Silicon is a fundamental material in modern technology, with common applications including solar panels and numerous electronic devices. While high-purity silicon is necessary for these industries to ensure optimal electrical properties, biomedical applications such as drug delivery can tolerate alternative synthetic methods that prioritize sustainability and cost-effectiveness. This research focuses on developing an environmentally friendly approach to producing high-surface-area porous silicon using self-propagating high-temperature synthesis (SHS). This method utilizes silicon dioxide (SiO₂) as the silicon source, magnesium (Mg) as a reducing agent, and sodium chloride (NaCl) as a reaction moderator. The exothermic reaction between SiO₂ and Mg rapidly generates the heat necessary to facilitate silicon production, while NaCl helps regulate temperature, maintain porosity, and control grain growth. Unlike traditional silicon production processes that require high thermal energy input and costly purification steps, this SHS-based approach is designed to be scalable and accessible, particularly in resource-limited settings.
In a typical reaction, the Mg and SiO₂ reactants are exposed to a finite voltage (~12V) for a fixed amount of time (minutes) to initiate the reaction. After synthesis, the crude silicon product undergoes purification by dissolving the magnesium oxide (MgO) byproduct in hydrochloric acid, leaving behind high-purity silicon. This study aims to optimize reaction parameters (magnitude of voltage and duration) to maximize silicon yield and structural integrity while minimizing environmental impact. X-ray powder diffraction (XRD) is employed as the primary characterization technique to evaluate crystallinity and purity. The combination of a low-energy, cost-effective synthesis process and naturally derived raw materials positions this method as a promising green alternative for producing porous silicon. Its potential for drug delivery applications, particularly in developing regions with limited access to advanced manufacturing infrastructure, further underscores its significance in the field of biomaterials and sustainable materials science.

Reactive oxygen species (ROS) are byproducts of normal cellular metabolism. While essential in cell signaling and immune responses, unregulated or chronic levels of elevated ROS can cause oxidative stress. If this occurs in the brain, oxidative stress can lead to irreversible damage of macromolecular structures, including neuronal cell damage. Excessive ROS species are a hallmark of Alzheimer’s Disease (AD) and other neurodegenerative disorders. Superoxide dismutase (SOD) enzymes serve as a critical defense mechanism against ROS but have been found in lower concentrations in individuals with neurodegenerative disease. As a result, water-soluble small molecules that can mimic the SOD1 activity are of great interest to controlling diseases derived from oxidative stress. Herein, we present the SOD mimic activity for a library of copper tetra-aza macrocyclic small molecules and compare it to the most active congeners reported to date.

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.