Engineering Commons^™

Scalable Quantum Network Routing Through Reinforcement Learning And Resource Optimization, Tasdiqul Islam

Computer Science and Engineering Dissertations

Long-distance quantum communication depends on distributing high-delity entanglement across quantum repeaters. Entangled states are fragile: they decohere in memory, are consumed when used, and lose delity after each swap. Quantum routing therefore diers from classical routing: an algorithm must decide not only the path, but when to generate, store, swap, and consume entanglement before they lose their usefulness. This dissertation studies scalable resource allocation and routing for quantum networks under delity, memory, and concurrency constraints. It rst addresses re- peater deployment with heuristics that nd near-optimal locations while cutting com- putation from days to seconds versus integer linear programming (ILP). …

Early Detection And Objective Assessment Of Neonatal Hypoxic-Ischemic Encephalopathy Severity, Soheila Norasteh

Bioengineering Dissertations

Hypoxic–ischemic encephalopathy (HIE) is a neonatal brain injury caused by reduced oxygen and blood flow to the brain around the time of birth. It remains a major cause of neonatal mortality and long-term neurodevelopmental impairment worldwide. Therapeutic hypothermia is the standard treatment for moderate-to severe HIE and improves outcomes when initiated within the first six hours of life. Therefore, accurate assessment of injury severity during this period is essential. Cur rently, HIE severity is determined primarily through neurological examination and classified as mild, moderate, or severe. However, these examinations are subjective, cannot provide continuous monitoring of brain function, and may …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Enhancing Traffic State Estimation At Bottlenecks Through Improved Demand Modeling: A Greenshields-Grounded Approach, Yuyan Annie Pan, Xianbiao Hu, Qing Tang, Yanyan Chen, Xuesong (Simon) Zhou

Civil & Environmental Engineering Faculty Publications

Accurate estimation of traffic state under oversaturated conditions is fundamental to a wide range of transportation applications. While intuitive, volume-to-capacity (q/qc) ratio-based link performance functions (LPF) are challenged by the U-shaped pattern of real-world speed-flow plots, which contradict the monotonic assumptions of link performance models such as the Bureau of Public Roads (BPR) function, particularly when q/q_c ≥ 1. This study addresses this critical gap by proposing an enhanced demand estimation method grounded in the Greenshields model, incorporating a real-time inflow correction factor to more accurately capture traffic demand at bottlenecks. In parallel, a modified LPF is introduced, replacing …

Element-Based Predictive Modeling Of Hydrothermal Liquefaction Bioproducts Derived From Corn Stover, Isamu Umeda, Meicen Liu, Yi Zheng, Jiefu Wang, Zhiwu Wang, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

The hydrothermal liquefaction (HTL) process offers an energetic advantage over pyrolysis because it does not require prior drying of the biomass feedstock. However, there are significant challenges in simultaneously estimating both the yields and characteristics of products from the HTL of biomass with theoretical support. This study developed a unique element-based kinetic model to predict the yields, higher heating values, and fuel characteristics of solid residue and heavy bio-oil, based on the temperature, residence time, solid loading, and elemental composition (C, H, N, and O) of corn stover. Furthermore, the model predicted the weights of dissolved carbon and nitrogen in …

Nonstationary Spatial Correlation Of Earthquake Ground Motions In California, Pengfei Wang, Busra Bocekli, Junhui Yang, Scott J. Brandenberg, Jonathan P. Stewart

Civil & Environmental Engineering Faculty Publications

Assessing seismic risk to spatially distributed infrastructure systems requires realistic representations of spatially correlated ground motions. Existing models for the spatial correlations of ground motions rely on strong second-order stationarity assumptions, under which the correlation structure is assumed to be invariant across space, potentially masking regional variations. Because repeatable site and path effects can vary spatially, the resulting correlation structure is likely to be nonstationary. We propose a nonstationary spatial correlation method that captures geographically varying correlation decay behavior. We compute site-to-site Pearson correlations of within-event residuals using earthquakes recorded at both sites in each site pair and model the …

Age-Related Hearing Loss Involves Mitochondrial Dna Instability And Copy Number Depletion In The Cochlea: Insights From In Vivo And In Vitro Models, Akil Turner, Bo Ding, Xiaoxia Zhu, Tam Nguyen, Parveen Bazard, Robert D. Frisina

Chemical and Biochemical Engineering Faculty Research & Creative Works

Age-related hearing loss (ARHL) is a highly prevalent sensory neurodegenerative disorder that involves various molecular mechanisms. The present study investigated if age-related oxidative stress (OS) induces alterations in mitochondrial DNA (mtDNA) copy number and heteroplasmy, using complementary in vivo and in vitro models. Aged (30-month) CBA/CaJ mice have elevated auditory brainstem response (ABR) thresholds amplitudes vs. young (3-month) CBA/CaJ mice, a key physiological characteristic of ARHL The in vitro experiments employed the strial SV-K1 cochlear cell line treated with hydrogen peroxide (H₂O₂). Both the aged cochleae and H₂O₂-treated cells exhibited a significant …

Orchestrating Bone Healing: Biomaterial-Driven Immunoengineering Of The Bone Microenvironment For Advanced Regenerative Therapies, Ah Joung Rachel Yu, Mohammad Reza Zare, Edikan Ogunnaike, Mostafa Yazdi

Chemical and Biochemical Engineering Faculty Research & Creative Works

Bone regeneration is a multistage and tightly regulated process driven by coordinated interactions between the immune and skeletal systems. While inflammation is essential for initiating repair, its dysregulation contributes to delayed union, nonunion, and impaired healing commonly seen in trauma, chronic disease, and aging. Traditional interventions, including autografts and inert biomaterials, provide structural support but fail to engage or modulate the immune microenvironment that governs successful regeneration. Emerging research in osteoimmunology has revealed the centrality of immune cell populations such as macrophages, T regulatory cells, dendritic cells, and myeloid-derived suppressor cells in directing osteogenesis, angiogenesis, and the resolution of inflammation. …

Proactive Safety Reasoning In Human-Robot Collaboration In Disassembly Through Llm-Augmented Stpa And Fmea, Morteza Jalali Alenjareghi, Fardin Ghorbani, Samira Keivanpour, Yuvin Adnarain Chinniah, Sabrina Jocelyn

Études primaires

Disassembly tasks in human–robot collaboration (HRC) environments present safety challenges due to hazardous materials, control system variability, and physically demanding operator tasks. To address these challenges, we propose an AI-augmented risk assessment framework integrating System-Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). This framework is implemented in four configurations: Term Frequency– Inverse Document Frequency (TF-IDF), Fine-tuned Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and RAG with a structured Knowledge Graph (KG) built from safety standards. The system supports real-time, standards-compliant safety reasoning by generating interpretable, context-specific recommendations. We evaluate these configurations across GPT-3.5 TURBO, GPT-4o, GPT-4.1, and …

Scalar Source Localization Using Multi-Sensor Domains Of Dependence In Turbulent Channel Fow, Zejian You, Xiaowei Zhu, Qi Wang

Mechanical and Materials Engineering Faculty Publications and Presentations

Tracking hazardous events such as wildfire smoke, coastal oil spills, or chemical releases in natural environments is complicated by turbulent dispersion and molecular diffusion. This study presents a method to localize pollutant sources rapidly and accurately using infinite time-averaged measurements from a multi-sensor network, leveraging the duality between mean forward and adjoint scalar fields. The forward-adjoint duality states that measurements at the sensor (forward feld) are equal to the adjoint field at the source, providing crucial spatial information about the source. Consequently, the adjoint scalar fields can be interpreted as the domain of dependence for the sensor observations [1, 2]. …

Electron Beam Irradiation Effects On Bulk Metals: A Comparative Study Of Polycrystalline Versus Single-Crystalline Structures, A. A. Elmustafa, N. A. Sultana, A. H. Al-Allaq, M. Ojha, Y. S. Mohammed, J. Vennekate, H. Baumgart

Mechanical & Aerospace Engineering Faculty Publications

This study investigates the effects of electron beam (e-beam) irradiation on the mechanical and structural properties of eight bulk metallic samples, comprising both polycrystalline (PC) and single-crystalline (SC) forms of Ni, Cr, V, and Ti. These metals were evaluated as potential candidates for beam exit windows in high-power (MW-class) particle accelerators. The primary objective is to identify metals capable of withstanding the conditions of high-power/MW-class e-beam accelerators and serve effectively as exit windows. Selection criteria were based on each metal’s intrinsic properties, power dissipation capability, and irradiation-induced changes in mechanical behavior, including hardness, elastic modulus, and defect density. Comprehensive characterization …

From Comparison To Integration: Building Energy Simulation Tool Variability And The Case For Intelligent Retrofit Workflows, Amir Safari, Dalya Ismael, Mahsa Safari, James Freihaut

Engineering Technology Faculty Publications

As the urgency to address climate change and modernize energy infrastructure grows, the building sector plays a key role in improving energy efficiency and reducing carbon emissions. This study evaluates five energy retrofit strategies for Building 101 at The Navy Yard in Philadelphia, comparing two real-world proposals from energy service companies with three simulation-based packages derived from Building Energy Simulation (BES) tools. The study examined whether advanced BES tools provide greater accuracy and decision-making value compared to simpler alternatives. Electricity savings ranged from 5 % to 40 %, gas savings from 29.7 % to 61 %, and annual cost reductions …

Generative Ai And Llm Applications In Renewable Energy And Smart Grids: A Systematic Review For The Sustainable Energy Transition, Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper, Ibrahim Kucukdemiral, Huseyin Kusetogullari, Murat Kuzlu, Yashar Mousavi, Sonam Norbu, Taha Selim Ustun, David Flynn

Engineering Technology Faculty Publications

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting …

Adaptive Electric Vehicle Routing And Charging With Deep Reinforcement Learning, Mandana Farhang Ghahfarokhi, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

Engineering Management & Systems Engineering Faculty Publications

As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

Evaluating Species At Risk In Data-Limited Fisheries: A Productivity-Susceptibility Analysis For Marine Aquarium Fish, Gabrielle A. Baillargeon, Alice A. Wynn, Jemelyn Grace P. Baldisimo, Michael F. Tlusty, Andrew L. Rhyne

Biological Sciences Faculty Publications

The marine aquarium trade (MAT) is a significant global industry harvesting millions of wild-caught, live coral reef fishes for public and private aquaria markets in the United States and Europe annually, while supporting fisher livelihoods in the Indo-Pacific. This diverse and species-rich trade is considered data-limited, creating barriers to quantifying the current and future socio-ecological sustainability of the fishery. We present a revised and expanded productivity–susceptibility analysis (PSA) that serves as a holistic risk assessment to estimate the vulnerability of marine aquarium fish to overfishing. Our global analysis includes 306 species that are actively in trade. Improvements to the PSA …

Formulation Development Of Topical Inserts Containing Doxycycline And Doxycycline Combined With Tenofovir Alafenamide And Elvitegravir For The Prevention Of Sexually Transmitted Infections, Vivek Agrahari, M. Melissa Peet, Jasmin Monpara, Rijo John, Sriramakamal Jonnalagadda, Pardeep K. Gupta, Meredith R. Clark, Gustavo F. Doncel

CONRAD Publications

Despite advances in oral and injectable HIV prevention options and oral prophylaxis for sexually transmitted infections (STIs) of bacterial origin, there remains a critical need for effective on-demand topical (vaginal and rectal) products for pre- and post-exposure prophylaxis (PrEP and PEP). To fill this gap, we have developed single and first-in-kind multi-active topical inserts for bacterial STIs and HIV/STIs prevention. We have formulated two different inserts, one containing doxycycline (DOX) at 10, 50, and 100 mg doses for bacterial STI prevention, and a multipurpose prevention product (TED insert) that combines DOX (10 mg) with the antiretrovirals tenofovir alafenamide (TAF; 20 …

Sulfhydryl-Functionalized Diarylethenes: Synthesis, Photoswitching, And Fluorescent Properties, Pramod Aryal, Jonathan Bietsch, Gowri Sankar Grandhi, Josh Choi, Guijun Wang

Chemistry & Biochemistry Faculty Publications

Sulfhydryl group (SH) plays important roles in reactions with various functional groups, especially in biologically active systems. Photoswitchable diarylethene (DAE) derivatives have demonstrated applications in many research fields. Among the many classes of diarylethene derivatives, thiol derivatives have not been extensively explored. In this study, we systematically synthesized and characterized a series of bis-thiol functionalized dithienylethene derivatives. The thiol groups in this series are attached directly to the thiophene, or through a phenyl or methylene linker. Each series incorporated both dithienyl cyclopentene and hexafluorocyclopentene bridges and total six thiol substituted diarylethenes were synthesized. Among the six sulfhydryl DAE derivatives, four …

Multidentate Surfactant-Dependent Synthesis Of Giant Iridium Superstructures, Ramjee Balasubramanian, Maeren E. Hill

Chemistry & Biochemistry Faculty Publications

Giant superstructures of iridium, comprised of smaller spherical and slightly larger anisotropic nanoparticles, were synthesized by reducing iridium chloride in the presence of resorcinarene, a class of tetrameric macrocyclic polyphenols, with sodium borohydride in ethanol in 30 min under mild conditions. These superstructures were characterized by a range of techniques including TEM, HRTEM, EDS and other spectroscopic methods. The impact of the macrocyclic surfactant, its features and reaction conditions in dictating the formation of giant iridium superstructures was evaluated. The packing density of nanoparticles in these giant iridium superstructures could be altered qualitatively by varying the duration of the reaction, …

Stress-Strain Behavior Of Saturated Clays From Vane Shear Test Results: A Numerical Approach, Gustavo Rodrigues De Avila Oliveira

Master’s Theses

The vane shear test (VST) is widely used to estimate the undrained shear strength of soft cohesive soils; however, it does not directly provide constitutive information such as the stress–strain response. Smoothed Particle Hydrodynamics (SPH), a mesh-free numerical method, offers an attractive alternative for modeling the large deformations involved in the VST and extracting additional information from the test.

This study develops and calibrates a two-dimensional SPH model to simulate the field vane shear test and investigate the stress–strain behavior of soft soils. The VST was implemented within an SPH framework, while field and laboratory data were used to define …

The Development Of A Convenient And Consistent Methodology For Flight Proficiency To Certify Multi-Rotor Uas Pilots For State Departments Of Transportation, Colin H. Dees, Joseph M. Burgett

The Professional Constructor

To fly an unmanned aircraft system (UAS), commonly referred to as a “drone,” the Federal Aviation Administration (FAA) requires pilots to pass a knowledge test. There is no requirement at the state or federal level for drone operators to demonstrate the ability to operate a UAS. The National Institute of Science and Technology (NIST) has created an exam for public and private entities to assess basic UAS flight proficiency. It is the only nationally recognized flight proficiency protocol. NIST does not provide a scoring recommendation and leaves it to the user to determine the minimum criteria to pass. There is …

Feasibility Of Upcycling Spent Lithium-Ion Battery To Carbon Dioxide Capture Adsorbent, Chimezie Frank Onwudinjo

Master’s Theses

This study investigates the feasibility of repurposing spent lithium-ion battery (SLIB) to lithium orthosilicate (Li₄SiO₄), a high temperature carbon dioxide sorbent. Two synthesis pathways including conventional acid-leaching method (Scenario 1) and a pyrolysis-based route (Scenario 2) were explored. Additionally, techno-economic analysis (TEA) and lifecycle assessment (LCA) of the two processes were also performed. Different analytical characterization techniques were performed to understand the material properties of Li₄SiO₄ including surface area, crystallinity, morphology and thermal stability. CO₂ capture performance of the synthesized Li₄SiO₄ was tested in a thermogravimetric analyzer (TGA) using …

Control System Emendation And Ai-Driven Optimization For Enhancing A Smart Residential Microgrid, Anthony Nyoyoko

Master’s Theses

This thesis began with a simple idea: to restore the control system of a smart residential microgrid to respond intelligently to electricity prices while remaining safe, reliable, and practical on low-cost hardware. At the start, the goal was to design an economically aware microgrid that could look at electricity prices from the PJM market and make better operational decisions than traditional rule-based control. The motivation was straightforward. As residential renewable energy adoption increases, microgrids are expected to do more than just supply power. They are expected to respond to price volatility, integrate renewable generation, and operate reliably using embedded controllers …

Discovering Naturally Occurring Antifreeze Peptides From Microbiome By Integrating Protein Language Models And Molecular Dynamics Simulation, Ibrahim A. Imam, Trevor Morey, Yuexu Jiang, Duolin Wang, Dong Xu, Qing Shao

Chemical and Materials Engineering Faculty Publications

Antifreeze peptides inhibit ice crystal growth and recrystallization, and are promising components of cryoprotective formulations for cell, tissue, and food preservation, as well as anti-icing surface coatings. However, the discovery of new antifreeze peptides has been hindered by their sequence diversity and the limited scalability of experimental screening. In this study, we identify novel antifreeze peptide candidates from a microbiome-derived sequence library using ensemble machine learning and molecular dynamics (MD) simulations. We developed an ensemble classifier composed of 10 adapter-tuned protein-language models and a random forest meta-learner. After training on a curated dataset of 73 766 sequences, we applied this …

Effect Of Protective Mutation On Structure And Dynamics Of Apoe: A Molecular Dynamics Simulation Study, Newton A. Ihoeghian, Usman L. Abass, Ibrahim A. Imam, Qing Shao

Chemical and Materials Engineering Faculty Publications

Apolipoprotein E (APOE) plays a significant role in determining the risk of Alzheimer’s disease (AD). Three mutations—APOE3–R136S, APOE3–V236E, and APOE4–R251G—have been reported to reduce the risk of AD. Unveiling the molecular mechanism behind this reduction could lay a foundation for developing therapeutics for AD. To shed light on this subject, we investigate the mutation-induced variation in structural and dynamic properties of APOE3–R136S, APOE3–V236E, and APOE4–R251G in explicit solvent using molecular dynamics simulations. The APOE2, APOE3, and APOE4 were used as the reference. The analysis unveiled that the three protective mutations may exert protection through different mechanisms. The R215G mutation makes …

Tiny Plastic, Big Trouble: How Polystyrene Nanoparticles Impact Dna-Damage Repair Deficient Cervical Cancer Cells, Jordan D. Berezowitz, Mira C. Fish, Lauren E. Mehanna, Breanna Knicely, Claire E. Rowlands, Eva M. Goellner, Brittany E. Givens

Chemical and Materials Engineering Faculty Publications

Microplastics are becoming increasingly abundant waste products; therefore, the risk of human exposure is also increasing. The cytotoxic consequences of microplastic exposure, particularly in cancer, have yet to be explored. We obtained commercially available polystyrene nanoparticles of uniform size (86.61 ± 6.41 nm) and confirmed the chemical composition and shape using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), respectively. We evaluated colloidal stability over a range of concentrations from 1–1000 µg mL⁻¹ using hydrodynamic diameter and zeta potential, determining that higher concentrations exhibit greater colloidal stability compared to lower concentrations. Specifically, the zeta potential increased from …

The Role Of Laminin Isoforms In Glioblastoma Migration, Aseel Ahmed, Faezeh Ghobadi, Hanna Devillier, Qi Cai

Distinguished Undergraduate Researcher Program

Glioblastoma (GBM) remains the most prevalent and lethal invasive brain tumor, denoted by a median survival of 12-15 months despite standard procedures such as safe surgical resection, radiotherapy, and chemotherapy. This limited effectiveness largely arises from the infiltrative nature of GBM cells, which interact with the brain’s extracellular matrix (ECM) by migrating along blood vessels and axonal pathways. The ECM contains the interstitial matrix (IM) and the
basement membrane (BM) made up of proteins and glycans which interact with the malignant cells. While research has mainly been focused on understanding how GBM cells interact with IM components, our knowledge about …

The Healing Power Of Nanoparticles: Bioengineering Gelatin Films With Lignin-Graft-Plga Nanoparticles For Enhanced Tissue Repair, Marie Howe, Cristina Sabliov, Thanida Chuacharoen, Willyam Nikiema

Distinguished Undergraduate Researcher Program

Fish gelatin, a biocompatible material, can be methacrylated to enable its photo-crosslinking and formation of hydrogels for tissue-repair applications (1). This study investigated the incorporation of lignin-grafted PLGA nanoparticles (LNPs) into 3D-printed fGelMA hydrogels as controlled drug delivery systems. Lignin has UV absorbing properties, which could potentially interfere with photocrosslinking of fGelMA (2). These LNPs possess hydrophobic PLGA core and hydrophilic lignin forming the shell providing ample opportunities for delivery of drugs of different chemistries (3). Fluorescent LNPs (FLNPs) were engineered by covalently bonding a fluorophore to the shell prior to nanoparticle synthesis to allow for fluorophore tracking. The understanding …

Innovative Smart Sensing System For Accurate Monitoring Of Uhpc Setting Time, Ted Atera, Khalilullah Taj, Masoud Pasbani, Yen-Fang Su

Distinguished Undergraduate Researcher Program

Ultra-High-Performance Concrete (UHPC) is an advanced concrete material known for its strength and durability. Its superior mechanical properties make it an ideal material for demanding structural applications such as bridges. Accurate setting time measurements are crucial in helping to optimise the performance of UHPC. All conventional methods and standards for measuring the setting time of concrete rely on using expensive specialised equipment, such as the Vicat apparatus, which assesses penetration depth or resistance. However, these methods may not be well-suited for UHPC due to its rapid surface drying while the interior remains fresh. This prevents free needle penetration and leads …