Engineering Commons^™

A Machine Learning Framework For Residential District Cooling: Forecasting Consumption, Explaining Drivers, And Evaluating Decarbonization Pathways, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

All Works

District Cooling Systems (DCS) in the Middle East, while energy-efficient, are significant contributors to carbon emissions. This study introduces a novel framework to decarbonize DCS operations by integrating predictive machine learning, explainable AI (XAI), and renewable energy planning, all grounded in extensive real-world data. Leveraging a unique dataset from 59 residential buildings in the UAE—including energy consumption, climate variables, and building features—we developed a high-fidelity cooling load forecasting model. Following a rigorous chronological validation methodology, the Random Forest model was identified as the most robust, achieving a strong performance (R² = 0.8256, RMSE = 11,668.31). Outdoor temperature was confirmed …

Pso-Style Social Influence In An Ant Colony Algorithm For Continuous-Domain Optimization, Ashraf M. Abdelbar, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

ACOR is a well-established Ant Colony Optimization (ACO) algorithm for continuous-domain optimization. In this paper, we propose an extension (which we call ACOR∗) in which several fundamental modifications are made to ACOR's solution construction process, including the incorporation of a social influence mechanism borrowed from Particle Swarm Optimization (PSO). Our modifications to the ACOR algorithm are intended to promote search diversity and combat premature convergence. We experimentally evaluate our proposal in the context of training feedforward neural networks for classification using 65 widely used datasets from the University of California Irvine (UCI) repository, as well as the optimization of several …

Constructing Orthonormal Bases With The Residuals Of Successive Approximations, An Introduction To Multiresolution Analysis, Elijah J. Guptill

Master's Theses

Wavelets and wavelet analysis are used in the study of signal processing, quantum field theory, functional analysis, multifractal analysis, and various other areas of mathematics. Multiresolution analysis provides a framework for building a wavelet basis of $\mathcal{L}^{2}(\mathbb{R})$ from a scaling function $\phi$, whose dyadic dilations and translations, $\{2^{j /2}\phi(2^{j}x-k):j,k\in \mathbb{Z}\}$, approximate $\mathcal{L}^{2}(\mathbb{R})$. One of the key properties of  $\phi$ is that it must satisfy $\phi(x)=\sum_{k\in \mathbb{Z}}{p_{k}2^{j /2}\phi(2^{j}x-k)}$ with respect to the norm on $\mathcal{L}^{2}(\mathbb{R})$. This equation is called a two-scale difference equation. Such equations enforce a regularity on the ordinary generating function $2^{-1 /2}\sum_{k\in \mathbb{Z}}{p_{k}z^{k}}$, known as the quadrature condition. …

Improving The Reliability And Performance Of A Supersonic Indraft Tube Wind Tunnel, Christian J. Kaml

Master's Theses

Access to supersonic testing is increasing in demand, and wind tunnels remain one of the safest and most cost-effective methods for gathering high-speed flow data. Despite being more economical than alternative options, supersonic wind tunnel facilities often require substantial investment to construct, operate, and maintain.

The novel indraft tube tunnel architecture was conceived as a high-speed flow testbed that incorporates features of both Ludwieg tubes and indraft wind tunnels to maintain costs low enough to be accessible even to small universities. This design was first developed and tested in 2018 at California Polytechnic State University, featuring a cost per test …

Fairlinked: Data Fairification Tools For Materials Data Science, Van D. Tran, Brandon Lee, Ritika Lamba, Henry Dirks, Quynh D. Tran, Balashanmuga Priyan Rajamohan, Ozan Dernek, Laura S. Bruckman, Yinghui Wu, Erika I. Barcelos, Roger H. French

Student Scholarship

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of …

Western Kentucky University Stormwater Utility Survey 2026, Warren Campbell

SEAS Faculty Publications

In the U.S., more than 2200 stormwater utilities have been enacted. This year, we have expanded the survey to include stormwater utilities in other countries. Many of these stormwater assessments are called taxes, but if the assessment is recurring, is not an ad valorem tax, and is dedicated to stormwater purposes, we call it a stormwater utility. With the help of ChatGPT we have been able to identify 25 stormwater fees in Australia. We were also able to identify 3 in New Zealand, and some in Europe as well.

Shared Language For Responsible Ai Integration, Asa B. Stone, Mark C. Stone, Alisha Bevins, Jean Claude Niyomugabo, Irene Magara, Jacob Abaare, Derek M. Heeren, Mubarak Abu Zouriq

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

As AI rapidly reshapes how we work and learn, employers increasingly seek graduates who can think before they prompt, exercising judgment under pressure rather than merely producing output. Yet students are praised for AI use in one course and penalized for it in the next, and faculty are left to lead responsibly on shifting ground, with no shared language to guide them.

This paper introduces the PRAIRIE Framework for AI Integration, a shift from reactive gatekeeping toward proactive stewardship. It emerged from a qualitative sentiment analysis of three communities (students, faculty, and industry partners) whose concerns converged on one need: …

Validation Of Electromagnetic Shower Development In A Reduced Alice Focal-E Detector Model Using Geant4, Isaac Lara Romo

Physics

This project focuses on the preliminary simulation of a reduced FoCal-E detector model for the ALICE experiment at CERN. Beamline tests were simulated using an electron gun at various incident energies, and the results were compared with those of the FoCal group. The longitudinal charge deposition per sensor layer was compared between the two setups to verify that the reduced model exhibited expected calorimeter behavior. Additionally, the transverse signal across the high-granularity sensor layers was examined to determine whether the reduced model reproduced the expected lateral development of electromagnetic showers. The results obtained from these measurements show that the reduced …

Quantum And Conventional Informatics Studies Of Synthesis Energetics And Defect Formation In Nitride Crystal Epitaxy, Andrew Steven Messecar

Dissertations

Machine learning is a valuable approach for the processing and analysis of complex information. By estimating relationships from recorded data, machine learning methodologies can be effective strategies for pattern recognition, enabling investigations and technological applications based thereon. The potential for improved understanding of high-dimensional data has drawn interest towards machine learning from across the sciences, including the research and development of new and improved material systems. In the context of experimental materials research, much of the reported efforts to incorporate machine learning into conventional practice have been primarily focused on either the enhanced analysis of characterization experiment data or the …

Computational Insights Into Nucleosome Dynamics In Epigenetics Using Molecular Dynamics Simulations, Rutika Patel

Dissertations, Theses, and Capstone Projects

Nucleosome core particles (NCP) are the building blocks that form a highly organized and compact chromatin structure. Nucleosomes package DNA in the nucleus of eukaryotic cells. The NCP consists of about 147 base pairs of DNA wrapped around the histone octamer, with 1.65 superhelical turns in a left-handed manner. The histone octamer is composed of two copies of H3, H4, H2A, and H2B. Together with histone H1 and linker DNA, they further assemble into a higher-order chromatin structure. The nucleosome complex is stabilized by electrostatic interactions between positively charged histone residues and the negatively charged DNA backbone. To effectively access …

Can Generative Ai Make Farming Decisions? Current Status And Future Pathways: A Case Study In Row Crop Production With Chatgpt, Nipuna Chamara, Yufeng Ge, Joe Luck, Yu Pan, Saleh Taghvaeian, Cory Walters, Christopher Proctor, Daran Rudnick, Daren Redfearn

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in …

Rc-Nf: Robot-Conditioned Normalizing Flow For Real-Time Anomaly Detection In Robotic Manipulation, Shijie Zhou, Bin Zhu, Jiarui Yang, Xiangyu Zhao, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the …

Thermally Induced Color Changes In Iron Oxide Pigments Used In Architectural Coatings, Han Diep

Master's Theses

The 2025 Palisades Fire was one of the most destructive wildfires in the history of Los Angeles, destroying thousands of structures and severely impacting the communities after. This widespread destruction demonstrated an urgent need for more effective wildfire prevention strategies. Architectural coatings do not only provide aesthetics and protection against environmental conditions but can also function as a thermal indicator when a fire breaks out. This study examines the thermally induced color transformation of iron oxide pigments, commonly used in earthtone paint, to provide information relevant to CAL FIRE for improved analysis of wildland-urban interface (WUI) fire behavior and mitigation …

Characterization Of The Thermally Driven Red Hue Effect In Coated And Uncoated Concrete Systems: Implications For Post-Fire Assessment In Wildland Urban Interface Communities, Juan C. Palominos Jr

Master's Theses

The increasing frequency and intensity of Wildland-Urban Interface (WUI) wildfires, such as the destructive 2025 Pacific Palisades Fire that destroyed over 6,000 structures, have underscored the urgent need for rapid post-fire safety assessment methodologies. This study sought to quantitatively characterize the Thermally Driven Red Hue Effect (TRHE), a unique, irreversible color shift observed in architectural coatings and concrete substrates, to determine its reliability as a permanent visual record of thermal exposure. Concrete substrates were formulated with three distinct coarse aggregates: gold granite (14.7% Fe), red cinder (22.4% Fe), and green rock (33.6% Fe) and coated with a 20 PVC waterborne …

A2s Uplink Latency Minimization For Wildfire Monitoring Systems Using Mbse And Stochastic Modeling, Luis Giovanni Wang

Master's Theses

Wildfire response depends on how quickly a detection reaches the people who act on it, and the slowest remaining step is often the link that carries an alert from a remote sensing platform to a satellite. This thesis models the latency of that link, the Air-to-Space uplink, for a wildfire-monitoring UAV that carries a Starlink terminal and sends an ALERT packet to a serving Low Earth Orbit satellite. The uplink is difficult to predict because both the UAV and the satellite move, and because the wildfire environment degrades the channel at the moment the data matters most.

The thesis uses …

To Wait Or To Transfer? A Three-Level Optimization Framework For Intermodal Transfer Coordination In First Train Timetabling And Bus Bridging Services Management, Hao Li, Liujiang Kang, Norman Weik, Huijun Sun, Qingying Lai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

This study addresses the integrated optimization of the first train timetabling and bus bridging service design (FTT-BBSD) for morning transfer challenges, two critical but interdependent passenger services in the public transit system. In contrast to most existing studies and conventional approaches, this study explicitly models the influence of passenger path choices and transfer mode selections on FTT-BBSD. Through a novel dual-level network representation that integrates subway and bus systems, we formulate the FTT-BBSD problem as a mixed-integer nonlinear programming model. The model simultaneously determines subway and bus timetables and bridging line deployment to minimize total travel time for all first …

Hydpn: A Hybrid Deep Reinforcement Learning, Programming, And Neighborhood Operations Framework For Integrated Scheduling On Parallel Batch Processing Machines, Yuqi Wang, He Luo, Guoqiang Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments …

Extensive And Intensive Margin Labor Supply On Ride-Sourcing Platforms, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Polarimetric Terahertz Imaging For The Measurement Of Birefringence In Plastic, Rachel Cohen

Theses

Birefringence offers a promising way to observe stress concentration in materials such as glass and plastic, and thereby to identify weaknesses. Polarimetric imaging can be used to measure the birefringence of material, so long as the material is transparent to the light being used for the imaging. In this research, 2D Terahertz imaging was investigated as a means of measuring the birefringence of plastics that are opaque to visible light but transparent to THz radiation, for the eventual purpose of analyzing the residual stress present. In order to do so, two separate terahertz cameras were characterized for potential use in …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Electrospun Nanofiber Scaffolds For In Vitro 3d Tissue Engineering, Victoria E. Santillan, Samerender Nagam Hanumantharao, Stephanie Bule, Ronish M. Shrestha, Carter Rodzik, Alan Mendoza Estrada, Stephen L. Farias, Marina Tanasova, Smitha Rao

Michigan Tech Publications

Tissue engineering is widely used in research for investigating cellular proliferation, behavior, and responses to various stimuli. However, the predictive value of preclinical studies using cell culture plates is limited by the inability to recapitulate the complexity of the physiological microenvironment. Synthetic three-dimensional (3D) scaffolds can be engineered to mimic the complex morphology of the extracellular matrix of native tissues and can serve as physiologically relevant platforms for preclinical studies. In this study, 3D electrospun scaffolds were characterized to aid in breast cancer research. Unlike previous studies that focused primarily on scaffold fabrication or cell viability, this work systematically evaluates …

Developing A Framework For Mooc Dropout: The Role Of Utilitarian And Hedonic Values In Student Retention, Bipllab Roy, Mave D'Souza, Madhura Laghane

Northeast Journal of Complex Systems (NEJCS)

Massive Open Online Courses (MOOCs) have expanded access to higher education but continue to face persistently high dropout rates, raising concerns about their long‑term effectiveness and sustainability. This study develops and empirically tests a structural framework that links utilitarian values (perceived usefulness, certificate value, time flexibility), hedonic values (enjoyment, variety and novelty, personal interest alignment), and individual characteristics (goal orientation, self‑efficacy, motivation type) to MOOC student retention. Data were collected through a structured questionnaire administered to 200 MOOC learners from Christ University, Lavasa Campus, and analyzed using Structural Equation Modelling (SEM) in AMOS. The results show that goal orientation, self‑efficacy …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Sophia Wismar, Henry Staats, Allison Metzler, Chloe Puckett, Rachel Levine, Christa Kilpatrick, Scott Wolf, Joe Walsh, Grace Doolittle, John Engebreston, Zoe Lopez, Christopher Aaby, Audrey Duff, Timothy Sisk, Katelyn Lamberton, Angela Narayan, Gilkah Argueta, Habiba Samir, Girena Tesfazghi, Genet Kenore, Sinit Tesfamariam, Effley Brooks, Abi Newell, Megan Doherty, Natalie Baer, Lexi Blood, Talya Riciputi, Jessica Jimenez, Devin Hernandez, Lynn Clark, Taj Kumar, Sunil Kumar, Allen Rutman, Mira Pronobis, Tess Carson, Anna Sher, Frankie Stroud, Tamra Pearson D'Estree, Alyssa Wilson, Emily Melnick, Jenalee Doom, Yihang Gao, Gwendolyn Geiger, Noah Gettle, Scott Nichols, Clare Ayoub, Cara Dienno, Sunny Walker, Zoe Hansen, Maya Wheeler, Addison Rice, Patrick Martin, Sanjana Acharya, Daniel Mcintosh, Amanda Mckellips, Calli Cain, Justin Blake, Peter Sokol-Hessner, Natalie Miller, Max Weisbuch, Sophia Dellota, John Macikas, Charlotte Snow, Mark Siemens, Zoe Lynch, Alex Huffman, Prachi Shah, Jason Roney, Halcyon Levi, Nicole Herzog, Andrea Koly, Daniel Linseman, Annie London, Xi Yang, Avery Zwisler, Jane Smith, Chaz Contag, Michael Kerwin, Lucy Rand, Grace Schroeder, Michelle Rozenman, Nissa Tapper, Guiming Zhang, Mateo Mazariego-Halpern, Keith Meyer, Julie Do, Dakota Park-Ozee, Travis Herink, Kara Neu, Jonathan Plomin, Eve-Odine Duchaufour, Debbie Gale Mitchell, Tennyson Anderson-Stricklin, Lily Treitz, Samantha Rosenberger, Sierra Griffith, Finley Joseph, Daniel Sampson, Emmy Davis, Skyler Kasnoff, Evon Lopez, Vivian Nguyen, Cassy Young, Franklin Sellner, Martin Tobon, Ila Graham, Zach Billings, Holden Hedit, Decatur Boland, Paul Kosempel, Cory Chandler, Jay Mahoney, Sam Dragan, Susan Dagget, Yarrow Ator, Heidi Vuletich, Owen Weber, Andrew Kloeppel, Petersen Gray, Mandi Schaeffer-Fry, Razleen Bassra, Bryanna Rodriguez, Christina Blue, Taubie Sanders, Rachel Epstein, Luke Milburn, Camryn Evans, Ezra Martinez, Mary Westwood, Gabri Notov, Robin Tinghitella, Lilou Cabrol, Eli Barbour, Juliet Mendik, Selma Myers, Zac Wise, Noah Fahlin, Michelle Knowles, Abigail Hopper, Michael Greenberger, Romi Laclair, Sarah Watamura, Sabrina Efroymson, Casey Barker, Sydney Seltzer, Bryn Yehle, Jennifer Hoffman, Sara Garcia, Ryuka Nagamine, Trevor Briggs, Remy Le Boeuf, Elena Krone, Eileen Farrell, Regan O'Rourke, Elena Roel, Greg Mortimer, Ali Ayoub, Stefani Langehennig, Caitlin Turk, Logan Scmid, Stefan Chavez-Norgaard, Karen Kim, Tatiana Peccedi, Courtney Cassidy, John Sebesta, Rhianna Lewis, Janice Bening-Lacek, Vivian Lawless, Mckenna Hanson, Jeffrey Amidon, Riya Joshi, Ram Ambre, Brady Worrell, Perrin Schneider, Ali Azadani, Brooke Agulnek, Lyndsie Salvagio, Elise Siemanowki, Yan Qin, Andre Allen, Melodie Nguyen, Megan Livengood, Abby Reams, Saffron Hartreeve, Bri Wylie, Sarah Brookman, Mariah Loiacono, Green Russo, Abhia Lodhi, Gabrielle Welsh, Nika Spehar, Shahked Levin, Evrim Baykal, Kimberly Chiew, Jocelyn Torres, Kailey Hicks, Mykaela Tanino-Springsteen, Audrey Bellows, Akam Chahal, Madeline Tepper, Shannon Murphy, Alexa Fonseca, Deborah Han, Cassandra Perez, Oluwatoyin Alaba, Julia Roncoroni, Vy Nguyen, Nana Burn, Sarah Sasse, Rubin Tuder, Anthony Gerber, Nancy Lorenzon, Christine Vohwinkel, Camryn Gunter, Tristan Weber, Sam Rommel, Brian Michel, Muskan Fatima, Alannah Oleson, Kira Frey, Edward Garrido, Beckett Morris, Kerstin Haring, Drew Middleton, Abigail Walpert, Liam Dee, Gabby Ishaw, Cole Carnes, Maddie Weiser, Claire Fox, Valeriia Vlasenko, Kateri Mcrae, Riley Smith, Abigail Templin, Kushani Rajapaksha

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

Modulating Electronic Structure With Linearly Fused Pyrazine Units For High-Voltage And Stable Zinc-Organic Batteries Cathode, Min-Jian Zhao, Li-Bin Zhang, Jin-Tao Wang, Kun Ding, Hai-Mei Liu, Yong-Gang Wang

Journal of Electrochemistry

High-voltage n-type organic cathode materials are critical for constructing zinc-organic batteries (ZOBs) with high energy density and long cycle life. However, the intrinsically unfavorable electronic structures and relatively high LUMO energy levels of most n-type materials often lead to sluggish kinetics, high solubility, and suboptimal discharge voltages (< 0.8 V). Here, we design a small molecule, quinoxalino[2’,3’:5,6]pyrazino[2,3-f][1,10]phenanthroline (DPQP), as a ZOB cathode by introducing locally electron-deficient motifs into the conjugated backbone of aromatic compounds. The linearly fused pyrazine units extending the pyrazine–benzene framework effectively optimize the electronic structure, thereby significantly enhancing the discharge voltage. Meanwhile, the expanded π-conjugated plane suppresses dissolution and accelerates charge-transfer kinetics. Benefiting from these features, the DPQP electrode exhibits an exceptional increase in average operating voltage from 0.61 V to 1.07 V (vs. Zn²⁺/Zn) at 0.1 A·g^–1, with an overpotential of only 140 mV. Notably, no discernible voltage decay occurs as the current density increases, indicating rapid and highly reversible redox kinetics. Furthermore, the DPQP cathode delivers outstanding cycling stability, maintaining over 2000 h of continuous operation at 0.1 A·g^{–1 …}

(Co,Ni,Mn,Cu,Zn)O High-Entropy Oxide Nanotubes As Efficient Bifunctional Electrocatalyst For Oxygen Evolution And Hydrazine Oxidation Reactions, Pan-Yan Chen, Wan-Wan Wu, Heng Bian, Wei-Wei Li, Xin-Sheng Zhao, Lu Wei

Journal of Electrochemistry

High-entropy oxides (HEOs) present significant scientific challenges in both design and synthesis due to their multielement and high-entropy nature, which involves complex combinations of multiple metal cations and oxygen anions, typically arranged in equimolar ratios to achieve structural stability. Herein, one-dimensional (Co,Ni,Mn,Cu,Zn)O high-entropy oxide nanotubes (HEO-NTs) are fabricated by means of a gradient electrospinning strategy with a tailored polyvinyl alcohol (PVA) molecular weight distribution and controlled pyrolysis. Benefiting from the HEO features and the synergistic effect of multicomponent sites, the as-synthesized (Co,Ni,Mn,Cu,Zn)O HEO-NTs exhibit exceptional bifunctional electrocatalytic activity for the oxygen evolution and hydrazine oxidation reactions (OER/HzOR). This study offers …