The inclusion of a pyridine moiety in the skeleton of tetra-aza macrocycles introduces rigidity while also introducing a handle by which the electronics and basicity of the ligand can be tuned. Metallation of these pyridinophanes has resulted in active mimics for metalloenzymes, such as superoxide dismutase mimics. However, recent work has explored their potential for industrially relevant catalytic reactions. Previous studies of iron RPyN3 complexes showed moderate success for a direct Suzuki-Miyaura C-C coupling reaction. In that work, it became clear that the substitution on the 4-position of the pyridine ring offered significant influence over the efficacy of the catalyst: the electron donating groups offer a better handle of modification of the electronic properties of the iron center, but the electron withdrawing groups increased the catalytic activity of the complex. In this presentation we introduce a second pyridine ring to the macrocycle skeleton, which includes a second position for modification, and compare the activity of this new RPy2N2 iron complex series to the previous RPyN3 series. Yields within this new series of iron complexes will be compared along with characterization of the respective complexes to understand what properties mitigate reactivity.

Oxidative stress is caused by the accumulation of reactive oxygen species (ROS) in the body and is a key player in many maladies, including neurological diseases like Parkinson’s and Alzheimer’s disease. Superoxide dismutase (SOD) enzymes are capable of transforming the common ROS molecule superoxide (O2-) into less toxic species such as H2O2 or O2, thus protecting the body from harmful reactions of superoxide. Synthetic metal complexes show promise as SOD mimics and can be effective alternatives to therapeutic dosing of SOD enzyme for oxidative stress. In this work, we present a series of 12-membered tetra-aza pyridinophanes (Py2N2) and the corresponding copper complexes with substitutions on the 4-position of the pyridine ring. The SOD mimic capabilities of the Cu[Py2N2] series were explored using a UV-Visible spectrophotometric assay. Spectroscopic, potentiometric, and crystallographic methods were used to explore how the electronic nature of the 4-position substitution affects the electronics of the overall complex, and the complex’s activity as a SOD mimic. This work is an initial step toward developing these Cu[Py2N2] complexes as potential therapeutics for neurological diseases by mimicking SOD’s capabilities and protecting the body from oxidative stress.

The therapeutic potential of macrocycles provides a tantalizing opportunity in drug discovery. The design criteria, such as solubility properties, for macrocycles is only beginning to be understood. One of the significant limitations to such investigations is the synthetic challenge that macrocycles provide and the need for comparison across similar molecules. This study describes the creation of a library of macrocycles to probe and ultimately to engineer partition coefficients. Recently, the quantitative dimerization of monomers to yield 24-atom macrocycles has been described. Historically, a trichlorotriazine is substituted with BOC-hydrazine, an amino acid, and an auxiliary amine (which has been limited until this point to morpholine or dimethylamine). Subsequently, an acetal is installed and treatment with acid results in quantitative dimerization to form macrocycles. To increase the efficiency of synthesis in this study, the acetal is installed prior to the auxiliary amine—the point of divergence. Here, five auxiliary amines were installed to give five monomers. These five monomers were combined in equimolar amounts and treated with acid to induce dimerization to yield five homodimers and ten heterodimers. The octanol:water partition coefficients of these molecules reveal a compensatory effect of substitution. That is, at pH 7, the partition coefficients of the heterodimers lie between the values of the corresponding homodimers. At this pH, the logP ranges between 1.9 and 4.3, indicating that relatively small molecular changes result in large variation in the logP of these macrocycles. The ability to engineer one property—the partition coefficient—suggests that a secondary property—shape—is conserved, a hypothesis borne out by NMR spectroscopy.

Asphaltenes constitute the heaviest, most diverse, and most chemically unresolved component of petroleum crude oils. Asphaltene mixtures are structurally complex, containing thousands of distinct species with a broad range of molecular weights, functional groups, and aromaticity. The structural diversity of asphaltenes, along with their tendency to aggregate, has hindered a complete understanding of the asphaltene component of crude oil. Modern asphaltene studies have deciphered hundreds of individual asphaltene structures through atomic force microscopy (AFM). The structural diversity and expanding chemical knowledge of asphaltene structures necessitates a way to store and easily retrieve and analyze this information. Additionally, much remains unknown about the connection of these imaged structures to ensemble properties of asphaltenes in crude oil. Herein, we address these two points via creation of a database of 69 published asphaltene structures. Quantum chemistry calculations are run to determine molecular properties of these individual asphaltenes and are stored on the database. These properties include molecular weight, solubility, aromaticity, dipole moment, and HOMO-LUMO gap. The database is exploited to generate graphs—such as the UV-Vis absorbance spectrum—using these computed properties to allow for a more complete chemical description of the ensemble properties of asphaltene mixtures. Our computational predictions give a more complete chemical description of previously determined individual asphaltene structures and help contextualize them with respect to their ensemble properties in crude oil.

Although chromatography is a reliable purification method in protein downstream processing, it has several limitations such as loading capacity, scalability and operation costs. These are important drawbacks especially for proteins generated from cell cultures with a high yield. Protein crystallization, which does not suffer these limitations, is regarded as a promising alternative to chromatography for protein purification. However, since protein crystallization is a complex not-well-understood process, protein crystals are often produced at low yield and with poor reproducibility. Thus, its implementation in protein purification protocols remain challenging. In our lab, we designed a new strategy for enhancing protein crystallization from metastable protein-rich droplets generated by liquid-liquid phase separation (LLPS). This strategy is based on the use of two additives; the first additive is needed to induce LLPS in protein aqueous solutions, while the second additive modulates the ability of protein-rich droplets to produce crystals. A protocol for determining yields of LLPS-mediated protein crystallization was also developed. This poster reports our experimental results on yield of lysozyme crystallization in the presence of NaCl (0.15 M) as an LLPS inducer and 4-(2-hydroxyethyl)-1-piperazineethanesulfonate (HEPES) as a modulator. Our results show that addition of HEPES (0.10 M) significantly boosts lysozyme crystallization yield from ≈5% (no HEPES) to 92%. The effect of temperature and incubation time on the yield of protein crystallization yield was also investigated. Our results reveals the key role of LLPS in enhancing protein crystallization.

Small molecules are reliable and pervasive pharmaceuticals because critical drug characteristics are predictable including solubility and membrane permeability. In addition, small molecules are typically inexpensive to produce and their mechanisms of action subscribe to a common paradigm vis-à-vis blocking an enzyme active site. In contrast, nature employs elaborate machinery to make large molecules, oftentimes rings (or macrocycles). Drug companies avoid these because the rules for predicting behavior are under-explored and the paradigm used for action is different vis-à-vis blocking protein-protein interactions. Moreover, they are costly and laborious to make. To contribute to an understanding of drug design for large molecules, our group is preparing a series of large molecules (macrocycles). The lead adopts a beta-sheet conformation in the solid state, but its behavior in solution is unknown. Here, a second member of the class is described wherein alanine replaces glycine in the macrocycle to provide additional handles to study conformation and the effects that structure has on critical parameters. The 26-atom macrocycle is synthesized in a three-step process. The reaction of a triazine core, and the addition of BOC-hydrazine, alanine, and dimethylamine yields the first intermediate which undergoes elaboration with a 4-carbon acetal group using traditional peptide-coupling strategies (HBTU). Dimerization of the resulting monomer occurs in a 1:1 mixture of dichloromethane and trifluoroacetic acid. Reaction progress is followed by thin-layer chromatography and the identity of the products is confirmed by 1H and 13C NMR spectroscopy. Conformational analysis rests on 2-D 1H NMR spectroscopy. The molecule will also be subjected to analysis for solubility and membrane permeability. In the longer term, these beta-sheet mimics will be used to disrupt protein-protein interactions with an emphasis on the BRCA1-PALB2 interaction implicated in breast cancer.

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.
Alginate-based hydrogels are water-infused, biodegradable polymer networks. These are particularly useful because of their environmental abundance, and their ability to interface well with human skin. 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 into various aqueous environments and hydrogels to test how variations in ion concentration affect the flow of electrical current as a function of applied voltage. pSi membranes are fashioned into diodes upon the attachment of 0.25 mm diameter copper wire using silver epoxy and annealing. An electrochemical cell is created by placing two pSi membranes parallel each other in an electrolyte composition. Current is measured as a function of applied voltage (typically from 0-5 V) for systems with differing NaCl concentration.
As expected, the magnitude of maximum current response is proportional to ion concentration present in the electrolyte, with an order of magnitude amplification or more of measured current for a given voltage upon immersion of the electrodes in an alginate hydrogel matrix relative to water alone.
This presentation will focus on initial diode fabrication protocols, as well as establishing limits of detection for simple ions species present in human sweat. More refined strategies are also envisioned, including the development of methods for stabilization of sensor performance along with miniaturization of the sensing platform itself.

Testing a Computational Workflow for Drug Design: pKa and logP from the SAMPL7 Blind Challenge
Katie Zabel
Advisor: Benjamin Janesko
Being able to produce accurate predictions of pKa for various molecules is an ongoing effort in computational chemistry. Drug companies and industries are constantly seeking accurate predictions of pKa and lipophilicity for molecules that are possible drug candidates. Accurate predictions of these values means that time, money, and effort won’t be wasted synthesizing molecules that aren’t going to be effective drugs. The Janesko group has developed a workflow that uses CREST for conformational analysis and (M11plus/def2TZVP/SMD) DFT calculations to identify a molecule’s pKa. The DFT calculations process and refine the relative energies of the stable conformations. The goal of this project is to benchmark the current workflow against the SAMPL7 challenge, which will test the workflow’s outperformance of the best quantum-mechanical methods from 2021. The SAMPL challenge is a competition that asks participants to predict the properties of molecules that have never been synthesized. These molecules will then be created in labs and their properties will be accurately tested. Comparison of the competitor's predicted properties to the true values measured will assess the accuracy of the competitor's predictions. If the prediction of pKa using the current workflow is accurate based off the benchmark against the SAMPL7 challenge, then the workflow could be entered into the next SAMPL Blind challenge.

During the college admissions process, students are presented with an overload of information from each school they are applying to and accepted by. A critical aspect for deciding on a school is the estimated Cost of Attendance (COA) and the financial aid package. Each school calculates their COA differently and thus offers a unique financial aid package. It is important for students to have a way of comparing and evaluating a school's cost with financial aid. While college counselors have developed excel sheets with algorithms that compare personalized cost with financial aid and scholarships, not all students are familiar with excel which may result in an inaccurate analysis. Transparent Tuition is a tool for students to accurately compare financial aid options from each university they are applying to. This project was developed using React.js and Spring Boot. These are two development libraries that will make Transparent Tuition scalable in the future. By creating a user-friendly web tool, students can better understand the school’s information and make a more educated decision when deciding on their university. Students will be able to connect with a college counselor to receive advice regarding their options when choosing a university. This will allow students to make an educated decision on their college based on both the short-term and long-term financial impact.

College students go through many transitions during their time at school. They learn to live on their own, manage household tasks, and balance their academics. A specific change in college is to learn how to grocery shop and cook for yourself. When students move off campus, they go from a dining plan where most of their meals are provided to needing to make all their meals. This results in many students relying on fast food or the same easy meals. Easy Bites, in partnership with TCU’s Nutrition Department, is designed to help students find quick, cheap, and nutritious meals. All our recipes are designed by Nutrition students on campus for college students to add variety to their diet. Easy Bites is composed of two aspects: an online portal for nutrition students to submit recipes for approval, and a mobile app for college students to view recipes. Our mobile app is connected to the Kroger database to provide users with accurate information about specific ingredients prices and availability. By working with the Nutrition Department and connecting with the Kroger database, we are making it easier for students from the deciding on recipes, shopping for the ingredients, and making the meal. With this, Easy Bites makes it easier to make nutritious meals as a college student.

49 million people in the United States have suffered from anxiety disorder in the past year, and 80 million have suffered in their lifetime. Many traditional methods of treatment, while often helpful, are sometimes inaccessible, time-consuming, expensive, intimidating, or overall impractical. In a world where people are increasingly in need of care and therapists are increasingly burnt out, technology bridges the gap and increases accessibility for those who previously would have been excluded. What CognitV strives to create as a solution is a Virtual Reality Exposure Therapy experience where patients can face their anxiety in a safe, controlled environment through a VR headset. Geared towards players with Social Anxiety Disorder, this treatment method allows patients to safely expose themselves to public speaking and confrontational scenarios from the comfort and privacy of their own homes. This treatment method would be faster and more accessible, is preferred by younger patients, and fills the treatment avoidance gap, all while providing a realistic, immersive experience that can effectively aid in treating mental health disorders, either with or without an accompanying clinician.

Using Virtual Reality and Artificial Intelligence, CognitV creates an immersive environment geared towards Players with Social Anxiety Disorder which allows them to safely expose themselves to public speaking and confrontational scenarios from the comfort and privacy of their own homes.

“AI Powered Patent Analysis Software”
Patent AI is an online patent analysis tool which gives feedback on uploaded patent application documents and provides a likelihood of it being accepted by the USPTO.
This tool is meant to reduce the rate of rejected patents –being at 90%– and the wait time associated in getting a response from the USPTO.
Our application is informational, accurate, intuitive, and will simplify the patent application process.

IMPROVING COLLECTION MANAGEMENT IN THE MONNIG METEORITE COLLECTION.
R. G. Mayne, J. Huther, Y. Tyagi, B. Danysh, R. Perkins, M. O' Connor, T. Truong, and B. Wei.
Monnig Meteorite Collection, Texas Christian University, 2950 W Bowie Street, Fort Worth, TX 76109 (r.g.mayne@tcu.edu)
Department of Computer Science, Texas Christian University, Suite 341, 2840 W Bowie Street, Fort Worth, TX 76109.

Introduction: Collection Management Software is a vital tool in sample-based science and a key part of any scientific collection of meteorites. However, this software is often designed as a one-size-fits-all solution, which can be used for all collections within a museum. As a result, much of the commercially available software for collections management is not ideal for the curation needs of extraterrestrial materials. Platforms are often vendor-specific, contain redundant and unnecessary functionality, and require significant time to be invested in staff training.

Over the past two decades, The Monnig Meteorite Collection has utilized FileMaker Pro for the management of the Collection. FileMaker Pro was chosen as it allows the user to design a custom solution to fit their specifications. However, this either requires that the administrator stays current on all updates and functionality of the software, or continual investment in external support for the system. The current database was designed in 2014 and is no longer meeting the needs of the Monnig Collection or the users of the database, who are primarily sample-based scientists and collectors. After consultation with industry experts, curators, and users of the database, it was decided that an update of the current database was not the best approach for the Collection, instead a new custom database that meets the needs of both the Curator and the user was commissioned.
This project introduces the development of a comprehensive database and user-friendly web application portal, marking a substantial improvement over the existing legacy system.

Project Overview: The primary aim of the Monnig Meteorite Database Project, hereafter referred to as MMDP, is to offer a detailed and robust database for the Monnig Meteorite Collection. It will feature an enhanced catalog search portal, enabling users to explore and search the collection through various parameters and filters. The system is also designed to aid gallery curators and administrators by providing detailed views of collection items, tracking sample history, and managing loans, all within a secure and user-friendly interface.

MMDP seeks to preserve the wealth of knowledge encompassed within the Monnig Meteorite Collection. The digital database and search tool will facilitate research and offer broad access to the collection for researchers, collectors, educators, and students. This initiative is set to serve as a valuable educational and scientific resource, equipped with extensive functionalities.

The database is being developed as a senior design project in the Department of Computer Science at Texas Christian University (TCU). The senior design project is a year-long program required of all Computer Science and Data Science graduates, where they work with external clients to develop and implement workable solutions to the briefs provided.

System Development and Preparation: in the Fall 2023 semester, the MMDP Team focused on data preparation and outlining the project scope into needs (must have features for launch), wants (features that are not required at launch but the capability to add them later is required), and wishes (features that are not required). Inconsistencies in the legacy data were identified and corrected; these included repeated entries, varied date formats, typographic errors, and missing fields. Python was utilized for data cleaning, and the team standardized data and organized it into relational database tables using PostgreSQL, hosted on Azure cloud for maintenance and backup.

Development will continue throughout the Spring 2024 semester and the outdated and insecure legacy portal will be replaced with a newly developed web application. This application is being built using Spring Boot for backend operations, and HTML5, CSS, and the VueJS Framework for a responsive front-end UI, ensuring accessibility across various devices. The current launch date for the new collections management system is May 2024.

Functionalities: MMDP will address the need for functionality for both the administrators of the database (primarily the Curator in this case) and the external user (Figure 1). The required parameters for both of these audiences are described below.
All users of the database will be able to:
1. perform parameterized searches using criteria such as Name, Monnig Number, Class, Group, Clan, Country, and Observed Fall or Found (Figure 2a).
2. filter and modify search results directly on the search result page (Figure 2b).
3. Find accessible detailed information about each meteorite sample, including images, from the search results via individual display pages for each sample.
4. download all the search results based on the given constraints with a single click from the search results page.

Administrators will be able to
1. have access to specialized functionalities that are secured and restricted from regular users. Upon logging in, they are presented with a portal offering various database management options.
2. view more detailed information about samples than regular users, including the sample's history and loan information. They have the ability to add new meteorite samples or create subsamples.
3. perform data manipulation tasks, such as deleting or modifying existing sample records.
4. have control over the media associated with samples, allowing them to add or delete media.
5. administrators able to create, view, update, and delete history entries for each sample. This historical data management is a key new feature not possible in the current system.
6. Access loan management capabilities include adding, modifying, archiving, and accessing archived loan entries for samples.
7. print labels for samples, which can be used for curation in the vault.

Summary: The MMCD stands as a model of integration, combining domain expertise, data best practices, and user-centric design. This project offers a template for other universities, museums, galleries, and research centers aiming to enhance their functionalities and provide a seamless, user-friendly experience for accessing and managing meteorite data collections.
Embodying the spirit of scientific collaboration, this initiative is open to opportunities for collaboration to expand the platform's capabilities or to implement similar solutions in other institutions.

Acknowledgments: We are grateful to the Department of Computer Science at TCU for their continued support of the Monnig Meteorite Collection through the Senior Design project. We also thank Dustin Dickens for his advice and assistance in the discovery portion of the database redesign.

The Department of Computer Science at Texas Christian University offers a course where senior students, in teams, collaborate with clients to solve real-world software problems. Students handle every project phase: definition, analysis, design, implementation, testing, deployment, and documentation. However, in these teams, there's a variation in how much each student contributes. Some are very active, while others are not. Communication issues can also arise. To handle these challenges and improve team efficiency, there is a Student Performance Tracking system in place that includes Weekly peer evaluations where each student evaluates their own teammates in accordance with the rubric defined by the professor and Weekly Activity Reports (WAR) where each student writes down their own contributions for the week.

While this system works and improves team efficiency, these tools are too manual and thus time consuming. For the WAR, each student has to edit the Google Docs document for the week which is then reviewed by the professor. This can lead to human error, meaning some students might not get the right credit if they make mistakes while filling out the Google Docs document. For the Peer Evaluation, each student must review the WAR for the week and then make an excel spreadsheet to evaluate their teammates and then upload it to TCU Online. Once all students have turned in their peer evaluation report for the week, the professor has to download reports of all students and then run these through a Java program which then calculates the results for all students. Then the professor uploads the results to TCU Online (a course management tool used by TCU). Not only does this leave room for human error on the students' side (spreadsheets must have the right columns), but it is also very time consuming for the professor as they have to download all reports manually from TCU Online and then run the Java program and finally upload the results back to TCU Online.

The automated Student Performance Tracking system (Peer Evaluation Tool) streamlines the evaluation process by providing a centralized website where students can directly fill out their Weekly Activity Reports (WARs) and complete peer evaluations. It also enables them to view their own submitted WARs and received peer evaluation scores from their teammates. For the instructor, the system offers the functionality to create and customize evaluation rubrics, which ensures consistency in peer assessments. Instructors can access and review all peer evaluations and WARs, allowing them to monitor team dynamics and individual contributions efficiently. This comprehensive solution eliminates the manual handling of documents and the need for external spreadsheet software, thereby reducing human error and saving time for both students and instructors.

This presentation investigates the learning process of artificial intelligence by training a model to play the game of Go using an AlphaZero-type algorithm. Through evaluation of 12 Go models, the authors reveal the split personality many exhibit, much like the famous Schreiber book Sybil. The best models appear indistinguishable from human players in the early stages of the game before devolving into self-destructive tendencies in the endgame. Possible remedies for this behavior are explored through modifying training data generation, hyperparameter tuning, and optimizing neural network input dimensions.

In the dynamic environment of venues with large seating capacities, efficient management of seating occupancy emerges as a critical challenge. Traditional manual monitoring methods are often cumbersome and prone to inaccuracies, hindering optimal seat allocation and event management. Addressing this issue, our senior design project introduces an AI-based solution tailored to revolutionize real-time seating availability reporting for event organizers.
This project aims to provide a comprehensive tool that enables event organizers to track seating occupancy in real-time, facilitating the identification of peak attendance periods and enabling data-driven decision-making. By harnessing the power of artificial intelligence, our system offers a detailed analysis of seating patterns, thereby enhancing the efficiency of event operations and optimizing resource allocation. The ultimate goal is to improve the event experience for both organizers and attendees by ensuring a seamless flow of information regarding seating availability, leading to more effective management of large-scale events. Through this initiative, we endeavor to set a new standard in venue management, where technology and data converge to create smarter, more responsive event environments.

Efficient teacher-student interaction analysis is essential for educators to enhance teaching quality. Traditional manual review methods are excessively time-consuming and can yield subpar feedback. ClassifAI offers a streamlined solution for educators to gain insights without sacrificing work hours, utilizing the OpenAI Whisper model for transcription and a fine-tuned Gemma model for question categorization.

ClassifAI is advancing existing tools by addressing four key improvements: transitioning to local hosting for cost savings and data security, integrating the WhisperX model for improved transcription accuracy, automating Costa's Three Levels of Thinking question classification via Google's Gemma, and upgrading the web interface for better user experience.

ClassifAI's architecture comprises a user-friendly web server with ExpressJS and React, a local MongoDB database, a fine-tuned Gemma model for question categorization, and WhisperX for speech-to-text. ClassifAI offers speech recognition, diarization, question categorization, and analysis, delivering enhanced performance. Educators easily upload their teaching audio/video on our platform via a file or YouTube, which is then processed by our GPU server for transcription and analysis. The resulting transcript, graphs, and metrics are accessible for review and can be exported in various formats.

Speech impairment ranks among the world's most prevalent disabilities, affecting over 430 million adults. Despite its widespread impact, many existing video-conferencing applications lack a comprehensive end-to-end solution for this challenge. In response, we present a holistic approach to translate American Sign Language to subtitles in real time by leveraging advancements in Google Mediapipe, Transformer models, and web technologies. In March 2024, Google released the largest dataset for the problem domain with over 180 GB in size, containing ASL gesture sequences represented as Mediapipe numeric values. Our methodology begins with the implementation and training of a Transformer model using preprocessed Google dataset, followed by the establishment of a back-end server equipped with the trained model. This server handles video input preprocessing and real-time inference, communicating with client services as a REST endpoint. To demonstrate the practicality of our approach, we developed a video conferencing application utilizing the AgoraRTC SDK, which communicates with our back-end server to transcribe user gestures to text in real time, displaying them on the receiving end. Through this end-to-end system, we enable video calls enhanced by the real-time transcription of fingerspelled gestures with low latency and high accuracy, effectively bridging the communication gap for individuals with speech disabilities. With a growing imperative for AI applications engineered for human well-being, our project seeks to promote the integration of AI in applications designed to enhance human wellness, thus bringing the broader awareness and adoption of this endeavor.

Wood is a fundamental material in various industries, from construction to furniture making. Understanding its mechanical behavior is crucial for optimizing its use and ensuring structural integrity. This study investigates six different wood types under flexural loading, offering insights into their performance in real-world applications. By analyzing key parameters such as density, flexural strength, and stiffness, this research aims to provide valuable data for informed material selection and design optimization. The wood types under scrutiny comprise white oak, birch, bamboo, maple, pine, and walnut with two contrasting grain configurations.

Key parameters: Density, Flexural Strength, Flexural Stiffness

Effective disinfection of medical surfaces is crucial in preventing healthcare-associated infections. The objective of this study was to compare two techniques for transferring bacteria, specifically Staphylococcus epidermidis, from contaminated medical surfaces to agar plates for growth assessment. The first technique involved imprinting the contaminated surface directly onto the agar plate, while the second technique utilized a sterile swab to pick up bacteria and transfer them to the agar plate. Results indicated a significantly higher percentage of bacterial transfer using the imprint technique compared to the swab technique. Consequently, the imprint technique was selected for further investigation to quantify results related to the disinfection of contaminated medical surfaces. This study underscores the importance of selecting appropriate bacterial transfer techniques for accurate assessment of surface disinfection efficacy in healthcare settings.

The two-microphone impedance tube test method is a well-established and widely used technique for determining the acoustic absorption coefficient and impedance ratio of materials. This method uses two closely spaced microphones to simultaneously measure the incident and reflected sound waves. A two-microphone impedance tube measurement system made of 6061-T6 Aluminum with a diameter of 3 inches, a 0.5 inch wall thickness, and microphones spaced 2.7 inches apart has been constructed for undergraduate research at Texas Christian University (TCU). These geometrical values suggest a usable frequency range of 50 Hz to 2637.77 Hz as referenced in ASTM Standard E1050-19. Validation of the system was achieved by taking measurements on Owen Corning Type 705 pressed fiberglass board with a 1-inch thickness and comparing them to absorption data provided by the manufacturer. Additional validation measurements were taken without a test sample in place. All validation tests suggest that the TCU impedance tube is an accurate measurement system.

Bacteria, the primary agents of infection in humans, are present on nearly all surfaces. To mitigate the spread of bacteria and infections, disinfectants are commonly used. This study explored the effectiveness of common disinfectants and different methods of disinfection, primarily focusing on the use of spray pumps and a transducer as a mechanism to disinfect surfaces using 70% IPA (Isopropyl Alcohol) or ethanol (often referred to by the brand name Lysol). Tests were conducted on bacterial lawns before incubation. The effectiveness of the tests was determined by observing bacterial growth over the next 24 hours after disinfection. Testing proved that both ethanol and 70% IPA are effective in stopping bacterial growth. While both the transducer and spray pump methods showed success, the transducer/ethanol combination was particularly efficient, using the least amount of disinfectant.

Thermal oxidation is an important process to create a thin film of silicon dioxide on silicon substrates in microfabrication. In this project, thermal oxidation characteristics on the silicon wafer will be analyzed through experiments in the clean room. The research method was conducted in the thermal oxidation furnace in the TCU Cleanroom on nine wafers with different placement orientations in the furnace and three different oxidation temperatures: 950°C, 1000°C, and 1050°C. In addition, oxide thickness measurements between different locations on the wafer were taken to investigate the film uniformity. The data analysis showed three trends: 1. oxide thickness varies across the wafers, 2. oxide thickness varies as a function of the furnace location, and 3. oxide growth rate varies as a function of furnace temperature. This project investigates how these factors impact thermal oxidation, one of the most critical steps in microfabrication

The Digital Micro-Mirror Device (DMD), which was originally developed for digital projection using visible light source, has seen numerous applications in automotive, manufacturing, spectroscopy, and underwater imaging that require wavelength beyond visible. The DMD window is an important part of the packaging that protects the digital mirror array. Since the light goes through the top and bottom surfaces of the window glass twice during operation, the transmittance of the window is usually optimized for the range of wavelengths specified by the applications through optical coatings. In this research work, we will explore the effectiveness of the optical coatings for different types of glasses for window transmittance improvement in visible and near-infrared wavelengths. We will evaluate the transmittance of the existing DMD window glasses and explore ideas of improving transmittance in the NIR range without compromising the effectiveness in the visible light range. In doing so, we would be the light efficiency of the DMD in a wider wavelength range.

This research focuses on developing an optical metrology system to characterize the 3D refractive index profile of intraocular Lenses (IOLs) and contact lenses. Recent innovations in IOLs and contact lenses have facilitated the creation of lenses with finely controlled refractive index gradients across their surfaces, and as a result, the demand for precise metrological techniques has increased. Optical Phase Computed Tomography (OPCT) holds as a possible method for precisely characterizing these gradients. OPCT operates on the principle of the parallel ray approximation, which assumes that the rays passing through a surface remain unaltered in angle and continue parallel. OPCT has proven effective in determining the refractive index of optical fiber, this success can be attributed to the minimal deviation from parallel ray assumption of the optical fiber. This study aims to ascertain the feasibility of using OPCT for the characterization of intraocular lenses (IOLs) and contact lenses. Our approach involves replicating, through simulation, the previously studied optical fiber to determine the maximum deviation angle from the parallel ray assumption. Utilizing simulated models of IOLs and contact lenses, we investigate the repercussions of deviations from the parallel ray assumption on OPCT precision. We aim to compare these findings to the established deviation observed in fiber optic studies. This comparative analysis will offer insights into the potential applicability of OPCT for IOLs and contact lenses, allowing for further development of enhanced optical metrology techniques.