniques to develop next-generation power magnetic components for electrification and space exploration applications.

Paul R. Ohodnicki Jr. is currently RK Mellon Faculty Fellow in Energy in the Mechanical Engineering and Materials Science department at the University of Pittsburgh with a secondary appointment in Electrical and Computer Engineering. In addition, he is the Engineering Science program director and founding director of the Advanced Magnetics for Power and Energy Development (AMPED) consortium.

Significance Statement

A toroid inductor for high-frequency use on Venus needs a constantly low permeability at around 500º for 24 hours. The nanocrystalline cobalt-rich alloy could serve as the core material to meet these demands.

Category: Experimental Research

Keywords: toroid inductor, magnetic material, amorphous and nanocrystalline alloy, anisotropy energy

Ingenium 2024

ABSTRACT The magnetic properties of alloys can be tremendously affected at an extreme temperature. To test the stability of an induced anisotropy with time, transverse magnetic field (TMF)-annealed and strain-annealed cobalt-rich alloys are reannealed at 450ºC and 475ºC, around the primary crystallization temperature of the alloy. The stability of the induced anisotropy ensures a constant, low permeability along the flux direction in the inductor, which is critical for consistent operation.

Both processing methods resulted in stable induced anisotropy energies for up to 24 hours.

1. INTRODUCTION This research aims to characterize the stability of a core material for a toroid inductor that works under highfrequency AC sources and high temperatures. To avoid irreversible harm by over-saturating the material, and considering the inductor going to work on the surface of Venus, it is expected to maintain a constant, low permeability at around 500ºC for 24 hours.

The requirement for low permeability in a toroid inductor can be simplified as a low permeability along the longitudinal (circumferential) direction of the core because magnetic flux flows along the longitudinal direction instead of the transverse (axial). Since the magnetic properties of crystalline materials are dependent on directions, magnetic field intensities that are required to saturate materials vary along different directions, and this effect is called magnetic anisotropy [1]. When that anisotropy results from specialized processing rather than inherent to the crystal structure, it is called induced anisotropy. This allows for low permeability along the longitudinal direction of the inductor that induces a high anisotropy energy between the easy (transverse) axis and the hard (longitudinal) axis.

Among current magnetic materials, cobalt-rich nanocrystalline alloys have shown strong responses to induced anisotropy energy [2]. Nanocrystalline soft magnetic alloys consist of crystallites smaller than 50 nm in diameter embedded in an amorphous matrix, which does not possess long-range atomic order. They are developed from a fully amorphous precursor, produced by melt spinning, which is then annealed to develop the primary nanocrystalline phase. In terms of phase transformation, primary crystallization is allowed because the amorphous matrix is enriched in glass formers during primary crystallization, which opposes the diffusion of Co in the amorphous matrix resulting in preventing the grain growth of magnetic nanocrystalline particles. In contrast, secondary crystallization is to be avoided because it generates a drop in saturation magnetization due to the formation of non-magnetic phases [3]. It is expected that the induced anisotropy energy of the cobalt-rich nanocrystalline alloy is relatively constant or slightly raised as reannealing time increases during 105

the first 24 hours at around the primary crystallization temperature.

2. METHODS A Co-rich alloy (Co76Fe2Cr2Nb 4Si2B14) produced by NASA Glenn Research Center was utilized in this project. Differential scanning calorimetry (DSC) was also performed which found the temperature of primary crystallization is about 460ºC and secondary crystallization is much higher than 500ºC. The magnetic properties of samples were measured by a Brockhaus single strip tester (SST) that can obtain the hysteresis loops of ribbon strips under alternating current excitation, from which the magnetic permeability (µr) and induced anisotropy energy (Ku) can be determined.

All investigated samples had the same compositions and shape but were produced in two ways: TMFannealed and strain-annealed. TMF-annealed samples were exposed to a transverse saturating magnetic field during the initial anneal, while strain-annealed ones faced a longitudinal tensile stress of 520 MPa during the anneal. Both lead to low permeability along the ribbon, defining the hard axis in that direction [4] [5].

The TMF-annealed and strain-annealed ribbons had respective widths of 27 mm and 27.5 mm, and roughly 20 µm thickness. Sections, approximately 180 mm long, were cut for measurements. To test the stability of induced anisotropy energy at different temperatures, the samples were reannealed in a furnace for various time intervals ranging from 30 minutes to 24 hours at temperatures of 450°C and 475°C. Permeability was determined via the anhysteretic curve that averaged polarization values of points with the closest magnetic field values on the upper and lower curves of a hysteresis loop to form a single curve without hysteresis losses. The hysteresis loop and the anhysteresis curve of the TMF sample reannealing at 450ºC for 30 minutes are shown in Figure 1.

3. RESULTS Since the permeability of Co-rich alloys is approximately constant before saturation, the anhysteresis curve is linear before saturation. The magnetic permeability can be calculated with equation 1, µr = k/µ0 + 1

(1)

where µ0 is the vacuum permeability and k is the slope of the anhysteresis curve before saturation. The results of relative permeabilities are shown in Figure 2.

Figure 2: Change in relative permeability with reannealing time.

The results of samples reannealing at 450ºC for 20hr and 24hr are not included due to equipment failure. Fortunately, 450ºC is below the primary crystallization temperature of the material so it is expected that the material’s magnetic properties after reannealing 20hr and 24hr are approximately the same as the previous states.

Since the SST could not provide a strong enough magnetic field to saturate the strain-annealed samples, the saturation polarization values were assumed to be the same as the TMF-annealed samples reannealing for the same time, which is reasonable because they have the same composition. The saturation polarization values were the maximum polarization values obtained from the corresponding hysteresis loops of TMF-annealed samples. The changes in saturation polarization with reannealing time are shown in Figure 3.

Figure 1: The hysteresis loop and the anhysteresis curve of the TMF-annealed sample reannealing at 450ºC for 30 minutes.

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4. DISCUSSION Strain-annealed samples exhibit significantly lower permeability and thus higher induced anisotropy energy compared to TMF-annealed ones. This conforms to prior research suggesting that strain annealing induces anisotropy at a much larger scale than field annealing [7]. Despite this, the exact mechanism behind strain annealing in Co-rich alloys remains inconclusive, necessitating further study to grasp its microstructural impact on induced anisotropy energy.

Figure 3: Change in saturation polarization with reannealing time at 450ºC and 475ºC.

According to the calculated permeability of each sample and the maximum polarization values from hysteresis loops, the corresponding induced anisotropy energy can be obtained with Equation 2, Ku = Js2 / 2µ0 µr

(2)

where J s2 is the saturation polarization of the material [6]. The change in the induced anisotropy energy with the reannealing time of TMF-annealed samples and strain-annealed samples are shown in Figure 4.

The induced anisotropy energy in both TMF-annealed and strain-annealed samples remains stable upon reannealing at 450ºC and 475ºC. According to the fluctuating trends of the induced anisotropy energy, opposing mechanisms are anticipated to influence the induced anisotropy energy, particularly during primary crystallization. Research suggests that pre-annealed alloys display increased induced anisotropy energy during reannealing without an applied magnetic field, termed “self-transverse field anneal”, resulting from remanent transverse magnetic moments formed in the pre-annealing process [8]. Conversely, there is an expected decrease in induced anisotropy energy during reannealing due to the thermal vibration of magnetic moments.

5. CONCLUSIONS The stabilities of TMF-induced and strain-induced anisotropies in a cobalt-rich nanocrystalline alloy at temperatures around its primary crystallization temperature were assessed. Alloys by both processing methods displayed a roughly constant permeability with times up to 24 hr. Fluctuations are suggested to be due to competing microstructural mechanisms, although more work must be conducted to confirm this.

However, the overall stability with time is promising for their use in extreme temperature inductor applications.

6. ACKNOWLEDGMENTS The research is funded by the Mascaro Center for Sustainable Innovation. Additionally, the first author would like to thank his supervisor Dr. Ohodnicki, and his graduate mentors Tyler Paplham and Lauren Wewer, for their generous guidance and help.

Figure 4: Changes in the induced anisotropy energy with the reannealing time of (a) TMF-annealed samples and (b) strainannealed samples.

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REFERENCES [1] R. E. Hummel, “Electrical properties of materials,” Understanding Materials Science: History· Properties· Applications, pp. 180–216, 1998.

[2] Y. Yoshizawa, S. Fujii, D. H. Ping, M. Ohnuma, and K. Hono, “Magnetic properties of nanocrystalline FeMCuNbSiB alloys (M: Co, Ni),” Scr Mater, vol. 48, no. 7, pp. 863–868, 2003.

[3] S. J. Kernion, V. Keylin, J. Huth, and M. E. McHenry, “Secondary crystallization in (Fe65Co35)79.5+xB13Nb4-xSi2Cu1.5 and (Fe65Co35)83B10Nb4Si2Cu1 nanocomposite alloys,” J Appl Phys, vol. 111, no. 7, 2012.

[4] M. Takahashi, S. Kadowaki, T. Wakiyama, T. Anayama, and M. Takahashi, “Magnetic anisotropy induced by magnetic annealing and cold rolling for Co and Co– Ni alloys. I. Experimental,” J Physical Soc Japan, vol.

47, no. 4, pp. 1110–1116, 1979.

[5] S. Chikazumi, Physics of ferromagnetism, no. 94.

Oxford University Press, 1997.

[6] G. Herzer, “Amorphous and nanocrystalline materials,” Encyclopedia of Materials: Science and Technology, pp. 149–156, 2001.

[7] A. Leary, V. Keylin, A. Devaraj, V. DeGeorge, P. Ohodnicki, and M. E. McHenry, “Stress induced anisotropy in Co-rich magnetic nanocomposites for inductive applications,” J Mater Res, vol. 31, no. 20, pp. 3089–3107, 2016.

[8] P. R. Ohodnicki Jr., “Crystallization and magnetic field processing of cobalt-rich cobalt, iron-based nanocrystalline and amorphous soft magnetic alloys,” Carnegie Mellon University, 2008.

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Magnetic properties of conventionally and flash annealed iron-nickel magnetic alloys Aron Wiener1, Lauren Wewer1, Tyler Paphlam1, Paul Ohodnicki1 Department of Mechanical Engineering and Materials Science, University of Pittsburgh, PA 1

Aron Wiener

Aron Wiener is a fourth-year undergraduate majoring in Materials Science Engineering. Through the MEMS FIRE Program, he was able to work with Dr. Paul Ohodnicki’s group in magnetic materials. After graduation, he will work with Hatch in their Downstream processes team.

Lauren Wewer is a second-year materials science PhD student in the Mechanical Engineering and Materials Science Department at the University of Pittsburgh. Her research interests include soft magnetic materials, alloy design, and materials characterization.

Lauren Wewer

Tyler Paplham

Paul R. Ohodnicki Jr.

Tyler is a third-year PhD student in Materials Science and Engineering.

His research involves the use of electromagnetic fields and other advanced processing techniques to develop next-generation power magnetic components for electrification and space exploration applications.

Paul R. Ohodnicki Jr. is currently RK Mellon Faculty Fellow in Energy in the Mechanical Engineering and Materials Science department at the University of Pittsburgh with a secondary appointment in Electrical and Computer Engineering. In addition, he is the Engineering Science program director and founding director of the Advanced Magnetics for Power and Energy Development (AMPED) consortium.

Significance Statement

In this research study, advantages and disadvantages of different annealing methods are investigated. It was seen that flash annealing displayed more tunable properties as well as lower losses compared to conventional annealing.

Category: Experimental Research Keywords: Soft magnets, Nanocrystalline, Amorphous

Ingenium 2024

ABSTRACT Soft magnets, particularly nanocrystalline amorphous alloy ribbons, are considered useful and efficient in the utilization of power converters, transformers, and electric motors. Post-processing steps are needed to develop nanocrystalites within the amorphous matrix and the current industry practice involves conventional annealing in a preheated furnace. In this study, however, flash annealing with very high heating and cooling rates was performed to understand its benefits. Various times and temperatures were tested to understand the tunability of properties with this method. The ribbon alloy used was (Fe70Ni30) 80Nb 4Si2B14, and both microstructure and magnetic properties were tested. It was observed that flash annealing had lower losses and a wider range of tunability compared to conventional annealing.

1. INTRODUCTION As the use of electric motors and power converters has increased over the last few decades, soft magnets have been developed for these applications due to their ability to function at medium frequencies with low losses[1]. Specifically, nanocrystalline amorphous alloys have been a topic of interest due to their low hysteric and eddy losses. Applications include transformers as well as electric vehicles such as cars and planes. Before annealing, the alloy structure is amorphous, indicating there is no defined crystal structure. After the ribbon is annealed, partial crystallization will occur, maintaining some of the ideal properties from the amorphous portion, but the Fe and Ni begin to precipitate. These elements are ferromagnetic, improving the magnetic properties. In this paper, (Fe70Ni30) 80Nb 4Si2B14 will be investigated as the Fe-Ni alloy group has high saturation inductions as well as tunable permeabilities [2]. After casting, different annealing techniques are performed to induce primary crystallization in the ribbons to obtain ideal magnetic properties, including high saturation, low coercivity, and high permeability [2].

In industry, conventional annealing (CA) is used to partially crystallize amorphous alloys. Conventional annealing is an isothermal process where ribbons are placed in a preheated furnace to induce partial crystallization, but this generally shows sub-par magnetic properties, including high coercivity, lower saturation, and higher eddy losses [3]. In addition, properties are harder to tune due to longer annealing time and increased brittleness [4]. Flash annealing (FA) is a new annealing technique with high heating rates (102K/s-103K/s), allowing for a wider range of tunable properties, increased ductility, and a faster processing time [4]. These high heating rates result in a grain size reduction and larger amount of nucleation sites, reducing losses and resulting in higher saturation magnetization [3]. The coercivity of the ribbons is

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directly related to the grain size of the ribbons, as seen by the Herzer relationship in equation 1:

Where Hc describes coercivity and D describes grain diameter. This relationship is valid when the domain wall thickness, δw, exceeds the grain size, so domain wall pinning no longer dominates coercivity [5]. In the case of this alloy, it is expected that the grains are small enough resulting in this relationship. There are two temperatures where crystallization occurs during annealing, primary (Tx1) and secondary (Tx2).

Heating will be above Tx1 and below Tx2 because if the second crystallization temperature is reached, then hard nonmagnetic intermetallic phases will form, deteriorating soft magnetic properties[6].

The thickness of the samples is expected to affect the magnetic properties as well as the microstructure. Due to the smaller area, it is possible that crystallization may occur in the 15 μm at a lower temperature than the 20 μm ribbons, as there is less mass to heat. The lower ribbon area may cause lower eddy losses in the ribbon, this is shown by the equation:

where Pe are the losses in W/kg, Ke is a predetermined coefficient, Bm is flux density, f is frequency, t is thickness, and V is volume. Using the equation presented, for 20 μm and 15 μm ribbons, the loss ratio should be 1.78, with higher losses being shown in the 20 μm samples. These lower eddy losses are ideal because less power is being wasted, and it creates a more efficient ribbon.

The goal of this experiment is to show the change in properties between flash annealing and conventional annealing, as well as quantitatively compare the different properties obtained based on ribbon thickness. The proposed hypothesis is due to the high heating rates, FA has a wider tunability of properties for optimization compared to CA and that the 15 μm ribbons will display lower losses but similar coercivities and permeabilities as the 20 μm ribbons.

2. METHODS Both 15 μm and 20 μm thick ribbons were cast with a planar flow caster by our partners at NASA Glenn Research Center. Based on differential scanning calorimetry (DSC), thermodynamic modeling, and previous experiments, an experimental testing matrix was produced, investigating annealing types, times, and temperatures. Conventional annealing was performed by placing a ribbon in a preheated furnace and annealing isothermally for a desired time. Flash annealing was performed using a custom setup with an industrial hot plate heating two copper blocks. Once at the desired temperature, the top block is lifted, and the ribbon is placed between the two, heating the ribbon at rates of 102 K/s, and cooled in air. Magnetic properties (coercivity, losses, and permeability) were calculated using a Brockhaus electrical Steel tester MPG 200 with 11 frequencies ranging from 500 Hz to 25000 Hz and 5 polarizations ranging from 0.1 T to 0.9 T. MATLAB code was developed to graph the B-H loops of each test as well as compare the coercivity, losses, and permeabilities for all different polarizations and frequency tests. To help understand core loss, the Steinmetz fit was utilized alongside the code. The Steinmetz fit is a way to fit the losses to the following equation:

B and f are flux density and frequency, respectively, while k, a, and b are all coefficients/exponents that are used to fit the surface. P is the power loss found by the Brockhaus tester. X-ray diffraction (XRD) was performed with a Bruker D8 Discover Diffractometer, with Cu-k radiation over a 2-theta range of 30 to 80 degrees. This allowed characterization of the phases present in the samples and the determination of grain size of the nanocrystals that formed during annealing.

To determine the grain size in the ribbons, Scherrer’s equation was used:

where D is grain size, K is shape factor, λ is the wavelength, β is the full width at half measure of the peak, and θ is angle at the peak location.

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3. RESULTS The ribbons were first characterized by XRD through peak indexing seen in figures 1a-b. The as-cast ribbon showed a hump at around 45º, while the annealed samples showed sharper peaks at this angle, indicating primary crystallization occurred. Using the Scherrer equation, it was also determined that the average grain size among the flash annealed samples was 1.69 nm.

Figure 1: a) shows XRD peaks for 15μm ribbons and b) 20μm ribbons. Three annealing methods are displayed.

The magnetic properties of the flash annealed samples were analyzed with heat maps based on the time, temperature, and chosen magnetic properties. The conditions used for these heat maps were at 2000Hz and 0.5T for permeability, and 2000Hz and 1000A/m for coercivity seen in figures 2a-d. Tunability of different properties based on different conditions is shown as well, seen by the wide range of values in the heat maps.

The desired magnetic properties of the ribbons include low coercivity, high permeability, and low losses. Fitting the desired parameters, ideal annealing conditions for both the 15 μm and 20 μm ribbons were determined to be FA at 480ºC and for 8 seconds.

Figures 2a-d: Heat maps for permeability and coercivity. Darker shaded regions indicate low values, while brighter regions indicate high values.

Three annealing methods are displayedIn figures 3a-b, the B-H loops for 2000Hz and 1000A/m are displayed in accordance with the heat maps in figures 2a-d.

Additionally, losses are plotted for all frequencies for as-cast, conventional, and flash annealed samples in figures 4a-b.

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Figure 3: a) shows the B-H loops for as-cast, FA, and CA, in the 20 μm ribbon, and 3b) shows the same for 15 μm ribbon. The applied conditions were 1000 A/m and at 2000 Hz.

Figure 4: a) losses in W/kg are shown for different frequencies at 0.5 T for the 15 μm and b) 20 μm.

As-cast, conventional, and flash annealed ribbons are displayed.

Figure 4c) shows 3D plots of losses for the 15μm FA 480ºC 8s sample, and d) shows the same sample but the 20μm thickness.

Using the developed MATLAB code, a 3D surface was plotted relating losses to frequency and polarization.

The surfaces for both the 15μm and 20μm ribbons flash annealed at 480ºC and for 8s are shown in figures 4c-d. Additionally, Steinmetz fits were found and are as follows:

where 5 refers to the 15 μm ribbon, and 6 refers to the 20 μm ribbon.

4. DISCUSSION The XRD results remain consistent with previous assumptions, the as-cast patterns have a wide hump at around 45º, indicating that there is no crystallization.

However, when annealed, primary crystallization occurs, and this hump turns into a sharp peak.

In the 20μm as-cast samples, it is seen that there are additional crystallization peaks. This was due to surface crystallization on the samples and was polished thoroughly to remove any crystallization before analysis on the annealed samples. Based on previously performed calculations involving the phase diagram of the Fe-Ni system, it is expected that the FCC

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phase should be more prevalent, accounting for about 88.3% of the composition [4]. Because the intensity of the peaks in the XRD plots are relatively low, it is conceivable that only the FCC peaks are present, and the BCC peaks are much smaller and less noticeable.

Additionally, because the average grain size of the flash annealed samples is 1.69 nm, the Herzer theory can be applied, and it can be assumed that the relationship established in section 1.1 holds true.

After careful consideration of each heat map generated, it was concluded that FA 480ºC for 8 seconds was the ideal heating condition for both thicknesses tested.

Although this condition doesn’t have the absolute optimal properties in each category, it is the only condition that fits all the criteria (high permeability, low coercivity). After finding the optimal conditions, B-H loops were generated for both thicknesses for the as-cast, conventional, and flash annealed samples.

The 15μm FA B-H loop displays a lower coercivity than the 20μm FA B-H loop. This is seen by the width of the loop. Wider loops indicate that there is a higher energy requirement to magnetize and demagnetize the sample. [9] In addition, it appears that in the 15μm samples, the loop area of the flash annealed samples is smaller than that of the other annealing conditions, indicating lower losses [5]. The losses for the same samples are also graphed in figures 4a-b. For both ribbon thicknesses, the flash annealed samples show lower losses than both the conventional and as-cast.

For the 15μm ribbons, the as-cast samples measure 2.56 times more losses compared to the flash annealed.

Additionally, CA experiences 3.02 times more losses compared to FA for 15μm. For the 20μm ribbons, the as-cast to FA loss multiplier is 1.74, and for CA to FA, it is 1.18. Looking at the flash annealed samples, it was observed that the loss ratio between the two thicknesses was on average 1.388, indicating that the 20μm samples lost about 39% more energy than the 15μm samples. It was estimated that the loss ratio between the two thicknesses should be 1.78, based on the equation presented. However, the ratio seen was 1.39. Although the 15μm samples had lower losses than the 20μm samples, they were not as low as expected.

This may be because other losses start to dominate at higher frequencies because the ratio only factored in eddy losses. Code is in development to separate the losses into eddy, hysteretic, and excess losses to understand what losses dominate for each thickness.

5. CONCLUSION Both conventional and flash annealing were performed on FeNi-based 15μm and 20μm ribbons to find an optimal processing method. An ideal flash annealing condition of 480ºC and 8s was determined, and its losses were plotted. It was hypothesized that the thinner ribbons should display lower losses by a factor of 1.78. It was concluded that the loss ratio between the two thicknesses was 1.39, lower than the expected

ratio due to unaccounted losses, such as hysteresis and excess losses. Future work involves the development of a code that separates the losses and produces a fit to the experimental data, as well as laser processing to determine the effects of even higher heating rates.

6. ACKNOWLEDGEMENTS Tyler Paplham, Lauren Wewer, NASA, MEMS FIRE Program

REFERENCES [1] N. Aronhime, E. Zoghlin, V. Keylin, X. Jin, P. Ohodnicki, and M. E. McHenry, “Magnetic properties and crystallization kinetics of (Fe100 − xNix)80Nb4Si2B14 metal amorphous nanocomposites,” Scr Mater, vol.

142, pp. 133–137, Jan. 2018 [2] A. Kolano-Burian et al., “The influence of ultra-rapid annealing on nanocrystallization and magnetic properties of Fe76-xNi10B14Cux alloys,” J Alloys Compd, p. 165943, Jun. 2022 [3] A. Talaat, J. Egbu, C. Phatak, K. Byerly, M. E. McHenry, and P. R. Ohodnicki, “Nanostructure refinement and phase formation of flash annealed FeNi-based soft magnetic alloys,” Mater Res Bull, vol. 152, p. 111839, Aug. 2022 [4] L. Wewer, K. Byerly, S. Kernion, and P. Ohodnicki, “FeNi-based Nanocomposite Soft Magnetic Alloy Magnetic and Mechanical Property Tailoring Through Flash Annealing.”

[5] M. E. McHenry, M. A. Willard, and D. E. Laughlin, “Amorphous and nanocrystalline materials for applications as soft magnets.”

[6] V. DeGeorge, E. Zoghlin, V. Keylin, and M. McHenry, “Time temperature transformation diagram for secondary crystal products of Co-based Co-Fe-B-SiNb-Mn soft magnetic nanocomposite,” J Appl Phys, vol. 117, no. 17, May 2015 [7] F. Fiorillo, Characterization and Measurement of Magnetic Materials, Elsevier Academic Press, 2004, p. 31 [8] M. H. Rashid, Power Electronics Handbook, 4th ed, Butterworth-Heinemann, 2017. P. 573 [9] Colonel W. T. McLyman, “Chapter 2: Magnetic Materials and Their Characteristics” in Transformer and Inductor Design Handbook, NY, USA, Marcel Dekker, 2004, pp 49-102

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Index Category: Computational Research Zhengkai Chen, Ying Fang, Boyang Li, Guofeng Wang Computational Evaluation of Spinel MnFe2O4 Adsorbent to Remove Heavy Metal from Wastewater........................................................................18 Tyler Johnston, Wayne Stallaert Cell fate analysis of ovarian cancer in response to chemotherapeutic treatment..........................................................................40 Hannah Levine, Ying Fang, Boyang Li, Guofeng Wang Materials for Solar Energy Technology: Cation Distribution and Behaviors of Spinel Oxide MnFe2O4 ...............................................................49 Raymond Murray, Jiwon Choi, Usamma Amjad, Helen Schwerdt Anticipatory licking and dopamine release in the monkey striatum.................................................................................................77 Caymus R. Ruffner, Vikas Khanna System-level modeling & analysis of Pennsylvania plastic waste: Implications for a circular economy...............................................................92 Ganesh Selvakumar, Matthew M. Barry Modeling Materials and Configurations of Radioisotope Thermoelectric Generator (RTG) Heat Sources...........................................97

Category: Device Design Ryan Caginalp, In Hee Lee Implementation of a capacitance-to-digital converter using an open-source tool flow........................................................................................14

Celeste E. Lintz, Katelyn E. Lipa, Meagan J. Makarczyk, Sophie E. Hines, Hang Lin Generation of Fat-Cartilage Microphysiological Model as a New Tool to Study Obesity-Associated Osteoarthritis......................................................58 Shaoyi Liu, Shuqi Liu, Gelsy Torres-Oviedo Cognitive Perseveration Is Not Associated with Training-Induced Improvements in Motor Perseveration In Older Adults..........................63 Jose Morales, Pierangeli Rodriguez De Vecchis, Zachary Harris, Markus Chmielus Density variations in large Binder Jet 3D Printed Inconel 625 Parts for Mechanical Testing Sampling...........................................................................68 Trin R. Murphy, David R. Maestas, Jr., Katarina Martinet, William R. Wagner, Sang-Ho Ye, Jonathan P. Vande Geest The Development of PLCL and Polyurethane Small Diameter Vascular Grafts of Varying Compliance..........................................................................73 Daniel S. Nolan, Shannon C. David, Tamar Kohn Effects of Commensal Respiratory Bacteria on The Persistence of Influenza A Virus in Droplets............................................................................82 Steven Panico, Pierangeli Rodriguez De Vecchis, Markus Chmielus Binder Jet Printing and Permeability Testing of Porous Metallic Shapes For Filtration Applications................................................................................87 Zisong Wang, Tyler William Paplham, Lauren Wewer, Paul Richard Ohodnicki Characterization of High-Temperature Magnetic Stability for an Extreme-Temperature Inductor Application............................................ 107 Aron Wiener, Lauren Wewer, Tyler Paphlam, Paul Ohodnicki Magnetic Properties of Conventionally and Flash Annealed Iron-Nickel Magnetic Alloys..................................................................................................111

Category: Experimental Research

Category: Methods

Charles W. Blackledge, Ioannis Zervantonakis

HER2+ breast cancer cells induce expression of Tumor-Associated Macrophage markers in Monocyte-Derived Macrophages in-vitro......... 9

Kasey Forsythe, Hendrik Santosa, Theodore Huppert First experiences integrating functional near-infrared spectroscopy brain imaging and virtual reality.....................................................................30

Adriana Cocco, Shivbaskar Rajesh, James F. Antaki, Harvey S. Borovetz Effects of estrogen on platelet activation relative to cardiovascular health .......................................................................................25

Elizabeth Ibata-Arens, April Chambers, Goeran Fiedler Comparison of prosthetic knee joint types in relation to slip risk...................................................................................................................35

Samuel M. Landon, Susan K. Fullerton- Shirey, Eric J. Beckman Triggerable dissolution of environmentally benign plastic in marine environments........................................................................................45

Category: Other

Zhehao Li, Meng Wang Determining the effects of amino acid composition on the activity of laccase-mimicking bionanozymes..................................................................55

Isabella Hsia, Kameren Jouhal, Shanker Pillai, Philippe Van de Putte, Evan Wang, Daniel Cole, Brandon Grainger Cyber-Informed Engineering in the Context of Prioritizing U.S. Grid Security.............................................................................................. 102

Category Definitions

u Computational Research—using computational techniques to address a scientific question  Device Design—focusing on the development of a product or device

 Experimental Research—using laboratory methods to achieve a novel overarching experimental aim

 Methods—developing new techniques and tools for research and design  Other

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