2025 Graduate Student Research Symposium

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  Sustainable Functionalization of Pyrene: A Green Chemistry Approach for Advanced Materials

  Tofunmi Abegunrin and Aleksandrs Prokofjevs

  Substituted pyrene compounds are versatile building blocks for advanced materials with tunable properties, applicable in organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), precursors to metal-organic frameworks (MOFs) for luminescence sensing, photocatalysis, and electrochemical applications. Direct functionalization of pyrene can suffer from regioselectivity issue while indirect methods offer greater regiochemical control but add complexity. This motivates the development of alternative approaches, such as the DMSO-based strategy explored in our research where it acts a source of the methyl (methylene) sulfonium cation, an intermediate with a positively charged sulfur atom bonded to methyl and methylene groups and facilitates diverse transformations due to sulfur's ability to stabilize adjacent negative charge. Our strategy involves activating DMSO to generate the cation, followed by electrophilic aromatic substitution with pyrene. Subsequent transformations enable the introduction of various functionalities, including aldehydes, sulfoxides, and carboxylic acids, expanding the utility of pyrene derivatives. This study explores a novel DMSO-based methodology for the controlled functionalization of pyrene. DMSO serves as a masked formyl group equivalent, enabling efficient production of mono-, di-, tri-, and tetra-substituted pyrene aldehydes, along with pyrene sulfoxides and sulfides, thus broadening their application in advanced material development.

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  Peanut Protein Hydrolysate: Its ACE-Inhibitory Activity and Secondary Structure as Affected by Simulated Gastrointestinal Digestion

  Seyi Adebayo

  Hypertension is a global health concern, and food-derived bioactive peptides are being explored as safer alternatives to synthetic antihypertensive drugs. In our previous study, peanut protein hydrolysate (PPH), produced via Alcalase© hydrolysis, showed significant ACE- and renin-inhibitory activities, suggesting potential antihypertensive benefits. However, its bioactivity may be influenced by digestion. This study investigates the effects of simulated gastrointestinal digestion on PPH’s ACE-inhibitory activity and secondary structure. PPH was digested using pepsin and pancreatin separately or sequentially at varying enzyme concentrations and digestion times. The antihypertensive potential of the digests was evaluated by ACE-inhibitory activity, and structural changes were characterized by FTIR. Results indicated that longer digestion times and higher enzyme concentrations enhanced ACE inhibition, suggesting the formation of smaller bioactive peptides. SDS-PAGE revealed protein degradation and the presence of peptides resistant to digestion. FTIR spectra revealed significant changes in PPH’s secondary structure post-digestion, which may also contribute to enhanced bioactivity. These findings suggest that gastrointestinal digestion enhances PPH’s bioavailability and antihypertensive potential, highlighting the role of structural changes in bioactivity improvement. Thus, PPH may serve as a promising and safer alternative to conventional drugs for hypertension management.

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  Advances in Plastics Upcycling: Catalytic Pyrolysis of Polypropylene Using Co- and Zn- Impregnated H - Mordernite to yield Value Added Chemicals

  Timilehin Gloria Adedej

  Polypropylene (PP) is one of the major plastic waste contributors produced globally for its wide range of applications, high chemical resistance and mechanical strength. In this study, cobalt (Co) and zinc (Zn) impregnated H-mordenite (HM) zeolite-supported catalysts were used for pyrolysis of polypropylene to produce lighter hydrocarbons and hydrogen. This study explores the catalytic pyrolysis of PP over Co- and Zn-impregnated Hmordenite (HM) zeolite catalysts to produce valuable light hydrocarbons and hydrogen. The catalysts were characterized by N₂ adsorptiondesorption, NH₃-TPD, H₂-TPR, XRD, XPS, and TGA to investigate their structural and chemical characteristics. Pyrolysis experiments were conducted at various temperatures (up to 450°C) and catalyst-polymer weight ratios (2:1 and 1:1), and gas products were characterized by GCMS. Results showed that Co-HM exhibited >90% PP conversion and 65% selectivity towards light olefins at 450°C, qualifying it for petrochemical application. Zn-HM achieved 90.51% conversion with >35% paraffin selectivity, leaning towards fuel production. The findings confirm that catalyst design is significant in hydrocarbon selectivity, offering a sustainable pathway for plastic waste valorization and complementing clean energy and circular economy initiatives.

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  Monitoring Soil Moisture Dynamics in a Cornfield

  Anuoluwapo Adelabu

  Soil moisture plays a crucial role in hydrological processes such as infiltration and runoff, influencing water uptake by plant roots, subsurface drainage, and chemical leaching into groundwater. Understanding the spatio-temporal dynamics of soil moisture is essential for optimizing water resources in crop production and minimizing environmental pollution. This study utilizes a capacitance probe soil moisture sensor to monitor these dynamics in a cornfield at North Carolina A&T State University. Continuous monitoring throughout the season will provide data for scientific recommendations aiding farmers in making precise irrigation decisions, which is important, particularly to North Carolina's increasing droughts and rainfall variability. Preliminary results show significant fluctuations in soil moisture at shallow depths (0.10 m), with sharp increases after irrigation and rainfall, followed by rapid losses through evaporation and plant uptake. At 0.30 m, moisture content was stable during the growing season but decreased during prolonged drought, acting as a buffer against crop water stress. Moisture at 0.56 m remained constant, critical for long-term soil moisture retention and groundwater preservation during droughts. Overall, this study highlights the importance of soil moisture monitoring for better irrigation practices and sustainable water management in agriculture, especially under climate variability.

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  Mouthing Behavior Patterns Among Young Children Aged 6 Months to 72 months: A Micro-Activity Analysis in Three States

  Foluke Adelabu, Keziah Floyd, Daylan Dawkins, and Isaiah Brathwaite

  Children's mouthing behaviors with various indoor surfaces provide primary pathways for ingestion exposure to environmental contaminants. This microactivity study examined 67 children aged 6 months to 72 months across three U.S. states (North Carolina, Florida, and Arizona) using improved videotaping and video-translation methods to collect and process video data. Analysis of 3- 4 hours of indoor activity per child revealed distinct mouthing patterns across age groups. Mouth contacts were predominantly characterized by "Nothing" behaviors (71-83% duration, 40-54 contacts/hour across age groups), with "Food-Cont" most prevalent in 6-12 month infants (14% duration, 17 contacts/hour) declining to minimal levels in 36-72 months (1% duration, 2 contacts/hour). Hand-to-mouth contacts increased with age, peaking at 36-72 months (7% duration, 18 contacts/hour). "Porous plastic toy" mouthing decreased with age (from 4% to 1% duration). Location analysis showed shifts from living room-focused mouthing in younger children (52 contacts/hour for 6- 12 months) to bedroom environments in older groups (25 contacts/hour for 36- 72 months). These findings provide crucial quantitative data for understanding children's mouthing exposure patterns in indoor environments and can inform risk assessment strategies for ingestion exposures to chemicals in children's products and environmental contaminants.

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  Enhanced Heat Transfer in Rayleigh-Bénard Convection via Hexagonal Honeycomb Prism Mixer: A Numerical Study

  Abraham Adu-Mills

  Rayleigh-Benard convection, which consists of a scenario where fluid is heated from below, is of great importance in industrial applications such as food processing and nuclear plant operations. In this study, we propose a passive method of heat transfer enhancement in a rectangular Rayleigh-Benard convection cell by inserting a hexagonal honeycomb prism mixer. This mixer is to be used to protect the coherent structures (thermal plumes) that arise from the heated bottom wall so that they can reach the top cold wall without energy dissipation, thereby enhancing heat transfer. First, the model is verified against an experiment with an aspect ratio of 1.72, and the results of Nusselt vs Rayleigh Numbers were observed to be within 3% of each other. We then proceeded to compare the Nusselt number ratios (Nu/Nu0) of various prism heights and it was found that the height of 0.8H, where H is the height of the cell, yielded the greatest heat transfer ratio (1.11). An enhancement was recognized in several cases and the research seeks to study the effect of different Prandtl numbers in order to develop a heat transfer correlation of the form Nu=f(Ra,Pr).

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  Cell-Free Engineering of Synthetic Phages to Overcome Pseudomonas Aeruginosa Resistance

  Akinwunmi Afuape

  Multidrug-resistant Pseudomonas aeruginosa is a major global health threat, demanding innovative therapeutic solutions beyond traditional antibiotics. Bacteriophages, viruses that infect bacteria, offer a promising alternative. Traditional phage engineering approaches rely on bacterial hosts for genome replication, mutation screening, and functional validation, leading to longer experimental timelines, unpredictable recombination events, and host dependent constraints. To overcome these limitations, we present a novel approach to rationally design and construct synthetic phages with reduced genomes to evade bacterial defense mechanisms. Starting from a de novo synthesized wild-type phage genome, we systematically eliminate nonessential regions while integrating targeted mutations to bypass bacterial resistance, including CRISPR-Cas and restriction-modification barriers. Unlike conventional approaches, our method employs a cell-free transcription translation (TXTL) system for rapid genome prototyping, precise modifications, and predictable engineering. This approach eliminates the host constraints by in vitro phage genome assembly and functional validation, facilitating iterative design and precise genetic modifications before host introduction. Our method improves precision, efficiency, and reproducibility in phage engineering by overcoming bacterial propagation constraints and establishing a next generation framework for targeted phage therapies.

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  Evaluating Trojan Attack Vulnerabilities in Autonomous Landing Systems for Urban Air Mobility

  Reza Ahmari

  This study examines vulnerabilities in the autonomous landing systems of Urban Air Mobility (UAM) vehicles, with a particular focus on Trojan attacks targeting Convolutional Neural Networks (CNNs) used for navigation. Trojan attacks introduce covert triggers into CNN models, leading to misclassifications under specific conditions while maintaining normal functionality in typical scenarios. Our research specifically investigates the susceptibility of Autonomous Aerial Vehicles (AAVs) employing the widely adopted DroNet model for real-time obstacle avoidance. To assess these vulnerabilities, we curated a custom dataset consisting of over 5,000 images of landing pads, categorized under both normal and Trojan-triggered conditions. Our analysis revealed a significant drop in model accuracy— from 96.4% under clean data conditions to 73.3% when Trojan triggers were present. This substantial decline highlights the severe impact of Trojan attacks, particularly during critical landing operations. These findings emphasize the urgent need for robust defense mechanisms to mitigate Trojan-induced threats within UAM systems, which play a crucial role in navigation, obstacle avoidance, and secure communication with ground control. As UAM technologies become increasingly integral to transportation and emergency response frameworks, ensuring their cybersecurity is imperative to protecting both lives and infrastructure.

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  Applying Cognitive Load Analysis and Physiological Signal Integration to Operator Safety in Human Robot Collaboration

  Clement Alabi and Sun Y Ph.D.

  As cyber-physical systems continue to proliferate in Industry 4.0, ensuring operator safety in human-robot collaboration (HRC) has become increasingly critical. Collaborative industrial settings demand a thorough understanding of how operators and robots interact under varying cognitive demands. This study explores the dynamic relationships among EEG, GSR, and ECG signals during collaborative robotics tasks using a robust multimodal approach. By employing analytical techniques such as phase space plots, canonical correlation analysis (CCA), time series analysis and mini-batch K-Means clustering, the research reveals insights into workload transitions and cognitive stress in a typical industrial setting. These findings underscore the importance of integrating physiological signals to provide a comprehensive view of operator responses, enabling the development of adaptive systems that enhance safety and efficiency in real time within HRC environments

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  One Health, One Planet: Optimizing Dairy Cow Metabolism with Natural Additives for a Sustainable Future

  Joel Alabi

  This study investigated the combined effects of unique blends of essential oil (EOBs) and fumaric acid (FA) on ruminal fermentation and metabolites in dairy cows, using the rumen simulation technique system. Three rumen-cannulated, non-lactating Holstein Friesian cows served as inoculum donors. The total mixed ration (TMR) included corn silage, alfalfa hay, and a concentrate mix in a 3:1:1 ratio. The four treatments were: Control (TMR without additives), EFA1 (TMR + EOB1 + FA), EFA2 (TMR + EOB2 + FA), and EFA3 (TMR + EOB3 + FA). Sixteen fermentation chambers, with four replicates per treatment, were arranged in a completely randomized design over a 9-day period. EOBs and FA were added at 10 μL/g feed and 3% of TMR, respectively. Results showed that EFA1 reduced methane (CH4) emissions by 60.2% (p = 0.0351) without negatively affecting nutrient digestibility. A total of 661 metabolites were identified, with significant changes in 16 metabolites for EFA1, 33 for EFA2, and 19 for EFA3. PLS-DA analysis showed clear separation of treatments, indicating modifications in the rumen fluid metabolome. Metabolic pathways for tyrosine, pyrimidine, purine, lipoic acid, bile acid, and porphyrin metabolism were influenced by the additives. Overall, EFA1 was the most effective in reducing CH4 emissions and optimizing ruminal fermentation, making it a promising nutritional intervention for dairy cows.

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  Investigation of Melt Rheology for Direct Powder Extrusion (DPE) 3D Printing

  Asmaa Alawbali and Lynn Ogot

  Additive Manufacturing (also known as 3D printing) is a versatile technology that can aid in developing personalized medicine while maintaining cost and time efficiency. Currently, fused deposition modeling (FDM) is one of the most common 3D printing techniques used in the production of pharmaceutical drugs. However, FDM faces a few limitations, such as a two-step printing process and exposing material to multiple thermal processing steps, making it disadvantageous for clinical applications. Direct Powder Extrusion (DPE) is a recently developed 3D printing technology that is gaining popularity in the pharmaceutical research industry for its single-step printing process, making it ideal for clinical applications. The present study aims to investigate melt rheology of formulations prepared for Direct Powder Extrusion to develop a pH-dependent drug matrix using Eudragit L100-55 as the base polymer. However, processing Eudragit L100-55 for extrusion application is proving to be a challenge. Thus, by assessing the melt rheology of Eudragit L100-55 based formulations, the printability of the formulation can be evaluated in advance. The Anton Paar MCR 302 Rheometer was used for the rheological investigation. The amplitude sweep test, frequency sweep test, and temperature oscillation ramp test were conducted to investigate formulation rheology at a temperature range of 120-160°C. The findings conclude that investigation of melt rheology is crucial to evaluate formulation printability.

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  Biomimetics of Pro-angiogenic Endothelial Dysregulations Enabled by Segregated Tumor Endothelial Co-culture for On-chip Drug Testing

  Reem Ali, Simrit Safarulla, Vikram Surendran, and Arvind Chandrasekaran

  Tumor Endothelial Co-culture for On-chip Drug Testing Reem Ali, Simrit Safarulla, Vikram Surendran, Arvind Chandrasekaran CBBE, COE, NCA&T Tumor angiogenesis plays a crucial role in cancer progression. The tumor vasculature interface exhibits aggressive disruption of the endothelial lining of blood vessels, mediated by the release of pro-angiogenic factors, by the tumor. Endothelial disruption allows the blood vessels to become permeable, enabling angiogenesis towards the tumor and ultimately, circulation of tumor cells into the bloodstream. Angiogenic sprouting is characterized by disruption of the endothelial adherens junction, adoption of tip-cell phenotype, and sprouting. Our previous work has successfully shown endothelial disruption by tumor stimulation through an On-Chip culture. Given the nature of that particular culture, angiogenic sprouting is difficult to image due to the orientation of cells. We have developed a new segregated co- culture platform that enables the examination of individual cell populations through an on- demand ECM introduction. When segregating the cell types, and not giving them a medium to migrate within, we impede chemotaxis while still allowing for interaction. This allows for the facilitation of cell dynamics and enables observation of cell responses based solely on signaling. Endothelial disruption occurs without the ECM, but the effect on sprouting is unknown. We seek to understand the mechanism behind angiogenic dynamics and explore the role of the ECM in angiogenic sprouting.

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  Liver Cell Culture on ITO-SAMs and Metabolite Analysis using NMR for Drug Toxicity Applications

  Oreoluwa Alonge, Bo Wang, Oluseyi Valerie Ochima, and Kammira Pearson

  Despite the emergence of three-dimensional (3D) cell culture systems, two dimensional (2D) cultures remain prevalent due to their cost-effectiveness, reproducibility, and ease of analysis. This study aims to develop a novel self-assembled monolayer (SAM)-based cell culture platform for growth of hepatocytes and to explore advanced metabolomic analysis techniques for enhanced understanding of hepatotoxicity. Indium-Tin Oxide (ITO) functionalized with 3-aminopropyl (triethoxysilane) (APTES) is hypothesized to serve as an optimal scaffold for drug toxicity screening and as a model for Micro Engineered Organs (MOE). Surface characterization was performed using Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS). HepG2 liver cells were cultured on ITO modified with APTES. Cell proliferation and cytotoxicity were assessed using MTT and Live/Dead assays. The scaffold of ITO modified with APTES provides an optimal SAM surface for HepG2 cell adhesion and proliferation as evidenced by confocal microscopy images. Preliminary metabolomic analysis through Nuclear Magnetic Resonance (NMR) spectroscopy offers detailed insights into intracellular responses and metabolites normally undetectable by regular CMP (comprehensive metabolomic pathway) tests. This study lays the groundwork for the development of more physiologically relevant in vitro hepatocyte models, potentially enhancing the accuracy of drug toxicity screening.

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  A Novel Organic-Inorganic Hybrid Polymerization Strategy for Titanium Oxide/Polymer Nanocomposites with Supercapacitive Properties

  Shanna Marie Alonzo, Moses Ashie, Gayani Pathiraja, and Bishnu Bastakoti

  A titanium oxide/carbon-based nanocomposite was synthesized via a unique organic- inorganic hybrid polymerization process. Radical polymerization of acrylonitrile and condensation polymerization of titanium isopropoxide were balanced in an acid-assisted, one- pot synthesis. The resulting hybrid polymer exhibited intermediary properties of its organic and inorganic components, indicating a successful harmonization of the competing organic and inorganic polymerizations. It is suggested that the acid-protonated nitriles from the organic polymer were the sites for hybrid polymerization with the inorganic polymer. In the proposed mechanism, some of the acetone (medium for the radical organic polymerization) might have undergone acid-catalyzed aldol condensation and water elimination during synthesis. When annealed at 900°C, the hybrid polymer transformed into a polycrystalline graphitic carbon/titanium oxycarbonitride nanocomposite with supercapacitive properties. It demonstrated satisfactory electrochemical performance, and an excellent cycling stability with no loss iΩgood specific capacitance of 55.5 F/g at a current density of 1 A/g, low charge transfer resistance of 0.23 capacitance even after 10,000 cycles. A fabricated symmetric device showed fair performance with a low charge-transfer resistance of 0.44 Ω, specific capacitance of 25.5 F/g at 1 mV/s, energy density of 1Wh/kg, and power density of 900 W/kg, highlighting its potential for energy storage applications.

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  Damage Characterization of Thermoset Composite Using Acoustic Emission and Deep Learning

  Richard Amevorku, David Amoateng-Mensah, Manoj Rijal, and Tanzila Minhaj

  The vast application of carbon fiber-reinforced polymers (CFRPs) in automotive, aerospace, and construction industries is advancing due to their excellent load-bearing capabilities, and durability. These incredible mechanical properties call for a thorough study of the flaws in CFRPs. Under quasi-static tensile loading until failure, thermoset CFRPs exhibit three primary failure modes-fiber breakage, matrix cracking, and delamination. Real-time transient waves emitted by the failure events are acquired as acoustic emission (AE) signals for analysis. Due to the numerous AE signals generated during experiments, effective classification of the failure modes through manual inspection of the waveforms has been challenging. Therefore, deep learning models were developed to study and classify the failure mechanisms efficiently and accurately. One-dimensional (1-D) and two-dimensional (2-D) convolutional neural network (CNN) models were trained using the signal amplitudes and spectrogram images, respectively, as training data. A thorough data processing was done to remove any outliers that may impair the performance of the models. The performances of the two approaches were compared using model evaluation metrics. Although the 1-D CNN model yielded an accuracy of 97%, the 2-D CNN model outperformed the 1-D CNN model with a flawless accuracy of 100%. The 2-D CNN model exhibited a perfect discriminative capability as depicted in the confusion matrix

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  Electrode Modification of a Microalgal Microbial Fuel Cell for Valorizing Agricultural Wastewater

  Dorcas Amoh

  Agricultural activities, though essential, generate significant amounts of wastewater, which poses environmental challenges. Agricultural wastewater comprises various pollutants, including organic matter, nutrients, pesticides, and pathogens. The significance of effective wastewater treatment lies in its ability to remove contaminants, protect ecosystems, and potentially recover valuable resources. Biological methods like anaerobic digestion (AD), microalgal cultivation, and microbial fuel cells (MFCs) are sustainable approaches to treat wastewater. However, biogas productivity is sensitive to operating conditions and requires long retention times in AD. Microalgal cultivation is limited by light penetration and harvesting costs, while MFCs face challenges in scaling up and maintaining long-term stability. The integration of microalgae cultivation with MFCs has become promising since microalgae act as an oxygen generator to facilitate reactions in the cathode chamber, efficiently removing phosphorus, nitrogen, and CO2. This study aims to cultivate and characterize anaerobic bacterial biofilm and a microalgal biofilm on the anode and cathode of MFCs. The effects of key parameters, including electrode surface modification, hydraulic retention time, pH, temperature, light intensity, and photoperiod, on biofilm growth, microbial diversity, and proteomic profiles are thoroughly evaluated, thus contributing to the development of sustainable wastewater management strategies

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  Safety Improvement of Grape Pomace by Pressure Cooking and Fermentation with Different Strains of Lactobacillus

  Ebenezer Aning-Dei and Salam Ibrahim

  Grape pomace (GP) is a polyphenol and dietary fiber-rich byproduct of grapes from winemaking and has great potential to serve as food and feed ingredients. However, limited studies have found that GP is often contaminated with high levels of Ochratoxin A (OTA), which has been reported to cause nephrotoxicity and other diseases in various animal species. In this study, we examined the impacts of pressure-cooking at different pH and fermentation with five lactobacillus strains (L. paracasei, L. acidophilus, L. plantarum, lactiplantibacillus plantarum and Lactobacillus bulgaricus (LB6)) on OTA and different classes of polyphenols in the GP. The GP was deseeded and adjusted to pH 5-7 with NaHCO₃, spiked to OTA of 50ng/g GP, and then pressure cooked for 15-60 min. The GP obtained at optimal pressure- cooking condition was fermented anaerobically at 42 °C for 4-144 hours. The untreated GP served as control. Pressure-cooking only decreased the OTA content by 18- 36% (P<0.05), while TP concentrations increased quadratically (R²=0.9642-0.9145, P<0.05). TA concentration decreased exponentially with increasing treatment times (R²=0.995). In the pH range OF 5-7, pressure – cooking achieved 40-63% of OTA reductions (p<0.05). OTA levels were further reduced by fermentation and a total OTA reduction of 98% was achieved.

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  Investigating the capability YOLOv8 object detection framework employing deep learning and machine learning algorithms, to accurately identify objects

  Ikenna Anyanwu, Aysia Hammond, KaMari Smith, and Jalia Brown

  The rapid advancements in artificial intelligence (AI), coupled with the proliferation of the Internet and the Internet of Things (IoT), are transforming the application of AI technologies, particularly in object detection. This research explores the feasibility of using a standard laptop webcam as an input source for real-time object detection. Specifically, it investigates the capability of You Only Look Once version 8 (YOLOv8), a state-of-the-art object detection framework employing deep learning and machine learning algorithms, to accurately identify objects (e.g., weapons) from webcam input. This work establishes a baseline model for object detection using deep convolutional neural networks, aiming to inform future research, development, and training, especially for users with limited technical expertise.

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  Improving OER Performance of NiRu Layered Double Hydroxide: Including Influence of Reaction Time on Morphology and Electrochemical Activity

  Eden Argaw and Bishnu Bastakoti Ph.D.

  In this study, NiRu layered double hydroxide (LDH) was synthesized using the Precipitation method for oxygen evolution reaction (OER). The synthesis was carried out at a constant temperature of 80°C, while the reaction time was varied (2 hours, 3 hours, and 4 hours) to investigate its effect on the catalyst's morphology and electrochemical performance. Scanning electron microscopy (SEM) analysis confirmed a well-defined morphology, with improvements observed as the reaction time increased. Electrochemical evaluation revealed that the over potential values for the samples synthesized at 2, 3, and 4 hours were 294 mV, 144 mV, and 144 mV, respectively. Compared to previously reported Ni-based LDH catalysts, the NiRu-LDH synthesized in this study exhibits superior performance, with a significantly lower overpotential and higher current density, making it a strong candidate for practical OER applications. Additionally, the current density of the catalyst exhibited an increasing trend with longer reaction times, indicating enhanced catalytic activity. Characterization techniques conducted in this study , including X-ray diffraction (XRD),Fourier-transform infrared spectroscopy (FTIR), cyclic voltammetry (CV), linear sweep voltammetry (LSV), and SEM and the findings highlight the impact of reaction time on the performance of NiRu LDH and its potential as an efficient catalyst for OER applications.

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  Heat Transfer Enhancement Using Triply Periodic Minimal Surface Inserts in A Square Duct

  Kofi Asante and Abraham Adu-Mills

  This study explores the thermal performance of three Triply Periodic Minimal Surfaces (TPMS) within a square duct: Gyroid, Helix Spiral, and Twisted Wave. The duct features a constant heat flux applied to opposite sides, with one end insulated, and fluid flows through the longitudinal section at a Reynolds number (Re) of 845,000. The primary focus is on evaluating the heat transfer enhancement and thermal performance factor (TPF). The heat acquired by the fluid (Q ratio) and the TPF were measured to assess the effectiveness of each surface in improving thermal performance. Results indicate that the Helix Spiral (TPF:0.77, Q ratio: 5.76) configuration achieved the highest heat transfer enhancement, followed by Gyroid (TPF: 0.75, Q ratio: 4.59) and Twisted Wave (TPF: 0.72, Q ratio: 3.9) . These findings highlight the potential of TPMS structures in optimizing thermal management systems, with the Helix Spiral emerging as the most effective design for heat transfer applications. This research provides valuable insights into the thermal performance of various TPMS geometries, offering a foundation for future advancements in heat exchanger design.

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  Incorporating Ru into TiO2 to Enhance Hydrogen Generation in Visible Light

  Moses Ashie and Bishnu Prasad Bastakoti

  The significant role that fossil fuels have played in energy utilization cannot be underestimated. However, owing to the non-renewable and CO2 emission associated to its usage has paved a way for a search for a more renewable and environmentally unfriendly energy sources of which hydrogen energy identified as a potential target. A highly porous TiO2-RuO2 heterogenous solvothermally engineered photocatalyst revealed how varying synthesis conditions can contribute to the modification of TiO2 towards effective photocatalytic water splitting in the visible region of the electromagnetic spectrum. Characterization techniques such as XRD, SEM, TEM, UV-Vis DRS, and electrochemical analysis revealed that TiO2-RuO2-20 exhibited reduced band gap, improved light absorption capability, lower electron-hole recombination rate, lower solution resistance which collectively contributed to effective photocatalytic activity. In addition, a high surface area and molg-1h-1 of hydrogen gas mesoporous nature contributed to 1794.8 molg-1h-1) and the commercially Compared to the pristine RuO2 (21.9 molg-1h-1), the TiO2-RuO2-20 sample produced available TiO2 (246.4 yield that is almost 81 times and 7 times respectively. This therefore proves the effectiveness of the solvothermal method and the ruthenium dioxide in modulating the photocatalytic properties of TiO2 photocatalyst for photocatalytic water splitting in visible light.

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  Mechanochemistry of Cu-Ni Alloy Nanocatalysts for Production of Hydrogen

  Dominic Awuzah

  A mechanochemical alloying (MCA) method was used to produce a 2:1wt% Cu2-Ni1 alloy nanoparticles. Mechanochemical synthesis of catalysts is an innovative method that relies on mechanical force to induce solid-state chemical reactions, typically by grinding/milling either metallic oxides or pure metals. Decreasing the internuclear distance (via alloy formation) between Ni and Cu atoms is expected to promote a synergistic catalytic activity and improved physiochemical properties. The stability and activity of unsupported alloyed, unsupported unalloyed, and supported alloyed catalysts were studied for their hydrogen fuel production capability via steam reforming of methanol (SRM)

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  Neutrophil Extracellular Traps Sensing Activates Directional Migration of Brain Metastatic Breast Tumors

  Rita Banga

  Brain metastases (BrM) in breast cancer are associated with poor survival, and neutrophil extracellular traps (NETs) have emerged as key drivers of tumor migration and invasion. This study explores how BrM cells sense and respond to NETs via CCDC25, a membrane-localized extracellular DNA sensor. Using a polyacrylamide-based hydrogel co-culture system, we spatially segregated tumor cells and neutrophils while preserving their biochemical interactions. Our findings reveal that CCDC25 expression is significantly upregulated in BrM cells in response to NETs, leading to enhanced tumor invasion. Notably, tumor migration was directly proportional to NET density, with cells aligning toward NET-rich regions, particularly when collagen was introduced to facilitate migration. Additionally, BrM tumors appear to reprogram neutrophils to generate NETs in the surrounding stroma, reinforcing a pro-metastatic microenvironment. The study suggests that NET-stroma interactions serve as key regulators of BrM tumor dynamics, promoting directional migration and invasion. These insights provide a mechanistic understanding of how NET sensing facilitates BrM progression, highlighting CCDC25-NET interactions as potential therapeutic targets for metastatic breast cancer

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  Human Robot Interaction through Augmented Reality: Bridging Efficiency and Safety

  Brooks Berdeen

  Throughout history, the relationship between humans and machines has been entwined, allowing humanity to prosper. Technology is constantly and drastically evolving, making it more important than ever to consider the human within the system. A human operator is still an invaluable asset within the industrial and militaristic settings due to their flexibility. As the field of robotics undergoes regular advancements, it is imperative to develop new technologies with the human part of the system given proper consideration. In systems engineering, finding the weak point is always the priority. We make the case that focusing on the human in the system will keep the human from becoming the weak point but also allows for the robotic technology in development to continue to prosper. We propose the implementation of augmented reality to bring both human and machine closer, raising efficiency and safety within the system.

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  A Metagenomic Investigation of Temporal Changes in Wastewater Microbiome across North Carolina A&T State University Campus

  Shilpi Bhatia and Dongyang Deng Ph.D.

  Wastewater microbial communities are highly dynamic, exhibiting significant variation over time. We employed Next-Generation Sequencing to identify various SARS-CoV-2 variants of concern, followed by a shotgun metagenomic approach to examine the relative abundance of pathogens in wastewater associated with SARS-CoV-2 across the North Carolina A&T State University campus. Biweekly grab samples were collected across the NCA&T State University campus between January 2021 and December 2023. RT-qPCR results revealed higher concentrations of SARS-CoV-2 RNA in wastewater samples from 2021-2022 compared to 2023. Targeted sequencing detected all major variants of concern, including Alpha, Delta, and Omicron, in 2021-2022 samples. Additionally, a shotgun metagenomic approach was used to investigate changes in wastewater microbial communities from a COVID-19 quarantine dormitory (Haley Hall) on campus, with samples collected each semester over a three-year period (2021-2023). Results showed a higher prevalence of healthy gut bacteria during the Spring semesters of 2021-2023, while pathogenic bacterial taxa were more prominent in Fall 2023, highlighting the impact of pathogens on the sewage microbiome. However, no significant temporal variation in the overall prokaryotic community composition was observed between or within Spring and Fall semesters from 2021 to 2023 using alpha and beta diversity indices (p >0.05). These findings underscore the dynamic nature of the wastewater microbiome