Abstract

Carbon Dioxide (CO₂) reduction to value-added chemicals, including alcohols and light olefins via heterogeneous catalysis utilizing novel photocatalysts, can reduce the adverse effects of excessive CO₂ emissions and our dependence on fossil fuels. In₂O₃, Cu, Fe and Zn based catalysts coupled with zeolites can increase the selectivity of desired products. Computational techniques such as density functional theory and machine learning assisted materials design will enable versatile and robust photo capture of CO₂. Utilizing machine learning models unlocks the potential to discover catalysts, predict catalyst performance, aid in the prediction of catalyst stability, and target performance enhancements of catalysts to further improve the selectivity of desired products.

Keywords: Carbon dioxide; Alcohols; Olefins; Heterogeneous; Photocatalysts; Fossil fuels