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Full-Text Articles in Engineering

Artificial Intelligence And Seeing The Invisible In Arts - From The Wane Of Axiality And Symmetry To The Wax Of Invisibility And Emptiness, M. Samir Abou El-Seoud Jan 2025

Artificial Intelligence And Seeing The Invisible In Arts - From The Wane Of Axiality And Symmetry To The Wax Of Invisibility And Emptiness, M. Samir Abou El-Seoud

Computer Science

Function has been always an important driver in architectural design. It is the request to generate form directly from purpose and utility. In 1896, Louis Sullivan projected that ‘form follows function’ and that the beauty of a building derives directly from its function and not from references of the built heritage. The assumption was that beauty would naturally derive from design once functional requirements are met. Thus, fitness for purpose equals beauty. In 1908, Adolf Loos even went so far as to ask for the total abandonment of decoration. In 1947, Mies van der Rohe made his paradigm shift, function …

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Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi Jan 2025

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

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Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington Jan 2025

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

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Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

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Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

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Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty Jan 2025

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

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Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin Jan 2025

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

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In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana Jan 2025

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

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Supercapacitors With Cotton Shell-Derived Activated Carbons And Porous Polymer Electrolyte Films, Saurabh Singh, Yulin Zhang, S. A. Hashmi, Fuqian Yang Jan 2025

Supercapacitors With Cotton Shell-Derived Activated Carbons And Porous Polymer Electrolyte Films, Saurabh Singh, Yulin Zhang, S. A. Hashmi, Fuqian Yang

Chemical and Materials Engineering Faculty Publications

Some of the well-known challenges in the field of supercapacitors (SCs), or more specifically, electrical double-layer capacitors (EDLCs), such as low energy density and high cost, have proven to be major barriers to their widespread market success despite having some excellent electrochemical merits such as high-power density and good cyclic stability. In this work, efforts have been made to overcome these gaps and eventually enhance the performance of EDLCs via a cost-effective and eco-friendly approach. To fabricate these EDLCs, a bio-waste, namely, cotton-shell-derived activated carbons (ZnACs) (activated with ZnCl2), was used in a mass ratio of 1 : 2 for …

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Improvement Of Cellular Pattern Organization And Clarity Through Centrifugal Force, Lauren E. Mehanna, James D. Boyd, Shelley Remus-Williams, Nicole M. Racca, Dawson P. Spraggins, Martha E. Grady, Brad J. Berron Jan 2025

Improvement Of Cellular Pattern Organization And Clarity Through Centrifugal Force, Lauren E. Mehanna, James D. Boyd, Shelley Remus-Williams, Nicole M. Racca, Dawson P. Spraggins, Martha E. Grady, Brad J. Berron

Chemical and Materials Engineering Faculty Publications

Rapid and strategic cell placement is necessary for high throughput tissue fabrication. Current adhesive cell patterning systems rely on fluidic shear flow to remove cells outside of the patterned regions, but limitations in washing complexity and uniformity prevent adhesive patterns from being widely applied. Centrifugation is commonly used to study the adhesive strength of cells to various substrates; however, the approach has not been applied to selective cell adhesion systems to create highly organized cell patterns. This study shows centrifugation as a promising method to wash cellular patterns after selective binding of cells to the surface has taken place. After …

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Integrating Protein Language Model And Molecular Dynamics Simulations To Discover Antibiofouling Peptides, Ibrahim A. Imam, Shea Bailey, Duolin Wang, Shuai Zeng, Dong Xu, Qing Shao Jan 2025

Integrating Protein Language Model And Molecular Dynamics Simulations To Discover Antibiofouling Peptides, Ibrahim A. Imam, Shea Bailey, Duolin Wang, Shuai Zeng, Dong Xu, Qing Shao

Chemical and Materials Engineering Faculty Publications

Antibiofouling peptide materials prevent the nonspecific adsorption of proteins on devices, enabling them to perform their designed functions as desired in complex biological environments. Due to their importance, research on antibiofouling peptide materials has been one of the central subjects of interfacial engineering. However, only a few antibiofouling peptide sequences have been developed. This narrow scope of antibiofouling peptide materials limits their capacity to adapt to the broad spectrum of application scenarios. To address this issue, we searched for antibiofouling peptides in the vast sequence pool of the microbiome library using a combination of deep learning-based high-throughput search and molecular …

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Clarifying The Role Of Η-Phase With Varying Chemistry On Localised Corrosion Of A Cu-Containing 7xxx Al-Alloy By Quasi-In-Situ Tem, S. K. Kairy, Y. Wu, S. Turk, A. Shekhter, Rudolph Buchheit, N. Birbilis Jan 2025

Clarifying The Role Of Η-Phase With Varying Chemistry On Localised Corrosion Of A Cu-Containing 7xxx Al-Alloy By Quasi-In-Situ Tem, S. K. Kairy, Y. Wu, S. Turk, A. Shekhter, Rudolph Buchheit, N. Birbilis

Chemical and Materials Engineering Faculty Publications

Quasi-in-situ transmission electron microscopy was employed to study dissolution associated with η-phase (Mg(Zn,Al,Cu)2) precipitates in a Cu-containing 7xxx Al-alloy. Two distinct η-phase chemistries, with Cu to (Zn+Mg+Al) ratios ∼0.12 and ∼0.2, were studied in 0.01 M NaCl at pH 6 and 2. In near-neutral NaCl, both η-phase precipitates exhibited ‘duplex’ behaviour, i.e., they were primarily more noble with respect to the surrounding Al-matrix, causing matrix dissolution, and simultaneously established nanoscopic active sites within the noble precipitate itself by generating nanoscale pits. Conversely, η-phase precipitates rapidly dealloyed in acidic NaCl, forming nocrystalline Cu remnants containing twins, which subsequently caused …

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Enhancing Intrinsic Ductility In Vnbmotaw High Entropy Alloy: A Combinatorial Investigation With Experimental Evaluation Of Theoretical Predictions, Taohid Bin Nur Tuhser, Ian Stewart Winter, Daryl Chrzan, Thomas John Balk Jan 2025

Enhancing Intrinsic Ductility In Vnbmotaw High Entropy Alloy: A Combinatorial Investigation With Experimental Evaluation Of Theoretical Predictions, Taohid Bin Nur Tuhser, Ian Stewart Winter, Daryl Chrzan, Thomas John Balk

Chemical and Materials Engineering Faculty Publications

The application of refractory high entropy alloys (RHEAs) as engineering materials has been hindered by their poor inherent ductility. In this study, a novel approach combining thin film combinatorial screening with fragmentation testing was employed to identify intrinsically ductile alloy compositions. Inspired by the physics-based intrinsic ductility parameter, the ‘χ-parameter,’ we investigated the replacement of group VI elements (W/Mo) with group V elements (V, Nb, Ta) in the VNbMoTaW system. Conventional combinatorial analyses, including composition, phase, and hardness assessments, were conducted across a wide range of non-equiatomic configurations. The results revealed a broad compositional space favoring a single-phase body-centered cubic …

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Enhancing Trust In Ai For Healthcare: A Quantative Evaluation Of Explainable Methods In Clinical Decision Support Systems, Abdul Aziz Noor Jan 2025

Enhancing Trust In Ai For Healthcare: A Quantative Evaluation Of Explainable Methods In Clinical Decision Support Systems, Abdul Aziz Noor

Masters

The integration of Artificial Intelligence (AI) into healthcare has revolutionized Clinical Decision Support Systems (CDSS) by enabling sophisticated predictive capabilities. However, the opaque nature of many machine learning models, commonly referred to as "black-box" systems, poses significant challenges to their adoption in critical clinical settings where transparency, interpretability, and trust are paramount. This thesis addresses these challenges by developing a rigorous, mathematically grounded framework to evaluate Explainable AI (XAI) methods and enhance their integration into CDSS.

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Pan-Caribbean Airlines: Unlocking Regional Aviation Potential, Dimitrios V. Siskos, Alexander Maravas, Jordan Karatzas Jan 2025

Pan-Caribbean Airlines: Unlocking Regional Aviation Potential, Dimitrios V. Siskos, Alexander Maravas, Jordan Karatzas

Publications

The Caribbean's air transport system is facing significant challenges, including operational fragmentation, excessive costs, and inadequate inter-island connectivity. These challenges limit its ability to support the region’s heavily tourism-dependent economies. This research evaluates whether consolidating small Caribbean airlines into a single or virtual Pan-Caribbean carrier could transform regional air travel. Based on financial analysis, historical traffic data, and case comparisons with airline mergers in South America and the US, the paper simulates the anticipated efficiencies resulting from route consolidation, fleet standardization, and common operational services. The research concludes that consolidation may create a regional GDP impact of up to $3.3 …

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Engagement In Practice: Partnering With Communities To Address Nuisance Flooding Challenges, Carol L. Considine, Mujde Erten-Unal, Dalya Ismael, Luka Alandra Hamal-Serenity, Farzaneh Soflaei Jan 2025

Engagement In Practice: Partnering With Communities To Address Nuisance Flooding Challenges, Carol L. Considine, Mujde Erten-Unal, Dalya Ismael, Luka Alandra Hamal-Serenity, Farzaneh Soflaei

Engineering Technology Faculty Publications

Many communities are already experiencing the impacts of climate change that disrupt their daily lives. In Coastal Virginia, these impacts take the form of nuisance and stormwater flooding caused by sea level rise and changes in precipitation. Coastal Virginia has one of the highest relative sea level rise rates on the Atlantic Coast and the regional planning district commission recommends that Atlas 14 rainfall intensity, duration, and frequency curves be increased by 20% to account for changes in rainfall. The Coastal Community Design Collaborative (CCDC), a partnership between Hampton University Architecture and Old Dominion University Engineering & Technology, has had …

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From Equitable Access To Equitable Usage: Moving Towards Data-Driven, Evidence-Based Cycling Assets Management, Kewei Ren, Yunping Liang, Chun-Hsing Ho Jan 2025

From Equitable Access To Equitable Usage: Moving Towards Data-Driven, Evidence-Based Cycling Assets Management, Kewei Ren, Yunping Liang, Chun-Hsing Ho

Durham School of Architectural Engineering and Construction: Faculty Publications

Cycling is increasingly recognized for its wide range economic, environmental, and health benefits as a mobility option. However, disparities in the provision and the quality of bicycle infrastructure persists. Ensuring fair access and usage of these benefits for all presents a significant challenge. As a result, establishing effective methodologies to evaluate cycling equity is critically important. This paper synthesizes research on infrastructure asset management with a focus on equity considerations in bicycle infrastructure. A systematic review of 19 North American studies critically examines how existing research categorizes vulnerable populations and assesses fairness. The results reveal that most existing analyses focus …

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Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic Jan 2025

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

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Modeling Ion-Specific Effects In Polyelectrolyte Brushes: A Modified Poisson-Nernst-Planck Model, William J. Ceely, Marina Chugunova, Ali Nadim, James D. Sterling Jan 2025

Modeling Ion-Specific Effects In Polyelectrolyte Brushes: A Modified Poisson-Nernst-Planck Model, William J. Ceely, Marina Chugunova, Ali Nadim, James D. Sterling

Business and Information Technology Faculty Research & Creative Works

Polyelectrolyte brushes consist of a set of charged linear macromolecules, each tethered at one end to a surface. An example is the glycocalyx which refers to hair-like negatively charged sugar molecules that coat the outside membrane of all cells. We consider the transport and equilibrium distribution of ions and the resulting electrical potential when such a brush is immersed in a salt buffer containing monovalent cations (sodium and/or potassium). The Gouy-Chapman model for ion screening at a charged surface captures the effects of the Coulombic force that drives ion electrophoresis and diffusion but neglects non-Coulombic forces and ion pairing. By …

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The Antibiofilm Efficacy Of Copper And Zinc-Enhanced Borate Bioactive Glasses On Polymicrobial Biofilms, Sarah Fakher, David J. Westenberg Jan 2025

The Antibiofilm Efficacy Of Copper And Zinc-Enhanced Borate Bioactive Glasses On Polymicrobial Biofilms, Sarah Fakher, David J. Westenberg

Biological Sciences Faculty Research & Creative Works

Healthcare-acquired infections (HAIs) are a significant global challenge driven by biofilm-forming pathogens. Polymicrobial biofilms, involving interactions between multiple microbial species, exacerbate treatment difficulties due to their enhanced resistance to antimicrobial therapies. Borate bioactive glasses (BBGs) are an emerging class of biomaterials that have attracted significant interest in infection control. The incorporation of copper and zinc into the BBG matrix can effectively disrupt biofilm formation and bacterial colonization. This study investigates the antibiofilm efficacy of copper and zinc-doped BBGs against polymicrobial biofilms formed by S. epidermidis, E. coli, and P. aeruginosa. Using static and dynamic biofilm models, BBGs were applied through …

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Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley Jan 2025

Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Abstract: In remanufacturing, a vital segment of the sustainable, low-carbon circular economy, existing versions of the traditional unequal-areas facility layout problem (UA-FLP) model face significant limitations in designing layouts. To be specific, in the process of minimizing the material-handling cost (MHC), these models also alter departmental dimensions, often diverging from construction specifications. This poses a difficulty, as critical equipment required for remanufacturing, e.g., sorting and cleaning machines, have unalterable dimensions, which implies that departmental dimensions cannot be changed from specifications provided. To address this, a novel Flexible Envelope UA-FLP (FE-UA-FLP) model is proposed in this work for designing layouts wherein …

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Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey Jan 2025

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

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Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo Jan 2025

Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Electricity theft presents a significant challenge to the power industry. This paper demonstrates an adaptive deep framework integrating dimensionality reduction, graph modeling, attention mechanisms, and dynamic feature refinement for improving theft detection. Principal Component Analysis squeezes consumption data while an Autoencoder extracts latent representations and denoises the input. A Gated Graph Convolutional Neural Network uses k-Nearest Neighbors to model local relationships, while Transformers capture long range global dependencies. Neural Ordinary Differential Equations then refine features over continuous time, improving adaptability to complex patterns. The framework achieves 94.01% accuracy with stratified 5-fold cross validation. However, class imbalance challenges the minority class …

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Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim Jan 2025

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

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Degradation Of Radioactive Waste Encapsulation Of Bao−Feox−P2o5 Glass Evaluated From Electrical Conductivity, Kazuki Mitsui, Nobuyasu Nishioka, Hiromichi Takebe, Richard K. Brow, Akira Saitoh Jan 2025

Degradation Of Radioactive Waste Encapsulation Of Bao−Feox−P2o5 Glass Evaluated From Electrical Conductivity, Kazuki Mitsui, Nobuyasu Nishioka, Hiromichi Takebe, Richard K. Brow, Akira Saitoh

Materials Science and Engineering Faculty Research & Creative Works

We report on the electrical conductivity of barium iron phosphate glass, a candidate for radioactive waste encapsulating glass. The glass composition has been optimized for slow dissolution in hot water because the precipitation of a stable hydration layer of FeO(OH) on the glass surface enhances the water durability of the glass. The mechanism of electrical conductivity of the glass is assumed to be an electron hopping between the electronic energy levels of Fe (II) and Fe (III) ions, obeying the electron carrier generation of Fe (II) → Fe (III) + e−. The electrical conductivity of the base glass was ∼3.2 …

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Ground Testing Of A Magnetic-Electrostatic Separation System For Lunar Regolith Beneficiation, Peter Bachle, Charles Wood, Jeffrey Smith, Fateme Rezaei, David Bayless, William Schonberg, Daoru Han Jan 2025

Ground Testing Of A Magnetic-Electrostatic Separation System For Lunar Regolith Beneficiation, Peter Bachle, Charles Wood, Jeffrey Smith, Fateme Rezaei, David Bayless, William Schonberg, Daoru Han

Materials Science and Engineering Faculty Research & Creative Works

The separation of lunar regolith by mineral composition and size category is useful forin-situ resource utilization (ISRU). The research presented herein discusses the designing and development of equipment that has the potential to separate lunar regolith into aluminum, iron-titanium, and magnesium-iron ores. Along with the metal ore separation, this equipment shows the potential to separate regolith by size categories. The combined effect of these separation methods generates output that is valuable to subsequent use in metal and oxygen extraction, additive manufacturing, and regolith sintering processes. The designed equipment uses a dual-strength magnet system with N42 and N52 neodymium magnets for …

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Evaluation Of Processing Conditions For The Reduction Of Electrochemical Salt Waste Using Phosphate-Based Dechlorination, Harmony Werth, Jade Beland, Paige Murray, Dave Liang, Laurel Sharpless, Jonathan Evarts, Charmayne Lonergan, Brian J. Riley, Michael Simpson, Krista Carlson Jan 2025

Evaluation Of Processing Conditions For The Reduction Of Electrochemical Salt Waste Using Phosphate-Based Dechlorination, Harmony Werth, Jade Beland, Paige Murray, Dave Liang, Laurel Sharpless, Jonathan Evarts, Charmayne Lonergan, Brian J. Riley, Michael Simpson, Krista Carlson

Materials Science and Engineering Faculty Research & Creative Works

Electrochemical processing of used nuclear fuel in molten chloride salts generates complex radioactive salt waste. Dechlorination of waste salt using phosphate compounds at elevated temperatures (∼600 °C) reduces the volume of material for disposal while evolving gaseous chlorine compounds that could be used to transform metallic uranium into UCl3. In this study, the effects of processing temperature, environment (air or argon), and H3PO4 precursor-to-chlorine ratio on the dechlorination efficacy of a simple alkali salt mixture (SSM) and a salt waste simulant (ERV3) were evaluated. For both salts, the highest chlorine release was concurrent with the water boiling. For the SSM, …

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Zrc Particle Size Effect On The Mechanical Properties Of Spark Plasma Sintered 60–40 Vol% Zrc–Mo Cermets, Nathaniel Blatt, Jeremy Watts, Brian Taylor, Jhonathan Rosales, Gregory Hilmas Jan 2025

Zrc Particle Size Effect On The Mechanical Properties Of Spark Plasma Sintered 60–40 Vol% Zrc–Mo Cermets, Nathaniel Blatt, Jeremy Watts, Brian Taylor, Jhonathan Rosales, Gregory Hilmas

Materials Science and Engineering Faculty Research & Creative Works

Cermet's of 60 vol% ZrC and 40 vol% Mo with different starting ZrC particle sizes were spark plasma sintered to full density at 1950 °C. Elastic moduli, Vickers hardness and fracture toughness were measured at room temperature and flexural strength was measured up to 1600 °C. Young's modulus was 365 ± 5 GPa, shear modulus was 149 ± 2 GPa, Poisson's ratio was 0.22 ± 0.01, and no significant difference was observed for the moduli with respect to the starting ZrC particle size. Room temperature flexural strength was affected by the ZrC particle size, measured as ∼524 and ∼598 MPa …

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Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai Jan 2025

Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated …

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Viscoelastic Materials Evaluated For Blast-Resistant Designs, M. Sutter, C. Thomas, A. D. Douglas, F. Dogan, C. E. Johnson Jan 2025

Viscoelastic Materials Evaluated For Blast-Resistant Designs, M. Sutter, C. Thomas, A. D. Douglas, F. Dogan, C. E. Johnson

Mining Engineering Faculty Research & Creative Works

Viscoelastic materials have extensive military applications due to their energy absorption capabilities, with the potential to reduce blast energy imposed on buildings, vehicles, and personnel. Based on current literature, limited information is available regarding the mitigation of blast energy related to these uses. The impact of thickness, nanoparticle addition, and layering variation was assessed in this study using commercially available viscoelastic materials in open-air blasts of Composition C4 to determine shock energy mitigation capabilities. Time-pressure waveforms were recorded to identify optimal changes in shock wave characteristics: reduced peak pressure, positive phase duration, and impulse, with increased rise times. Results were …

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