Engineering Commons^™

Development Of Non-Precious Iron-Cobalt Alloy Catalyst For Electrocatalytic Reaction, Harsh Panchal

Electronic Theses & Dissertations

The development of a FeCo alloy catalyst with tunable Fe/Co ratios is examined to improve electrocatalytic performance in reactions like the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR). Many energy conversion devices, such as fuel cells, metal-air batteries, and water-splitting systems, depend on these interactions to function. These technologies have huge potential to meet the increasing need for hydrogen production and renewable energy sources worldwide, which are critical to attaining a sustainable energy future. When compared with noble metal-based catalysts, the FeCo alloy catalyst shows much higher catalytic activity, according to previous studies. Hydrothermal …

Zn–Assisted Synthesis Of M (Mn/Fe/Co/Ni)-N-C Catalysts For Multifunctional Electrochemical Activity, Kemila A. Chaudhary

Electronic Theses & Dissertations

The design and development of atomically dispersed M-N-C catalysts (metal (M) supported on an nitrogen-carbon (NC) matrix with high multifunctional electrocatalytic performance is desirable but proved to be very challenging. Herein, we synthesized M-N-C catalysts (M = Fe, Co, Mn, and Ni) using Zn-assisted high temperature treatment and characterized using various techniques. The prepared catalysts were tested for their electrocatalytic performance towards oxygen and hydrogen evolution reaction (OER and HER) as well as oxygen reduction reaction (ORR) in alkaline media. The results indicated that Mn-N-C catalyst showed higher performance towards both the ORR (E_1/2 = 0.90 V) and …

Who Owns The Wind: The Absence Of Community Wind Farms In California, Sky Berry-Weiss

Master's Projects and Capstones

Community ownership structures for wind farms have been around for decades, particularly in European countries, due to high socioeconomic benefits. Given these significant benefits, one might expect community wind to thrive in the United States—especially in a state like California, which prides itself on progressive climate policy and renewable energy leadership. Yet utility-scale community wind remains largely absent from research on California’s energy system, raising questions about its existence in the state. To pinpoint how many utility-scale community owned wind farms are in California, this study surveys every operational wind turbine in the state. After classifying each wind farm by …

Design Of A Subthreshold Cmos Inverter-Based Amplifier For Low-Noise And Low-Power Applications, Landon Alexander Schmucker

Electrical and Computer Engineering ETDs

Amplification is a fundamental function in most analog circuits. There is a fast-growing demand for low-power, low-noise, and high-gain amplifiers. Modern semiconductor processes are increasingly optimized for digital applications, which has introduced new challenges in analog design. To address these challenges, analog designers have investigated replacing conventional analog circuits with digital implementations. One promising application is the typical CMOS inverter as an amplifier.

This research presents a CMOS inverter-based amplifier with feedback designed to achieve low power consumption, low input noise, and high gain. Unlike typical CMOS inverter-based amplifiers, this topology has two distinctive features: (1) it uses a MOSFET …

A Fine-Tined Bert Model For Improved Querying Of The Unmanned Aerial System Integration Safety And Security Technology Ontology, Minh Hong To

Theses and Dissertations

The use of unmanned aerial vehicles (UAS) in all industries is steadily increasing every year. To govern the use of UAS, the Federal Aviation Administration (FAA) seeks to provide a foundation of rules and regulations for UAS operation in the National Airspace System (NAS). The UAS Integration Safety and Security Technology Ontology (ISSTO) was developed using the Web Ontology Language (OWL) in 2023. In 2024, a query application was developed to search ISSTO for information about the safety and security of UAS operations. While the application is functional, the search results can be further fine-tuned to match what the user …

The Role Of K-12 Educators In Shaping Stem Pathways: Examining Social Capital And Self-Efficacy In Stem Education, Holly Trisch

Theses and Dissertations

This dissertation examines the impact of K-12 educators on students’ pathways in STEM (Science, Technology, Engineering, and Mathematics) by focusing on two main factors: the social capital of K-12 and elementary school teachers and the self-efficacy of preservice teachers in STEM education instruction. The first study investigates the social capital of first year engineering students, emphasizing the relationships and resources they gained during their K-12 education that influenced their decision to major in engineering. A quantitative study using survey data collected from first-year undergraduate engineering students indicates that K-12 educators play a crucial role by providing mentorship and resources that …

An Updated Viscoplastic Self-Consistent Model To Capture The Effects Of Residual Stress, Microstructural Heterogeneity, And Precipitation In Cold-Sprayed Aluminum Alloys, Aulora Williams

Theses and Dissertations

Cold spray additively manufactured (CSAM) aluminum alloys exhibit heterogenous microstructures and mechanical properties, primarily due to high dislocation densities, sub-grain structures, and variation in inter-particle (intersplat) bonding arising from high-velocity particle impacts. Thermal post-processing has been shown to enhance intersplat bonding, reduce high residual stress concentrations, and lower dislocation densities, thereby improving the overall mechanical properties. Building on previous advancements with a mean-field viscoplastic self-consistent (VPSC) model, integrating intersplat boundary terms, Hall-Petch relations, and physically informed residual stress parameters, the current work extends the model to include precipitates and their effects. Observations of CSAM aluminum 7050 alloy indicate precipitate congregation …

An Approach To Expanding The Industrial Training And Assessment Center To The Residential Sector, Cailee Addison Bush

Theses and Dissertations

The Industrial Training and Assessment Center (ITAC) conducts no-cost energy audits for small and medium sized industrial and commercial buildings across the United States, with a goal to train students in energy optimization and cost reduction, while helping companies to reduce inefficiencies and waste. Expanding this program to the residential sector can reduce energy usage across the United States, largely due to this sector contributing to 15% of America’s energy usage. This research examines the development of a procedure to transition the ITAC into including the residential sector. To ensure a seamless introduction of this sector, the procedure must contain …

Adaptive Multi-Sensor Fusion For Robust Autonomous Perception In Unstructured Environments, Samantha S. Carley

Theses and Dissertations

Autonomous vehicles commonly employ multiple sensors to perceive their surroundings. Coupling these sensors would ideally improve perception compared to using a single sensor. An autonomous system can be equipped with object localization and classification, often performed using a visual camera to understand a scene intelligently. Object detection and classification can also be applied to LiDAR and infrared (IR) sensors to further enhance scene awareness of the autonomous system. Herein, sensor-level, decision-level, and feature-level fusion are explored to assess their impact on perception and mitigate sensor disagreements. Specifically, the fusing of RGB, LiDAR, and IR sensor data to improve object classification …

Lateral Control Of Autonomous Vehicle Using Deep Cnn And Ampc, Iffat Ara Ebu

Theses and Dissertations

Autonomous functionalities are pivotal for the advancement of Advanced Driver Assistance Systems (ADAS), driving towards collision-free and environmentally sustainable transportation. This thesis presents two distinct studies addressing critical aspects of autonomous vehicle control: lane centering via deep learning and lateral vehicle control using model predictive control. The first study explores end-to-end learning f or autonomous s teering command generation, focusing on lane centering. A convolutional neural network (CNN) model, inspired by NVIDIA’s PilotNet, is employed to directly map raw camera pixel inputs to steering commands, eliminating the need for intermediate feature engineering. The model is trained and validated using datasets …

Multi-Modal Sensor Fusion Of Radar And Lidar For Enhanced Navigation In Obstacle-Occluded Environments, Kyler Ashton Farrar

Theses and Dissertations

Multi-sensor fusion is a practical and well-researched methodology to combine a variety of incoming sensory data into an enhanced digital representation of a real-world environment. A typical use-case for multi-sensor fusion is the combination of LiDAR and RADAR data to obtain simultaneous 3D positioning and velocity measurements for a particular RoI (Region of Interest). This study investigates LiDAR/RADAR sensor fusion for enhanced navigation information when placed in obstacle-occluded environments such as highly vegetated areas. Specifically, a novel fusion-map approach is designed and evaluated for use with a LiDAR/RADAR sensor suite to produce a fused cost map to determine optimal and …

Interactions Between Different Deformation Mechanisms In Magnesium, Ethan T. Holder

Theses and Dissertations

Magnesium is a strong and lightweight material with the potential to be used for weight reduction in various industries. However, manufacturing magnesium parts is difficult because magnesium is brittle at room temperature. Understanding the underlying deformation mechanisms in magnesium is critical to improving its ductility. In this work a Rapid Artificial Neural Network (RANN) potential was used to perform a Molecular Dynamics (MD) simulation in Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) to study twin formation and growth as well as interactions between different twin modes in a magnesium bicrystal. This work offers insight into the mechanisms of plastic deformation in …

Investigating Structure-Property Relationships Of Bio-Inspired Gel, Mohammad Moinul Hossain

Theses and Dissertations

High-modulus, stretchable, and resilient hydrogels possess a wide array of promising applications across multiple fields, such as the development of sophisticated prosthetic devices, artificial skin, electronic devices, and soft robotics. Elastomeric biopolymers, like resilin, show high stretchability and resilience, facilitating power-amplified movement in various species essential for feeding and defense mechanisms. To mimic the properties of resilin, we developed a hydrogel system of hydrophobic and hydrophilic components. These gels are synthesized by free radical polymerization of acrylic acid (AAc), methacrylamide (MAM), n-tert-beutylacrylamide (BAM) or n-isopropylacrylamide (NIPAM), and poly (propylene glycol) diacrylate (PPGDA). This research aims to compare the large-strain mechanical …

Finite Element Analysis Of Hydroxyapatite (Ha) Coated Porous We43 Magnesium Scaffolds For Simulating Long-Time In Vitro Degradation Using A Continuum Damage Model, Dam Kim

Theses and Dissertations

While magnesium and its alloys are viable biodegradable implant candidates for its similarity of Young’s modulus with human bone and its strong biocompatibility, a pure magnesium scaffold degrades too rapidly to be used in many orthopedic implications. Overcoming this high degradation rate has been one of the biggest technical challenges for Mg-based biomedical applications. This research focuses on validating a finite element analysis framework using a continuum damage model and its application to simulate the degradation process of non-coated additively manufactured WE43 and HA-coated WE43 magnesium scaffolds. This work includes determining model input parameters by matching in vitro experiment results …

An In Depth Examination Of Educational, Family, Economic, And Personal Determinants On Academic Performance, Engagement, And Stress: A Comprehensive Study Among Engineering Students, Oumaima Larif

Theses and Dissertations

This study explores the effects of family, education, economic, and personal factors on students’ decisions to pursue engineering as a profession and their long-term impact on performance as engineering students. We adopted a mixed-method approach, collecting data through surveys administered to undergraduate and graduate engineering students at Mississippi State University. The study results revealed that family, education, economic, and personal factors profoundly influence students' decisions to study engineering. We found that parental expectations, background information, and socioeconomic status, in conjunction with cultural norms, values, gender expectations, and religious beliefs, affect students. Additionally, this study identified gaps in the existing literature …

Asphalt Durability Improvements By Way Of Specification Advancements, Jessica V. Lewis

Theses and Dissertations

The objective of this dissertation is to provide content through which industry and agencies can adjust and/or update standards and specifications, as appropriate, to accommodate the present-day needs of the asphalt industry towards producing more durable asphalt. A total of 78 asphalt mixtures and 9 asphalt binders were evaluated for up to 10 years of field aging to provide content that was analyzed to meet this objective. A full-scale aging site in Columbus, Mississippi was the focal point of the aging experiments. A major conclusion from this work was that Cantabro Mass Loss (CML) testing of cores was able to …

Impact Of Circular And Non-Circular Nozzle Exit Geometries On Jet Flow Propagation And Turbulence Characteristics, Hiba Maazioui

Theses and Dissertations

This thesis aims to investigate the impact of nozzle exit geometry on jet flow propagation and turbulence characteristics. Computational fluid dynamics simulations were performed using ANSYS Fluent, employing the Reynolds-Averaged Navier-Stokes (RANS) approach, to compare circular, elliptic, and rectangular nozzle geometries. The analysis focused on velocity distributions, turbulence characteristics, and cross-flow interactions. Results indicate that circular jets have the longest potential core and slowest velocity decay, reflecting lower mixing rates. In contrast, elliptic and rectangular jets have shorter potential cores, faster velocity decay, and elevated TKE peaks, suggesting enhanced turbulence and mixing. These findings highlight the significant role of nozzle …

A Rapid Artificial Neural Network Interatomic Potential For Bismuth, Lee Michael Mayfield Jr.

Theses and Dissertations

To study molecular dynamics in bismuth, Quantum Espresso was used to create a density functional theory (DFT) database which was then used as the input for rapid artificial neural network (RANN). The RANN interatomic potential that was developed using this database was then validated. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) was used to create simulations and gather data using the RANN potential. The properties of bismuth, including elastic constants, melting points, and volume change, were calculated and compared to DFT and experimentally observed data. The RANN potential coincided well with these values. The RANN potential shows good predictive capabilities for …

Integration Of Machine Learning Techniques With Computational Fluid Dynamics For Enhanced Backward-Facing Step Flow, Yahya Mekkaoui

Theses and Dissertations

This research presents a novel integrated framework combining computational fluid dynamics (CFD) with machine learning techniques to enhance the analysis of backward-facing step flows. A high-fidelity OpenFOAM CFD simulation was developed and validated against experimental data, accurately predicting the reattachment length (��/�� ≈ 6.18) and velocity profiles throughout the domain. Machine learning models, including Random Forest, Gradient Boosting, and Support Vector Regression, were integrated through a robust data pipeline, with the ensemble approach demonstrating superior performance (RMSE of 1.18 m/s, ��2 of 0.951). Feature importance analysis revealed pressure (32%) and turbulent kinetic energy (28%) as the dominant physical parameters governing …

Atomistic Modeling Of Mg And Al Systems Using Rapid Artificial Neural Network Derived Interatomic Potential, Edward Omozusi Obasuyi

Theses and Dissertations

Machine learning derived interatomic potentials have proven to be effective tools in simulation that mimic the accuracy of ab-initio calculations to the sub meV/atom level. They offer the advantage of computational speed operating at linear scaling with respect to the system’s size making them more efficient than classical potentials like MEAM (modified embedded atom methods). In this work, interatomic potentials are created based on the rapid artificial neural network (RANN) formalism for magnesium (Mg), Aluminum (Al) and their alloys. From previous works, the RANN formalism produces high-fidelity atomic models with accurate force fields for several metals, offering new insights into …

Removal Of Harmful Algal Bloom Toxin, Microcystin-Lr Via Graphene Coated Polymers, Justin Douglas Puhnaty

Theses and Dissertations

This study investigates a novel 3D-printed graphene-coated polymer (GCP) for the removal of Microcystin-LR (MC-LR), a harmful cyanotoxin produced during harmful algal blooms (HABs). While graphene nanoplatelets (GnPs) exhibit high adsorption capacity, their powdered form limits practical application. To address this, GnPs were coated onto 3D-printed poly(lactic acid) (PLA) substrates, enabling enhanced handling for field deployment. Surface characterization using laser confocal microscopy, Raman spectroscopy, and thermogravimetric analysis confirmed GnP distribution and coating uniformity. Batch adsorption experiments revealed pseudo-second-order kinetics, a maximum adsorption capacity (qmax) of 596 μg/g, and adsorption behavior best described by the Langmuir isotherm. Statistical analysis and comparison …

An Overview Of Global Navigation Satellite System Reflectometry In Coastal Wetlands, Luke Andrew Redwine

Theses and Dissertations

With rising global temperatures, increasing sea levels, and the accelerated erosion of coastal wetlands, efficient methods for monitoring this vulnerable ecosystem are crucial. Traditional approaches, such as manual surveys, are labor-intensive, hazardous, and invasive to the environment they are attempting to protect, while current remote sensing methods are cost prohibitive and rely on irregular data collection techniques. To address these challenges, a scalable solution is needed for reliable and frequent data collection. This study explores the use of GNSS Reflectometry (GNSS-R) combined with unmanned aerial vehicles (UAVs) to monitor the shifting topology in wetlands with minimal human invasion. By leveraging …

The Importance Of Community: An Investigation Of Stress, Coping, And The Value Of Social Support For First Responders, Brian Reid

Theses and Dissertations

People are designed to be in community with others, to work together and share the load and weight of life. First responders are a community that has not emphasized the importance of social support to mitigate and buffer against the stress inherent in their jobs. This study investigates the sources of stress, coping methods, and social support of first responders. Results from the first study show the impact of workplace and family stress on the first responder is impactful from the beginning. The secondary study finds that adaptive coping methods are the preferred method to cope with stress and that …

Raad-Llm: Adaptive Anomaly Detection Using Llms And Rag Integration, Alicia Russell-Gilbert

Theses and Dissertations

Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies that are adaptive, transferable, and capable of integrating domain-specific knowledge. This work presents RAAD-LLM, a novel framework for adaptive anomaly detection, leveraging large language models (LLMs) integrated with Retrieval-Augmented Generation (RAG). This approach addresses the aforementioned PdM challenges. By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time-series data without requiring fine-tuning on specific datasets. The framework's adaptability mechanism enables it to adjust its understanding of normal operating conditions …

Efficient Wattle Design For High-Level Nuclear Waste Tanks, Jasmine B. Clarke

UNLV Theses, Dissertations, Professional Papers, and Capstones

At the Hanford Site in Washington state and the Savannah River Site in South Carolina, there are underground steel tanks that hold high-level nuclear waste (HLW) and nearly 450 million curies of radioactivity. At these sites, some leaks have been detected in HLW tanks. While detecting these leaks allows for identification of the issue, it does not solve the problem or prevent the leakage of HLW contaminants into collection pans and secondary containment. Repairing these leaks poses potential threats to workers for exposure risk. To address this issue, the present research seeks to create and utilize an active barrier in …

Mfgat: Map-Free Trajectory Prediction With Graph Attention Networks For Autonomous Vehicles, Zehra Gunindi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This …

Improved Electric Load Modeling Of Residential Air Conditioning, Julius Johnson

UNLV Theses, Dissertations, Professional Papers, and Capstones

The recent increase in energy production from renewable resource introduces a new challenge in managing and maintaining balance between electricity supply and demand, due to uncertainty and variability of wind speed and solar irradiance. To address this growing problem, demand-side management, such as Demand Response (DR) programs, is employed to adjust power consumption. Residential air conditioners (ACs) are the most suitable candidates for DR, due to their intensive power consumption and inherent thermal inertia that allows flexibility in their operations (by adjusting their set-point temperatures) without sacrificing customer comfort. Most prior research on AC load models assumes that such a …

El, A Navigational Assistant Based Upon Echolocation, Arthur Mazer

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis investigates the integration of a parametric speaker with a microphone array to enhance the echolocation of objects. A parametric speaker focuses ultrasonic and audible waves in a specified direction. This endows the parametric speaker with the capacity to focus waves across a wide frequency spectrum enabling adaptation of the frequency to environmental considerations.Beam forming allows one to focus a microphone array in a specified direction. The thesis investigates different microphone array configurations for the purpose of enhancing the echolocation ability of an echolocation device. The primary goal of the thesis is the construction and testing of an echolocation …

Analysis Of Incident Duration And Real-Time Incident Delay Estimation Using Big Data Analytics And Machine Learning Techniques, Zhubin Najafi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Traffic incidents have a significant impact on freeway operations, leading to severe delays, congestion, fuel wastage, and economic losses for commuters. Each year, billions of gallons of fuel are wasted, and drivers incur thousands of dollars in lost time due to incident-related delays. To mitigate these effects, Traffic Management Centers (TMCs) implement incident management strategies aimed at reducing both the frequency and severity of incidents while ensuring their prompt and safe clearance.

Dynamic Message Signs (DMSs) are key tools used by TMCs to communicate travel times and incident information to commuters. Under normal conditions, default travel times are displayed on …

Investigating Decision Factors For Modularization And Standardization Decision On Early Project Phase: Qualitative Comparative Analysis, Amrit Shahi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Modularization involves shifting site-based work to offsite locations to enhance overall productivity, reduce costs, shorten schedules, and improve project competitiveness. By developing and utilizing consistent designs, facility standardization further optimizes project schedules, costs, and value. When combined, they create a leveraging opportunity, as seen in the shipbuilding and manufacturing industries. While both strategies offer significant advantages, capital projects struggle to have a well-informed modularization and standardization decision during the early project phase (i.e., Opportunity Framing). This often results in improper implementation and lower modularization and standardization levels. The main cause for this is the industry’s limited understanding of early-phase high-level …