Conveners
Parallel Session D: AI-Guided Biointerfaces, Microstructure Prediction, and Functional Materials Design
- Etienne Gnimpieba (University of South Dakota)
Prediction processing-microstructure-property (PMP) link is critical for material processing, characterization, and discovery. We demonstrate GAN-based machine learning models that can accurately predict PMP relationships, specifically in the prediction of (1) the microstructure of alumina under arbitrary laser power, (2) the expected microstructure from the desired hardness, (3) real-time,...
Harnessing microbial systems for methane bioconversion necessitates a detailed understanding of enzyme architecture and microbial-surface interactions. Integrating microbial biotechnology, materials engineering, and artificial intelligence (AI) offers a transformative strategy for advancing sustainable methane bioconversion. In this study, we computationally remodeled the particulate methane...
Sulfate-reducing bacteria (SRB) thrive in many natural environments, deep environments, and processing facilities in industrial settings and form biofilms. SRB biofilms alter the physiochemical properties of metals, inducing fouling and later biocorrosion, which cost USD 1.1Trillion to global GDP every year. To combat biocorrosion, it is essential to understand the interaction at the bacteria...
Sulfate-reducing bacterial (SRB) biofilms are prevalent across natural and engineered environments, mediating biogeochemical sulfur cycling while accelerating biofouling and microbiologically influenced corrosion (MIC). To uncover surface-dependent biofilm adaptations, we performed a comparative transcriptomic analysis of Oleidesulfovibrio alaskensis G20 biofilms formed on copper and...
