Eight students hailing from the Gallogly College of Engineering have been chosen as recipients of the summer 2023 Engineering Dissertation Award. The $5,000 award, designed to foster excellence among doctoral students, supports scholars in the final stages of their Ph.D. studies. The awards committee, led by associate dean for research Zahed Siddique, Ph.D., underscores the award’s significance for scholars near their graduation.
Initiated in 2018, the Engineering Dissertation Award is funded by the Thomas Ira Brown Jr. Endowed Scholarship. Remembering Brown’s pioneering work in electronic control of industrial gas turbines, this award perpetuates his legacy. Brown earned a bachelor’s degree in electrical engineering in 1950 from OU.
The summer 2023 awardees are:
Chris Billings, School of Aerospace and Mechanical Engineering, Recommended by Yingtao Liu, Ph.D.
Title: “Additive Manufacturing And Synthesis Of Advanced Antibacterial And Sensing Photocurable Polymer Nanocomposites”
Abstract: The dissertation covers the synthesis, manufacturing techniques, and characterization of several novel nanocomposites produced with direct light processing-based additive manufacturing systems. Custom thermodynamic and UV control systems are implemented for photocurable resin systems to synthesize and manufacture up to 14 novel nanocomposites. These novel nanocomposites are synthesized to improve tensile strength, wear resistance, water contact angle, antibacterial resistance, and electrical conductivity. This research discusses the advanced properties offered by each nanocomposite utilizing titanium dioxide and zinc oxide in weight concentrations as high as 7.5%. Carbon nanotubes (CNT) were also utilized in weight concentrations of up to 1%. Secondary functions such as strain sensing are evaluated for human motion detection utilizing custom printed sensors, and ultimate tensile strength increases of over 200% are observed. Wear resistance was documented to improve by over 15% in titanium dioxide and zinc oxide samples at 7.5% weight concentrations. Optimization studies are performed to determine the cure rate and in situ curing characteristics' effect on final part strength for CNT nanocomposites. This work demonstrates the ability to manufacture nanocomposite-reinforced parts that exhibit significantly improved physical, thermal, electrical, and antibacterial properties at high fidelity on low-cost hardware ideal for the medical, automotive, and aerospace sectors.
Alex Frickenstein, Stephenson School of Biomedical Engineering, Recommended by Michael Detamore, Ph.D., and Stefan Wilhelm, Ph.D.
Title: “Improving Quantification of Nanoscale Interactions By Engineering Monodisperse Gold Nanoparticles”
Abstract: Mechanisms of nanoparticle-cell interactions are dependent upon the size of the administered nanoparticles. At pre-clinical and clinical levels, precise control over the size and monodispersity of nanoparticles is required to produce consistent, effective, well-understood results. Gold nanoparticles (AuNPs) are a commonly used model nanoparticle for understanding nano-bio interactions based on AuNPs relative ease of synthesis and characterization. While ensemble characterization methods of AuNPs made using citrate-based synthesis approaches indicate relative size monodispersity, single nanoparticle analytical techniques reveal a wide size distribution. This wide size distribution decreases confidence in exact size-interaction correlations within nano-bio interactions and confounds sensitivity of single nanoparticle analysis. There is a need for AuNPs possessing a lower size distribution for improving single cell and single nanoparticle analyses of nano-bio interactions. In the current dissertation, single particle inductively coupled mass spectrometry (SP-ICP-MS) is used to characterize AuNPs synthesized using two different synthesis methods – citrate-based and CTAC-based – to identify differences in size monodispersity. Our analysis confirmed that CTAC-based AuNPs possess a tighter size distribution compared to citrate-based AuNPs. SP-ICP-MS was used to assess size prediction and synthesis scale-up models for each of the two synthesis methods. Further, AuNP growth kinetics of CTAC-based synthesis were characterized. AuNPs synthesized using CTAC-based methods are innately cytotoxic, making them unfit for biomedical use immediately after synthesis. To overcome this challenge, different methods of surface modification were developed that improved biocompatibility and biofunctionalization of CTAC-based AuNPs. SP-ICP-MS measurements confirmed that surface modification strategies did not result in a change in monodispersity, maintaining a tight size distribution for CTAC-based AuNPs. Biocompatible, biofunctional CTAC-based AuNPs were compared to citrate-based AuNPs in cell viability, cell uptake, and surface ligand interaction experiments. The overall findings of these results provide tools and methods by which highly monodisperse AuNPs may be synthesized, modified, and applied to better understand nano-bio interactions. Further, these results illuminate the possibilities and advantages of applying biocompatible monodisperse AuNPs in nanomedicine.
Subhindra Gopal Krishna, School of Computer Science, Recommended by Sridhar Radhakrishnan, Ph.D.
Title: “Compression Techniques for Extreme-Scale Graphs and Matrices: Sequential and Parallel Algorithms”
Abstract: This dissertation delves into efficient solutions for managing time-evolving graphs in the context of burgeoning social networks. Time-evolving graphs experience changing edge states, necessitating innovative storage and processing strategies. The work introduces encoding techniques for Compressed Sparse Row (CSR) format to handle large graphs and proposes a combination of structures (CSR + CBT) to optimize space and time trade-offs. Additionally, it explores methods to store multi-dimensional data and extends matrix multiplication algorithms for compressed structures. The research further presents parallel techniques for graph construction and querying, along with parallel time-evolving differential compression of CSR using a prefix sum approach. These contributions collectively address the complexities of evolving graph storage and processing.
Shahrukh Kasi, School of Electrical and Computer Engineering, Recommended by Ali Imran, Ph.D.
Title: “Designing Data-Aided Demand-Driven User-Centric Architecture For 6G And Beyond Networks”
Abstract: Despite advancements in technologies like massive MIMO and intelligent reflective surfaces, network densification remains crucial for capacity gains in future networks like 6G. However, densification increases interference and power usage. Traditional cellular architectures struggle to balance these factors without compromising service quality or capacity. The user-centric radio access network (UC-RAN) approach offers more flexibility but is challenging to design and operate optimally. This dissertation introduces four novel approaches for UC-RAN design and operation: incorporating Coordinated Multipoint (CoMP) technology to reduce interference and power consumption; Deep Reinforcement Learning (DRL) to dynamically adjust UC-RAN service zone size for different application demands; a risk-aware DRL framework for safe UC-RAN optimization; and a hybrid aerial-terrestrial UC-RAN model for universal coverage. Analytical and simulation results suggest that these approaches can facilitate a paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks.
Thi T. Le, School of Sustainable Chemical, Biological and Materials Engineering, Recommended by Daniel Resasco, Ph.D., and Bin Wang, Ph.D.
Title: “Effects Of Non-Equilibrium Charge Carriers On Photocatalytic Reactions Over Hybrid Plasmonic Catalyst”
Abstract: Plasmonic catalyst has been investigated to increase the utilization of the visible wavelength and tailor the reaction selectivity toward a more sustainable future. In this dissertation, it is attempted to give an understanding of how non-equilibrium electrons can facilitate reactions on the hybrid plasmonic catalyst. Using CO2 conversion as the probe, we found that the non-equilibrium does not change much the intrinsic activation energy of CO2 dissociation on Cu2O pristine surface at the excited state compared to the ground state. In contrast, if the oxygen vacancies are introduced, the intrinsic barrier could be reduced at the ground state, and this remaining barrier could be further eliminated at the excited because of the non-equilibrium electrons. We also found that the non- equilibrium charge carriers can alter the interaction between ammonia and the Lewis acid site of Beta zeolite. The effect is more significant when there are mid-gap states because Sn or Ti is doped into Si-BEA framework. Apart from the effect on the heat adsorption, it is found that if the doped concentration of Ti into zeolite increases, the intrinsic activation energy of the propylene epoxidation also can be reduced due to the higher population of the non-equilibrium electrons to induce reactions. These studies provide the opportunity to use highly energetic charge carriers, generated in plasmonic metal, to tune the zeolite activity for acid-catalyzed reactions. Finally, we explored the effect of solvent on the excitation energy and found that the H- bond between solvents and reactants can interfere with the interfacial charge transfer process. Notably, the H-bond between solvent and ammonia can prohibit the excitation energy to trigger an electron from the Fermi level to LUMO of ammonia. At the same time, it can reduce the excitation energy to excite an electron from molecular HOMO to the Fermi level. The findings of this dissertation could be a valuable guideline for hybrid plasmonic catalyst design for sustainable energy and chemical.
Aditya Narasimhan, School of Computer Science, Recommended by Sridhar Radhakrishnan, Ph.D., and Randa Shehab, Ph.D.
Title: “Combinatorial Algorithms In The Approximate Computing Paradigm”
Next step: Narasimhan’s research focuses on combinatorial algorithms in the approximate computing paradigm with the aim of gaining advantage in resource utilization, with particular emphasis on designing optimal algorithms while attempting to solve extremely large resource hungry problems. He has contributed to developing pioneering algorithms in tackling such situations in computing, according to his recommendation letters. Narasimhan recently defended his dissertation and moved to Boston working as a researcher and software engineer at Cadence Design Systems.
Oanh Pham, School of Sustainable Chemical, Biological and Materials Engineering, Recommended by Dimitrios Papvassiliou, Ph.D., and Bin Wang, Ph.D.
Title: “Flow Structure Effects On Turbulent Transport Using Computations”
Abstract: Turbulent flow is known as the most common flow in industrial applications and atmospheric phenomena. The random motion properties in the flow contribute to fluid mixing and interaction with solid surfaces such as friction, heating, and pollutant dispersion. The main goal of my research is to investigate the fundamental effects of the very large-scale structures of turbulence on convective transport and the effects of the interplay between molecular diffusion and flow structure on turbulent transport. Applying the Lagrangian computations in conjunction with direct numerical simulations of turbulent flow and the high-performance computing, we could explore the effects of the very large-scale turbulent fluid motions on scalar transport and determine the mechanism by which they contribute to transport. From that we can develop a predictive model for turbulent scalar transfer and control this. One of our applications is the effect of turbulent flow in the cardiovascular devices such as blood pumps, ventricular assist devices and even heart valves. The second application is to investigate the role of helicity in turbulent transport and the interplay between helicity with scalar dissipation and coherent structures.
Yu Yan, School of Sustainable Chemical, Biological and Materials Engineering, Recommended by Daniel Resasco, Ph.D., and Bin Wang, Ph.D.
Topic: “Computational Study And Manipulation Of Local Chemical Environment In Heterogeneous Catalysis And Separation”
Abstract: Reactive separation, a revolutionary approach combining catalytic reactions and selective separation, holds significant promise in transforming chemical engineering, particularly for polluted water treatment. Our research focuses on catalyst design and membranes to enhance selectivity, activity, and efficiency in pollution removal. By adjusting coordination environments in catalysts, we unveil key insights that can revolutionize process design for catalytic membranes. Through comprehensive simulations, we reveal the positive impact of water as a solvent on catalytic pollution removal. Understanding these chemical environments in promoting reactions paves the way for strategic advancements. This study addresses the critical issue of polluted water treatment, presenting groundbreaking findings that unlock unprecedented possibilities in chemical engineering for a sustainable future.
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By Lorene A. Roberson, Gallogly College of Engineering
