Photoelectrochemical (PEC) systems can be used to harness solar energy to drive sustainable oxidations reactions, such as those mediated by TEMPO ( 2,2,6,6-tetrameth-ylpiperidinyl-N-oxyl), a stable radical with applications in organic synthesis. This work focuses on preparing bismuth vanadate (BiVO4) films for multilayer electrodes (FTO|WO3-BiVO4-NiO) to enable PEC TEMPO oxidation studies. Double-layered BiVO4 films were fabricated on fluorine-doped tin oxide (FTO) substrates through dip-coating and a subsequent thermal treatment at 450°C. Various means of optimizing film performance and quality were explored, including precursor stoichiometry, dipping frequency, and drying conditions.

Our experiments demonstrate that the uniformity and quality of BiVO4 firms are greatly dependent on preparation parameters. Adjustments to the drying procedure, designed to slow solvent evaporation, resulted in improved uniformity as observed through UV-Vis spectroscopy and profilometry. Photoelectrochemical testing of select replicates under illumination confirmed photoactivity, with distinct differences between dark and light conditions. Further experimentation with cyclic voltammetry and chronoamperometry will explore the efficiency of these films in greater detail. This work establishes an effective approach for BiVO4 film preparation for future use in WO3-BiVO4-NiO multilayer electrodes for TEMPO oxidations studies and advancing solar-driven oxidation processes.

The goal of this project is to select the variants of an archaea leucyl-tRNA synthetase (MLRS) to incorporate N-𝜀-acetyl lysine (AcLys) into specific positions of proteins in bacterial cells. Acetylation of lysine is one of the most important post-translational modifications of proteins that regulate their functions. One application of this study is using site-directed incorporation of AcLys to introduce novel functions to proteins. Previously, we successfully randomized five positions in the MLRS active site to generate millions of different variants. Genetic screening procedures were performed to select MLRS variants specific for AcLys. Positive selection is performed in the presence of AcLys where bacterial cells containing MLRS that attach any natural amino acids or AcLys onto the tRNA can survive in the presence of chloramphenicol antibiotics. In the negative selection performed in the absence of AcLys, bacterial cells containing MLRS that attach natural amino acids will die in the presence of 5-FU as a toxic substance is produced. Only cells containing MLRS variants that attach AcLys can survive in the presence of 5-FU, because no toxic substance is produced. Two clones made it through multiple rounds of selection and are being tested for successful incorporation of AcLys at the 7th position of the Z-domain protein. Mass spectrometry will be used to detect the presence of AcLys.

Impact of Sensor Design on Hydrogel-Porous Silicon Structures Capable of Detecting Ion Concentrations in Human Sweat

Dylan Walters1, George Weimer1, Leigh T. Canham,2 and Jeffery L Coffer1

1Department of Chemistry and Biochemistry, Texas Christian University, Fort Worth, TX 76129
2Nanoscale Physics, Chemistry and Engineering Research Laboratory, University of Birmingham, Birmingham, B15 2TT UK

Utilizing the supportive structure of hydrogels, the semiconducting character of porous silicon (pSi) membranes, and the biodegradability of both, a unique biosensor for the chemical analysis of health-relevant analytes can ideally be created.
Hydrogels are water-infused, biodegradable polymer networks. Alginate based hydrogels are particularly useful because of environmental abundance, along with their ability to interface well with human skin. The addition of acrylamide segments to the polymer chains adds stability and useful shelf-life to the material. These characteristics also make them an ideal medium for supporting pSi membranes and simultaneously assimilating them into a wide range of tissues.
Porous silicon (pSi), a highly porous form of the elemental semiconductor, is utilized to measure and conduct electrical signals throughout the hydrogel matrix. In diode form, these membranes exhibit measurable current values as a function of voltage, which can be used to detect bioelectrical stimuli such as the concentration of physiologically relevant ionic species (e.g. Na+, K+, and Ca2+).
Recent experiments center on integrating pSi membranes in Acrylamide/alginate co-polymer hydrogels to test how variations in ion concentration affect the flow of current as a function of applied voltage. pSi membranes ~110 m thick and 79% porosity are fabricated from the anodization of low resistivity (100) Si in methanolic HF at an applied bias of 100 mA/cm2 for 30 min. Membrane pieces ~ 2 mm by 2 mm are heated for one hour at 650°C. They are then fashioned into diodes upon the attachment of Cu wire using Ag epoxy and annealed for 15 minutes at 95°C. The backs of the membranes, the connection to the copper wire, and the copper wire itself are sealed using clear nail polish to prevent current flow from the back of the membranes and bubble formation. In each ion sensing experiment, an electrochemical cell is created by placing two pSi membranes parallel each other ~2 mm apart vertically in a fixed electrolyte composition. Current is measured as a function of applied voltage (typically from 0-5 V) for systems with different NaCl concentrations in the nM to mM range. NaCl solutions are injected directly into the hydrogel in between the two pSi membranes 2 µL at a time. At local concentrations of approximately 0.25M, the magnitude of maximum current response increases with increased volume of ion solution added.
This presentation will focus on the porous silicon hydrogel fabrication protocol, as well as results from experiments with varying NaCl concentrations. Future work is being designed to determine the saturation behavior and the ion concentration limits of the pSi membranes in hydrogels.

Fluorescent small molecule environment-sensitive probes change their emission properties (including emission wavelength, intensity or lifetime) in response to the changes of the environment around them, such as changes in temperature, viscosity, and polarity. Thus, these probes have found numerous applications in sensing and imaging, especially in biologically relevant systems. Ratiometic probes is a special group of molecules that has two or more emission wavelengths that exhibit a relative change in response to changes in the media, which provides an internal calibration, increases signal-to-noise ration, and improves the integrity of sensing. However, synthesis of such molecules is usually non-modular in nature, and it often requires multiple steps coupled with numerous purifications. In this presentation, we will highlight our synthetic efforts on the developments of several types of fluorescence ratiometric probes that are based on versatile fluorescence scaffolds, such as BODIPY and squaraine dyes.

Historically, drug design has focused on small molecule drugs, but macrocycles show potential to interact with targets that small molecules cannot, such as protein-protein interactions. These interactions do not have an active site that can be specifically targeted, so macrocycle drug design must explore as much structural diversity as possible. This work explores a new site for introducing structural diversity on the macrocycle backbone that does not compromise conformation or yields during cyclization. This glycine-containing macrocycle uses a two pot, four step synthesis where all but one intermediate can be isolated and characterized. This macrocycle is a dimer, in which the monomer is synthesized in three steps by three substitutions onto an aromatic triazine ring. These substitutions include BOC protected hydrazine, dimethyl amine, and glycine conjugated to an amino-ketone. The use of a ketone rather than an acetal during monomer synthesis introduces a new site for adding structural diversity to the macrocycle backbone. The molecule is purified via silica gel chromatography after every substitution to prevent side reactions and increase yield. Once the monomer is synthesized, dimerization occurs with acid-catalyzed imine formation. 1H and 13C NMR confirm the successful synthesis of each intermediate as well as the macrocycle. Additionally, COSY data confirms the structure of the macrocycle, while ROESY data confirms the shape and folding. The implication on future drug design is described.

FrogCrew is a comprehensive web-based system designed to simplify the management of TCU Athletics sports broadcasting crews. Traditional manual methods of scheduling, tracking availability, and assigning roles are inefficient and prone to errors. This often leads to miscommunication and scheduling conflicts. To solve these challenges, FrogCrew provides a unified platform for administrators. It enables them to manage game schedules, assign roles based on availability and qualifications, and automate notifications efficiently. Key features include customizable crew member profiles. These profiles allow users to update essential information such as availability, roles, and qualifications. The system also offers an automated scheduling tool that simplifies the process of creating game schedules and assigning roles. Additionally, FrogCrew includes a shift exchange feature. This feature allows crew members to request shift swaps, with automated notifications sent to administrators for approval. The system's reporting tools provide financial reports, position-specific insights, and individual performance analyses. These tools help administrators assess crew utilization and manage costs effectively. By automating core functions, FrogCrew reduces manual workload and minimizes errors. It also improves communication between administrators and crew members, ensuring optimal staffing - ultimately enhancing the execution of our TCU sporting events; Go Frogs!

Wearable smart devices, which continuously capture physiological signals such as heart rate, respiratory patterns, and blood oxygen levels, offer significant potential for the early detection of serious health conditions. Timely diagnosis of diseases such as arrhythmia and sleep apnea can greatly enhance patient outcomes by enabling early intervention. However, extensive collection of diverse, real-world wearable sensor data faces challenges due to privacy concerns, data scarcity, and logistical constraints. This research introduces a novel deep learning framework that integrates publicly available wearable sensor data with synthetic physiological signals generated by large language models (LLMs) to create comprehensive and privacy-compliant hybrid datasets.The proposed framework leverages convolutional neural networks (CNNs), optimized for time-series data analysis, alongside advanced machine learning techniques to identify early signs of arrhythmia, sleep apnea, and related health conditions from physiological data. The integration of synthetic data generated by LLMs addresses critical challenges of limited data availability and privacy concerns, enriching the training datasets with diverse scenarios and physiological variations. Preliminary experimental results demonstrate that the hybrid approach, combining publicly accessible wearable sensor data and LLM-generated synthetic signals, significantly enhances the model's accuracy, generalization capability, and resilience to data variability. Models trained on hybrid datasets consistently outperform those relying solely on real-world data, suggesting that synthetic data provides meaningful supplementation to traditional datasets. This study further highlights how synthetic physiological data can enhance the scalability and efficacy of AI-based health monitoring systems, reducing dependency on extensive clinical data collection. By exploring and validating this innovative data synthesis approach, the research contributes significantly to developing more effective, accessible, and proactive healthcare diagnostic tools, ultimately advancing AI-driven solutions in preventive healthcare.

The iPELiNT project develops an AI-powered patent analysis dashboard designed to streamline the patent prosecution process for attorneys and practitioners. This web application leverages modern technologies including Vue.js with Nuxt3 framework for frontend development, NodeJS with Express for backend services, MongoDB for database management, and integrates AI models from OpenAI to analyze patent documents.

The system features a user-friendly dashboard that allows practitioners to upload patent applications, analyze document health, view CPC prediction analytics, examine keyword relevance, and identify potential prior art conflicts. Key functionality includes document parsing, automated health checks, Art Unit prediction, and generation of actionable reports. The solution also incorporates user account management, notification systems, and specialized document generation tools.

Development followed an iterative process with clearly defined milestones and tasks distributed across team members. The project addresses a critical need in the patent industry by providing an all-in-one platform that simplifies complex patent analysis, replacing traditionally fragmented and cumbersome tools with a streamlined, intuitive interface.

The completed iPELiNT dashboard enhances efficiency for patent professionals, improving application quality through AI-powered insights, and ultimately streamlining the patent prosecution workflow with modern design principles and cutting-edge technology.

Public property tax data is often presented in raw formats, making it difficult for the average user to interpret. Our client initially developed a product that provided access to Kern County property tax information only. Our project enhances accessibility by developing ParcelSearch.com, a platform that centralizes property tax data. With this rebranded system, we have expanded coverage to include Kern, Monterey, and Tulare Counties, with plans for further expansion. Users can create accounts and choose from various subscription plans to conduct property searches using multiple search criteria: owner name, parcel number, and legal descriptions. With the development of a user-friendly interface and expanded search functionalities, the platform caters to realtors, investors, and homeowners seeking property insights. This system was built using modern web technologies, including Vue.js for the frontend, Java and Spring Boot for the backend, and PostgreSQL for database management, to name a few. Future plans include expanding nationwide to create an all-encompassing and user-friendly property data platform.

Longspeeds is an innovative e-commerce platform designed to streamline the buying and selling of auto parts, providing a seamless experience for both individual customers and automotive businesses. The platform offers a comprehensive catalog of high-quality parts for a wide range of vehicles, from everyday cars to performance and specialty models. Leveraging modern web technologies such as Next.js, Longspeeds ensures fast performance, responsive design, and secure transactions. Key features include advanced search and filtering, user-friendly inventory management, real-time order tracking, and support for both retail and wholesale transactions. With a focus on reliability, affordability, and user satisfaction, Longspeeds aims to become a trusted destination for auto enthusiasts and mechanics alike.

Academic advising presents significant challenges in both time management and complexity. Currently, students navigate between two advising options: generic online resources and personalized consultations with professors and advisors. While personalized advisement offers tailored advice, professors cannot be expected to meet with every undergraduate in their major, especially as enrollment grows, and academic advisors may lack specialized knowledge required for some majors. Echelon addresses this gap by creating a middle ground between generic and personalized advising, offering students an effective supplement and saving time for all parties involved. Echelon functions as an intelligent chatbot assistant powered by large language models such as Llama 3 and Mistral. Upon signup, students share their academic records, enabling Echelon to create individualized profiles that consider key factors such as major/minor selection and performance in critical courses. The project is being built using TypeScript and Rust with Svelte and Axum frameworks, respectively. Echelon utilizes PostgreSQL for user account and conversation storage and Qdrant for vector storage and retrieval. Designed with flexibility in mind, Echelon can be deployed at any university, given basic institutional information such as course catalogs and degree requirements.

Cryptococcus is an invasive fungus that causes cryptococcosis, an infection that highly affects immunocompromised people. There are currently a limited number of antifungals available to treat Cryptococcus, and with the increased in antimicrobial resistance, we need different alternatives to treat fungal infections. Our lab has identified proteins involved in the synthesis of eicosanoids, which are lipid signaling molecules involved in regulating the immune response. Moreover, fungi can produce eicosanoids using different enzymes that humans do, opening a line to identify new drug targets using these pathways. Previously, our lab had identified genes upregulated in the presence of the eicosanoid’s precursor, arachidonic acid. Our goal is to use bioinformatics to predict and characterize the protein structure, using AlphaFold2, a machine learning application based on a deep neural network, and using this tool, identify small molecules that will bind to the proteins and help make drug design more efficient.

FWDX BioBlade is a web-based system designed to automate genetic sequence comparison for Fort Worth Diagnostics (FWDX), a company specializing in high-quality diagnostic reagents. FWDX faces a significant challenge: ensuring its reagents remain effective as pathogens mutate over time. Currently, this process is manual, time-intensive, and costly, relying on external bioinformatic consultants to compare existing reagent sequences against national and international genetic databases like NCBI and GISAID.

BioBlade improves this workflow by automating sequence comparisons, detecting mutations or deletions, and generating real-time reports. This automation significantly reduces turnaround time, improves accuracy, and lowers costs, empowering FWDX scientists and regulatory personnel with timely and accurate information. Key features include:
- Automated sequence analysis for efficient reagent validation
- Customizable query intervals for up-to-date comparisons
- A user-friendly reporting dashboard for streamlined decision-making

As artificial intelligence and machine learning continue to evolve, the need for efficient search and retrieval mechanisms for unstructured data has grown exponentially. Traditional relational databases, optimized for structured queries, struggle with the high-dimensional nature of modern AI-generated embeddings. This challenge has led to the rise of vector databases, specialized systems designed to store, index, and retrieve data based on similarity rather than exact matching.

This symposium explores the fundamental concepts of vector databases, their key components—such as vector embeddings, indexing techniques, and similarity search algorithms—and their advantages over traditional databases. We discuss how vector search operates using distance metrics like cosine similarity and Euclidean distance and compare the roles of vector databases and standalone vector indexes.

This project presents an interactive forecasting tool for Influenza-like Illness (ILI) trends using historical CDC data and machine learning models. Built with Python and Streamlit, the app enables users to visualize yearly ILI patterns, compare predictive models, and forecast future cases based on recent trends. Three models — Linear Regression, Random Forest, and XGBoost — were evaluated using Root Mean Squared Error (RMSE) and R² Score. Surprisingly, Linear Regression achieved the best performance with an RMSE of 0.106 and R² of 0.960, indicating that simple models can be effective for this type of time-series data. The app also includes features for dynamic forecasting and CSV export, making it a practical tool for public health analysis and planning.

Our client, Trailspur Capital Partners, is a real estate investment company based in Texas. We assist the company by building a database about commercial / industrial real estate to manage the market more easily and better decision-making. The business requires both the Geographic data from the County’s officials and the properties listings with vacancies information. Our goal is to design a database that can handle the aggregate data coming from both sources, which includes arranging and categorizing the properties, coming with several built-in functions namely identifying listings / vacancy changes, before deploying everything to the server. Our frontend, built with Vite and Vue, provides a smooth and interactive user experience while on the backend, we utilize AWS Lambda with Python to automate essential tasks, including downloading official county appraisal data, performing spatial merges using GIS functions, and managing our Supabase database. After successfully aggregating real estate data from both sources into a structured database, which enables easier tracking of property status changes, the platform efficiently processes and visualizes real-time property listings, allowing our client to analyze market trends and make data-driven investment decisions. Our project enhances real estate market intelligence for Trailspur Capital Partners. The system’s automated functions minimize manual workload and improve the accuracy of property tracking, providing a scalable solution for future expansion.

Cognitive Behavioral Therapy often relies on patients consistently completing therapeutic homework, regularly assigned by their therapist. A leading cause of Cognitive Behavioral Therapy failure for patients is non-compliance with their assigned therapeutic homework. About 20%-50% of patients fail to complete assignments due to inconvenience, a lack of clear instructions, or forgetting to finish the assignment. MENDmate is an online platform designed to solve this problem by providing a streamlined user experience for homework assignment and completion. MENDmate allows providers to assign homework to their patients and monitor their progress. It also provides patients with the ability to track and complete their homework assignments. Additional features of MENDmate include a learning library that gives patients an opportunity to learn about mental illness and practice coping techniques, a journal that allows patients to record their experiences and daily mood and anxiety assessments. MENDmate also tracks and reports the patient's data trends such as completed assignment, mood level, and anxiety level, which allows both the therapist and the patient to keep track of their progress over time.

Fort Worth PsychWorks, a leading psychiatry office, provides comprehensive neuropsychological and psychological assessments for patients across all age groups. Currently, after administering a variety of cognitive and behavioral tests, psychiatrists must manually input the resulting data into report templates, a process that is both labor-intensive and inefficient. This manual approach can take between 45 minutes to two hours per report, detracting from the time available for direct patient care and reducing the clinic’s overall operational efficiency.
To address this challenge, our senior design project introduces an automated report generation system named the PsychWorks Report Generation System. This software solution empowers psychiatrists to select or customize templates tailored to individual patient needs, add or remove specific tests, and automatically generate detailed report text, tables, and charts based on input scores. By maintaining the flexibility of the existing Excel-based system while significantly reducing the time and effort required for report creation, the PsychReport Automator enhances the user experience for clinic staff, boosts the potential for billable hours, and ultimately supports improved patient outcomes.

Obesity disproportionately affects underserved communities due to systemic barriers such as limited healthcare access, socioeconomic challenges, and a lack of culturally relevant health resources. Under the leadership of Dr. Christina Robinson and her team of medical students—Rumaila Hussain, Kavita Patel, Joice Song, and Fatema Jafferji—we are developing a mobile health application designed to support individuals in managing their health more effectively. This app will provide users with tools to track biometrics, manage chronic conditions, and set SMART (Specific, Measurable, Achievable, Relevant, Time-bound) health goals. A key feature of the app is a motivational text messaging system that encourages users to stay engaged with their health objectives. By integrating personalized and accessible interventions, this project aims to bridge healthcare gaps and empower individuals to take proactive steps toward healthier lifestyles.

The rapid advancement of artificial intelligence (AI) presents a unique opportunity to revolutionize education through personalized learning experiences. Traditional teaching methods often fail to address the diverse learning needs of students. This research explores the application of AI in education, focusing on machine learning algorithms, intelligent tutoring systems, and adaptive learning models to create personalized educational experiences. By analyzing student data, AI can optimize learning pathways, improve comprehension, and enhance engagement. The study discusses the potential, challenges, and future directions of AI-driven personalized education.

As global energy demand evolves, maintaining power quality has become a critical challenge in modern electrical systems. This research project examines key factors influencing power quality, focusing on maintaining a stable voltage magnitude and frequency across the grid. To achieve this, we explore techniques such as power factor correction and its role in improving energy efficiency and reducing costs. With the increasing integration of electric vehicles, data centers, and other high-power loads, new challenges arise in grid stability and demand management. Additionally, we investigate system overloading and transmission line considerations, addressing the risks of rising power demand and strategies for mitigating losses. Through this comprehensive study, we highlight the importance of power quality in ensuring the efficiency, reliability, and resilience of modern electrical infrastructures.

This project focuses on automating and standardizing the crowning process of a 15-foot Farnham roll form machine, used to shape aluminum parts, including fuselage and wing skins. The current crowning adjustment compensates for force imbalances caused by screws positioned at the machine’s ends and requires extensive manual shimming for optimal contact along 18 adjustable brackets. This process is detrimental to the manufacturing flow, as the time it takes to adjust the Farnham Press for different types of sheet metal or bends is long enough to significantly slow down production. To streamline this process, the project’s objectives are to design a method to measure bracket-to-material contact accurately, create an adjustable bracket system without the need for shims, and provide operators with real-time measurement feedback to optimize crowning adjustments efficiently. This will be achieved by redesigning the brackets with integrated sensors to accurately read the changing force along the beam.

Progress to date includes multiple bracket designs developed by the mechanical team, featuring adjustable mechanisms such as vertical screws, wedges, and easily insertable shims for depth control. Concurrently, the electrical team has conducted extensive research into sensor options and collaborated with sensor companies to identify suitable measurement solutions. Efforts are also underway to establish a data display interface that can provide real-time readouts from all 18 sensors, enabling operators to make informed adjustments during operation. Future work aims to explore a CNC-style interface for full control automation, which would allow streamlined adjustments for different part profiles and material thicknesses. This approach is expected to significantly reduce setup time and improve consistency in part quality.

An analysis of the sound-producing characteristics of a tenor trombone has been initiated at TCU. Focus of the effort will be on the model Conn 44H "Vocabell" tenor trombone due to its unique rimless bell. A numerical model of the instrument using Autodesk Inventor has been created. The model was imported into NASTRAN for further structural and acoustic analyses.

Key areas of focus include understanding the interaction between the instrument's structural vibrations and the sound radiated from the bell. The "Vocabell" design, known for its unique construction and acoustic qualities, will be critically examined to assess how its geometry and material properties influence sound production and associated frequency spectrum. Radiated sound and structural vibration measurements have been conducted on the physical instrument, providing data for model correlation and validation. Once validated, the numerical model will be used to explore more advanced concepts of brass instrument design.

The engineered concrete slab is a fundamental structure in construction with its mechanical properties influenced by the rebar placement, curing process, and the ratios of its primary components aggregate, cement, and sand. This study investigates how variations in rebar placement, concrete composition and curing methods effect the flexural strength of the sample. In ENGR 30014, 18 engineering teams produced their best sample of concrete with different ratios, rebar patterns, and different types of curing. The results provide insights into optimizing the concrete ratios, rebar placement, and methods for curing and their effect on flexural strength.

The goal of this research was to enable information transmission through light using a Phase Light Modulation (PLM) module to decode and display encrypted information. We conducted a literature review and set up an evaluation module capable of sending encrypted messages and transmitting data without the need for optical cables. Our setup includes a laser light source, a beam expander, a Digital Micromirror Device (DMD) controlled by an electronic control board, and a laptop running the software GUI provided by Texas Instruments. We conducted various experiments with these components to optimize the design and explore potential applications. Our findings highlight the potential of this technology for future data transmission and optical devices.