Digital Commons Network^™

Epitaxial Growth Of Metal-Organic Framwork Thin Films By Electro-Conversion And Electrodeposition, Xiaoting Zhang

Doctoral Dissertations

Electrochemical epitaxy is a facile and inexpensive soft-solution process to fabricate highly ordered thin films. This research focuses on the epitaxial growth of metal-organic framework (MOF) thin films by electrochemical methods which include electro-conversion and electrodeposition.

First, an electrochemical conversion pathway was invented to achieve epitaxial MOF thin films. Epitaxial single-domain Cu-BTC(111) thin films were manufactured by electrochemical oxidation of Cu₂O(111) films electrodeposited on single-crystal Au(111). The single-crystal-like Cu-BTC(111) thin films with out-of-plane and in-plane order provide well-aligned 3.5 Å triangular windows along the [111] direction. Cu-BTC(111) foils were fabricated from the Cu-BTC/Cu₂O system by electrochemical …

Design And Synthesis Of Organocatalyts For Efficient Decontamination Of Organophosphate-Based Nerve Agents And Pesticides, Emmanuel Kingsley Darkwah

Doctoral Dissertations

Exposure to organophosphate-based nerve agents and pesticides poses significant health and security threats to civilians, soldiers, and first responders. Despite extensive efforts to develop chemical detoxification agents for use in topical applications on exposed skin surfaces and for intravenous injections, there remains an unmet need for effective, non-hazardous decontaminating agents. The current state-of-the-art decontaminating agent, Dekon-139 (2,3-butanedione oxime, potassium salt), exhibits adverse effects when applied to the skin.

In this study, we designed and synthesized pharmaceutically relevant aminoguanidine-derived aldimines that are relatively non-toxic and substantially more effective at decontaminating nerve agents and pesticides compared to existing agents, and they act …

Design Of Real-Time And Energy-Efficient Driver Assist Systems Using Electroencephalogram And Neuromorphic Computing, Nathan Alan Lutes

Doctoral Dissertations

Despite the technological breakthroughs in advanced driver assist systems, distracted driving persists as a major challenge to roadway safety. This investigation advances the body of knowledge towards a solution by developing an individualized driver-state detection method using electroencephalogram (EEG) and neuromorphic computing to provide a less invasive and more energy efficient ADAS solution. It furthermore explores the changes in brain functional connectivity under distracted conditions to better understand brain state information that could be used for neuro-feedback intervention systems. The first contribution introduces the concept of using Convolutional Spiking Neural Networks (CSNNs) for recognition of patterns with movement-intention predictive power …

Higher-Order Statistics And Normalized Decay Analysis For Detector Deadtime Characterization, Abdallah Wazzan

Doctoral Dissertations

Detector deadtime limits radiation measurement accuracy at high count rates, yet current methods rely on idealized paralyzable or non-paralyzable models. Real detectors exhibit hybrid behavior requiring advanced characterization approaches. This study explores deadtime characterization using two Monte Carlo simulation approaches: higher-order statistical analysis of inter-arrival times and simplified deadtime correction with hybrid models.

Using MATLAB (PULSE-WIZ), we analyzed full decay curves spanning ~4.5 half-lives of Cobalt-60 and Vanadium-52, examining coefficient of variation (CV), skewness, and kurtosis of inter-arrival times, plus normalized decay curves with Full Width at Half Maximum (FWHM) analysis. Hybrid models incorporated paralyzable and non-paralyzable components with dead …

Advancing Thermodynamic Modeling In Materials Design: From Phase Stability In Metallic Systems To Ferroelectric Property Prediction In Functional Oxide, Kyaw Hla Saing Chak

Doctoral Dissertations

This dissertation advances the CALPHAD (CALculation of PHase Diagrams) approach for thermodynamic modeling which often lacks sufficient description of crystal lattices for critical phases in multicomponent system. Additionally, CALPHAD does not consider the structural features and its connection with functional properties for functional materials. To address these issues, a novel dual-ordered sublattice model for the κ-phase in Fe-Al-C system, (Fe,Al)3(Fe,Al)1(C,Va)1(C,Va)3, is introduced that improves predictions of equilibrium compositions and phase stability by accounting for both substitutional and interstitial ordering. For the Fe-B-C system, new sublattice formulations for FCC [(Fe)1(C,B,Va)1] and BCC [(Fe,B)1(C,B,Va)3], phases enhance boron solubility predictions and reveal insights …

Dynamic Response, Assessment, And Retrofitting Of Prestressed Concrete Bridge Girders Subjected To Lateral Impact Loads, Haitham A. Abdelmalek

Doctoral Dissertations

Over height vehicle collisions pose a growing threat to the resilience of bridge infrastructure across the United States. According to the American Road & Transportation Builders Association (ARTBA, 2024), approximately 36% of all U.S. bridges require major repair or replacement, with an estimated cost of $400 billion. This dissertation investigates the structural dynamic response, damage assessment, and retrofitting strategies for prestressed concrete (PC) bridge girders subjected to lateral impact loading.

The research comprised two main phases: (1) numerical modeling and (2) experimental testing. A validated 3D nonlinear finite element (FE) model was developed to conduct parametric studies on impact behavior, …

Nutrient Fate, Mass Balance, Species Selection, And Plant Management Strategies With Floating Treatment Wetlands, Carla Campbell

Doctoral Dissertations

Excess nutrients in shallow urban ponds can lead to harmful algal blooms, low dissolved oxygen, and internal phosphorus cycling. These effects can compromise municipal water supplies and cause negative impacts on fisheries and recreation. Floating treatment wetlands (FTWs) show promise to mitigate surface water nutrient pollution. However, despite recent research, many aspects of FTWs remain poorly characterized. The contribution of FTW components to nutrient removal, local species selection, and plant management strategies were the focus of this dissertation. Mesocosm experiments revealed that the combination of plants and coir fiber growth media in FTWs appeared to positively affect plant biomass gain …

Synthesis Of Graphene Using Carbonaceous Materials In An Ultrasonic Reactor, Paul Chukwuma Ani

Doctoral Dissertations

The synthesis of high-quality graphene from sustainable carbonaceous feedstocks offers an avenue to reduce the environmental and economic costs associated with conventional graphite-based production. This research investigates the co-utilization of biochar and graphite as precursors for graphene fabrication in an ultrasonic reactor. Biochar was produced from biomass via downdraft gasification at 850 °C, yielding a high fixed-carbon, partially graphitized material with favorable surface area and porosity. Graphite, selected for its crystalline structure, was investigated with biochar to assess the influence of precursor composition on exfoliation efficiency, layer thickness distribution, and defect density. Ultrasonic-assisted liquid-phase exfoliation was employed, with process parameters …

Tailoring Selected Aerogels To Targeted Applications, Stephen Yaw Owusu

Doctoral Dissertations

Aerogels are ultra-lightweight, porous solid materials characterized by a three-dimensional nanostructured network. Owing to their exceptional physical and chemical properties, aerogels have garnered considerable attention within the materials science community and were recognized by IUPAC in 2022 as one of the top ten emerging technologies in chemistry. Although numerous aerogels have been synthesized, only a few have been effectively tailored for specific applications. Optimizing the properties of known aerogels for targeted uses remains challenging. This dissertation investigates strategies for tailoring aerogels derived from isocyanate-benzoxazine, benzodiazine, and phenolic resins to meet the requirements of various advanced applications. The approaches employed include …

Deep Learning Architecture Design For Nano-Satellite Image Super-Resolution, William Everette Symolon

Doctoral Dissertations

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. NanoSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation, and research. They also have the potential to enhance satellite constellation resilience. However, the inherent size, weight, and power limitations of NanoSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution, which limits the utility of CubeSat images for military planning purposes and national intelligence applications. This research proposed a deep learning architecture capable of enhancing …

Advancements In Signal Processing And Image Reconstruction For Active Microwave Thermographic Measurements, Logan Martin Wilcox

Doctoral Dissertations

Active microwave thermography, or AMT, is a coupled electromagnetic-thermographic nondestructive testing and evaluation technique. AMT has found success in a variety of inspection needs in the aerospace, space, and infrastructure fields due to its unique type of thermal excitation. During an AMT inspection, a specimen is exposed to microwave energy from a radiating source (i.e., an antenna). This exposure to microwave energy results in dielectric/magnetic heating, which causes an increase in temperature and potential defect indications to manifest on an inspection surface (which is measured via an infrared camera). Due to the use of an antenna, there is a spatially …

Machine Learning Models For Location Prediction, Message Routing And Path Planning For Rescue Of Underground Miners, Abhay Goyal

Doctoral Dissertations

Self-rescue during underground mine disasters is vital for miner safety. Evolving hazards and post-disaster conditions demand solutions that enable navigation under severe communication and computational constraints. Centralized systems often fail in such rugged settings, while decentralized methods—particularly Delay Tolerant Networks (DTNs), proven in battlefields and space missions—offer distinct advantages for underground applications. This research addresses five core challenges: (i) predicting miners’ next locations on low-power devices using points of interest and movement sequences; (ii) delivering timely updates on safe routes, evacuation zones, and hazardous areas; (iii) evaluating energy efficiency and comparing graph-based approaches to existing methods; (iv) enabling edge-ready frameworks, …

Investigating The Effects Of Fly Ash On The Properties Of Silica-Deficient High Alumina Materials, Sai Akshay Ponduru

Doctoral Dissertations

This research is made up of five studies mainly focused on attaining sustainability in high alumina cements like calcium aluminate cement (CAC) and calcium sulfoaluminate belite cements (CSAB) by partially replacing them with additives and supplementary cementitious materials (SCMs) like fly ash (FA). The first study focused on understanding the reaction kinetics of CAC and FA binders at different replacement levels (i.e. between 10%-to-50% by mass) and their effect on mechanical properties. It was also shown that the reactive nature of FAs can be determined by calculating a single parameter called number of constraints (nc) derived from topological constraint theory. …

Process-Structure-Property Relationships In Laser Powder Bed Fusion Produced 17-4 Ph Steel, Ben Brown

Doctoral Dissertations

Laser powder bed fusion (LPBF) is a metal additive manufacturing method that produces non-traditional microstructures as a result of the rapid solidification and thermal cycling inherent to the process. When using LPBF-produced material in application, these unique microstructures challenge the applicability of well-developed mechanical property databases achieved by conventional heat treatments. For wider adoption of this technology, a more holistic understanding is necessary on how process attributes develop material structure, which dictate mechanical properties. This dissertation explores the process structure-property relationships in LPBF 17-4 PH steel through systematic evaluation of atmospheric processing and heat treatment effects on microstructure and mechanical …

Identification Of Corrosion Damage, Vibration, And Loose Conections In Aircraft Data Transmission Lines Using Reflected And Transmitted Signals; Formulation Of Vegetable Oil-Based Nanofluids As Cutting Fluids For Mql Machining, Saidanvardzhon Valiev

Doctoral Dissertations

This research presents a novel Frequency Domain Transmitometry (FDT) method that uses transmitted signals (S21) to detect and characterize aircraft data transmission lines (ADTL) corrosion damage without additional reflectometry circuitry. Corrosion experiments were conducted according to ASTM G85-A5 over 14 weeks. Combined FDT and reflected signals (S11) analyses revealed distinct three-peak signatures associated with damage and corrosion. The square root of the Area Under the Curve (AUC) of the FDT damage peak and the Full Width at Three-Quarter Maximum (FW3QM) of the S11 damage peak is correlated with corrosion propagation depth and width. S11 signals were further used for vibration …

Miniaturized Wearable Biosensors For Continuous Health Monitoring Fabricated Using The Femtosecond Laser-Induced Graphene Surface And Encapsulated Traces And Electrodes, Homayoon Soleimani Dinani, Tatianna Reinbolt, Bohong Zhang, Ganggang Zhao, Rex E. Gerald, Zheng Yan, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Wearable sensors are increasingly being used as biosensors for health monitoring. Current wearable devices are large, heavy, invasive, skin irritants, or not continuous. Miniaturization was chosen to address these issues, using a femtosecond laser-conversion technique to fabricate miniaturized laser-induced graphene (LIG) sensor arrays on and encapsulated within a polyimide substrate. The femtosecond laser-converted conductive traces can have a size of 20 to 2 μm compared to the traditionally larger CO2 laser dimensions of around 300 to 100 μm. This marks a 93-98% decrease in trace size when using a femtosecond laser. This miniaturization allows for the ability to process temperature, …

Enhanced Multidimensional Harmonic Retrieval In Mimo Wireless Channel Sounding, Yanming Zhang, Wenchao Xu, A. Long Jin, Tianquan Tang, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a two-dimensional matrix format along the r-th dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the r-th dimension, respectively. Furthermore, recursive …

Component Level Em Emission Assessment And Management For Rf Desensitization, Xiangrui Su, Wenchang Huang, Junghee Cho, Joonki Paek, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Radio frequency (RF) desensitization is a common issue caused by high-speed components in modern electronic devices. Consequently, numerous studies have focused on characterizing and quantifying electromagnetic (EM) emission sources by using near field scanning to detect EM noise sources. However, before near field scanning, no predetermined threshold is available to quickly assess whether an EM noise source will pose RF desensitization risks in the receiving antenna. This article presents a method to optimize near field scanning settings through EM emission management analysis. Drawing on experience from numerous EM emission studies, we introduce an EM emission management procedure for two common …

Print Quality Assessment Of Additively Manufactured Resonant Structures, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Cody Morrow, Doyle T. Motes, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Due to the increased popularity of additive manufacturing (AM) and availability of electrically conductive filaments, the potential for using AM to fabricate high frequency resonant structures has been realized. As these structures require specific operating conditions to provide the desired resonant response (such as substrate/superstrate material, operating frequency, polarization, etc.), print quality validation through an assessment of the resonant response can be difficult to accomplish. To this end, active microwave thermography (AMT) is proposed as an alternative technique to assess and validate an AM resonant structure. Two AM resonant structures were manufactured using fused deposition modeling (FDM) and the resonant …

Mapping Biomedical Ontology Terms To Ids: Effect Of Domain Prevalence On Prediction Accuracy, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

This study evaluates the ability of large language models (LLMs) to map biomedical ontology terms to their corresponding ontology IDs across the Human Phenotype Ontology (HPO), Gene Ontology (GO), and UniProtKB terminologies. Using counts of ontology IDs in the PubMed Central (PMC) dataset as a surrogate for their prevalence in the biomedical literature, we examined the relationship between ontology ID prevalence and mapping accuracy. Results indicate that ontology ID prevalence strongly predicts accurate mapping of HPO terms to HPO IDs, GO terms to GO IDs, and protein names to UniProtKB accession numbers. Higher prevalence of ontology IDs in the biomedical …

Enhancing Measurement Accuracy In Industrial Applications: The Impact Of Sensor Data Imputation On Model Parameter Estimation, Steven Thompson, Michkath Omanda Bouraima, Maciej Jan Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Digital twins are meant to revolutionize the manufacturing industry by enabling advanced condition monitoring and predictive maintenance processes. However, disruptions within the manufacturing process, such as sensor malfunctions or connectivity issues are inevitable and will cripple these advanced analysis methods if not properly addressed. Therefore, efficient data management and analysis practices are key to advancing this technology. This work examines the impact of missing data imputation on model parameter estimation, a crucial task in developing models for digital twins. We theoretically derive the Cramer-Rao Lower Bound (CRLB) for a DC signal with an unknown scalar parameter in the presence of …

A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Although deep learning is increasingly promising in the field of Non-Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost-effective sequence-to-points NILM solution is proposed, integrating three-point labelling with non-causal convolution techniques. The approach introduces a semi-automatic labelling framework for obtaining NILM three-point data, which provides a low-cost data collection and labelling solution for large-scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence-to-points scenario …

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise …

Filter Based Motor Control For Robotic Applications, Shuzan Kumar Sarkar, Noah Wilding, Shruti Pandey, Nicholas Hawkins, Michael L. Mcintyre

Electrical and Computer Engineering Faculty Research & Creative Works

Controlling coreless DC motor in the field of humanoid robotic application involves considering various surrounding electromagnetic environment interference and sudden change of load with parameters variation of motor dynamics which makes the system complex. This paper presents a filter-based control scheme for a coreless DC motor drive system using an H-bridge inverter as the input circuit of the motor which is easy to implement and cost effective. From the electromagnetic characteristics, the dynamic model of the motor along with the control scheme is implemented in the commercial software PLECS. Then the effectiveness of this approach is validated through simulations demonstrating …

Impact Of Voltage Regulator Modules On Power Distribution Network Impedance, Hanyu Zhang, Zhiping Yang, Alvis Hsu, Ryan Hou, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The voltage regulator module (VRM) impacts the power distribution network (PDN) impedance in the low frequency range. The effect of VRM is essential for reliable PDN analysis and simulation. However, the existing VRM models for power integrity (PI) simulation lack an in-depth understanding of the effect of the feedback control in the VRM. In this paper, the impact of the VRM control loop on the PDN impedance is investigated. The relationship between the VRM output impedance and the PDN impedance at the IC input pin is derived. The output impedance of a VRM is analyzed using small signal analysis. Eventually, …

Extended S-Parameter Model Of The Power Distribution Network For Rapid Coupling Predictions, Cody Goins, Aaron Harmon, Mckennan Starkey, Kristen Donnell, Victor Khilkevich, Daryl Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Power and return planes are part of the power delivery network of almost all modern high frequency printed circuit boards. These power and return planes can form the basis of unintended radiated emissions from, or radiated coupling to, these boards. Predicting coupling to complex systems is a difficult problem and typically reserved for full wave simulations. Recent works have introduced segmentation approaches that are able to predict coupling to complex printed circuit board designs by using pre-rendered segments and cascading these segments through a circuit solver approach. The extended S-parameter models used by the segmentation approach currently do not include …

Efficient Decoupling Capacitor Impact Calculation, Faye Squires, Yifan Ding, Matthew Doyle, Matteo Cocchini, Samuel Connor, Francesco De Paulis, Albert E. Ruehli, Chulsoon Hwang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

Methods of optimizing decoupling capacitor placement on power distribution networks (PDNs) are often limited due to the computational complexity required to calculate the impact of connecting loads to an impedance matrix with hundreds of rows and columns. This work proposes that by removing all but one member of the impedance matrix before calculating, checking the impact of adding capacitors to the matrix can be done efficiently, and optimization methods can be viable even when requiring millions of impedance calculations.

Analyzing Micrometeoroid And Orbital Debris Shield Performance For Sample Return Spacecraft, William P. Schonberg, Michael Squire

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Sample-return missions typically seek to collect and return samples from extraterrestrial locations to Earth for analysis. The return of samples from certain locations in the solar system represents a potential hazard to Earth's biosphere from foreign microorganisms. One way this could occur would be a break-up of the returning capsule during entry (which would disperse the samples through the air) due to undetected damage to the capsule thermal protection system (TPS). As a result, missions that plan to return payloads from some destinations are likely to have certain design requirements for the TPS surrounding their returning capsules, which may include …

An Improved Table Method For Coded Target Identification With Application To Photogrammetric Analysis Of Soil Specimen During Triaxial Testing, Xiaolong Xia, Xiong Zhang, Sara Fayek, Zhaozheng Yin

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Accurate and efficient recognition and identification of coded targets are of great importance in coded target-based photogrammetry. Recently, a deep learning-based method has been utilized to recognize the coded targets. Then, a table method has been developed to decode the coded targets, identify falsely identified coded targets, and recover missing coded targets. This method takes advantage of the geometric arrangement of the coded targets. In this paper, an improved table method has been developed to improve the coded targets recognition and identification results. Blob analysis, instead of deep learning, is utilized to recognize coded targets. Then, the RANSAC algorithm was …

Challenges In Numerical Simulation Of Frost Heave, Antai Dong, Xiong Zhang

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Frost heave of soil extensively exists in northern regions and poses a significant threat to infrastructure in cold regions. Despite over a century of research, challenges persist in numerically simulating frost heave. This study addresses two key issues: (1) What is the primary driving force for liquid water transfer during the freezing process? (2) How can we correctly represent unfrozen water content? Critical insights are derived from the theoretical analysis of coupled hydrothermal migration during soil freezing processes, followed by a case simulation using COMSOL Multiphysics. It concludes that of the water content gradient, suction gradient, and hydraulic gradient, only …