Soft Materials Initiative Seminar

ABSTRACT:  Harnessing light for solar-to-chemical energy conversion remains complex due to coupled reactions, competing pathways, and stability issues in artificial photosynthesis systems. Traditional sequential experiments are often insufficient because the parameter space is vast and the reaction landscape is complex. Even high-throughput workflows that rely on conventional methods such as NMR or chromatography face a separate limitation: measuring a complete 96-well plate can take hours, yielding a time resolution that cannot capture real-time kinetics changing by the minute. This challenge is addressed through automated, high-throughput platforms combined with machine vision techniques that quantify reaction kinetics in parallel. In photochemical hydrogen generation, hydrogen-sensitive tape is imaged to spatially and temporally resolve which (photo)catalysts produce H₂ and when. Oxygen-sensitive films have also been developed that enable real-time monitoring of O₂ production and consumption in renewable energy and organic synthesis reactions. The resulting structured visual datasets are used to train AI/ML models that predict outcomes and guide optimization.