The Coll de Montllobar cliffs in the Pyrenees Mountains contain plant fossils known as root models, which show signs of oxidation and reduction along a depositional dip, indicating varying environmental conditions Since plant roots do not grow below standing water levels, these fossilized roots and their distribution can serve as markers for past water table positions. This study examines whether root density decreases toward the bottom of the channels, indicating that roots stopped growing once they reached below the water table. If the roots disappear at a certain depth, it suggests that the bar was saturated at that level, stopping root growth. By analyzing the presence and absence of these roots, we aim to determine if they mark a clear boundary indicating historical water table levels. Our findings contribute to understanding past depositional environments and hydrological conditions in this region

The Barnett Shale formation in the Fort Worth Basin has been a substantial producer of oil and gas energy resources. The Barnett Shale serves as an ideal testing ground for innovative approaches to subsurface analysis, offering both abundant production history and a wealth of existing data. This study integrates innovative thermal analysis techniques with AI-driven workflows to rapidly process and interpret large volumes of geochemical data. We aim to identify and evaluate geochemical variability and the distribution, content, and quality of geogenic carbon with depth across key stratigraphic intervals. Expanding subsurface applications of AI and machine learning enhances the scalability of resource assessments and underscores the broader potential of these emerging analytical tools in energy exploration.

Pesticide degradation in the environment is an important element when it comes to understanding long-term soil and water contamination. There are many key molecular factors like molecular weight and octanol-water partitioning (logP) that influence how pesticide degradation works. By taking a computational approach, we derived daughter molecules of ferulic acid, 1,2,4-Trihydroxybenzene, and vanillic acid which share similarities with pesticide byproducts. We specifically computed molecular weight and logP for each derivative to assess their potential to contaminate the environment. By comparing these values to oxidative pesticide breakdown products from glyphosate (Roundup), atrazine, and chlorpyrifos, we identified solubility trends that may influence the transport of these molecules into soils and water systems. These findings provide insight into the environmental risks associated with pesticide use and degradation, potentially aiding in the design of more sustainable agricultural chemicals.

The "You Belong in Chemistry" Periodic Table is a unique and innovative visual representation designed to foster unity and a sense of belonging among students within the TCU College of Science and Engineering. This table uses the traditional periodic table, replacing chemical elements with students, each symbolizing a distinct individual who contributes to the diverse academic environment. The table is not just an artistic display but a tool for connecting students, encouraging collaboration, and highlighting the central role of the Chemistry Club: creating a supportive and inclusive space. Through this representation, students are reminded that, regardless of their backgrounds or academic focus, they have a home within the chemistry community, where they can grow, learn, and thrive together. By bridging gaps and strengthening bonds, the Student Periodic Table stands as a symbol of inclusivity and community.

Artificial intelligence’s integration into healthcare promises more effective and higher-quality patient care. However, its impact on the human aspects of care, such as trust and bias, remains not fully understood. Through a literature review and analysis, this poster provides an up-to-date overview of how the implementation of AI affects patient-provider interactions. This research seeks to answer the question: “How does AI-driven diagnosis and treatment influence patient-provider interactions, and what role does AI bias play in shaping trust and healthcare disparities?” Our findings show a consensus that AI improves productivity, but there is concern that the public’s growing trust in AI over human providers may reshape relationships and perpetuate healthcare disparities. Understanding these dynamics is crucial for developing AI systems that enhance care while maintaining equity and trust in healthcare settings.