Catalytic and photocatalytic CO2 conversion into valuable chemicals

This thesis explores catalytic and photocatalytic CO2 conversion processes with a focus on catalyst design, performance analysis, and machine learning assisted interpretation. A series of datasets compiled from the literature were analyzed to identify patterns governing CO2 methanation, CO2 hydrogenation to methanol, and photocatalytic CO2 reduction using halide perovskites. In methanation, Ni emerged as the most commonly used active metal, with CeO2 and ZrO2 supports yielding the highest conversions. Machine learning model, particularly random forest, demonstrated high predictive accuracy (R2=0.95 for training and R2=0.87 for testing), with temperature and catalyst formulation as key descriptors. For CO2 hydrogenation to methanol, a larger dataset was evaluated. SHAP and association rule mining (ARM) highlighted the significance of Ga, Ru, Y, and support materials such as ZnO-ZrO2 and In2O3-ZrO2 in enhancing selectivity. Cu and Ru catalysts prepared via precipitation and deposition-precipitation methods, respectively, were identified as favorable combinations. ARM results also emphasized promising but underexplored metal–support pairs warranting further experimental validation. Photocatalytic CO2 reduction over halide perovskites was also examined through an extensive dataset. CsPbBr3 dominated the literature, though FA and Bi-based perovskites showed higher gas production rates. MOFs, Ti3C2, and WO3 were among the most effective cocatalysts. The random forest model successfully predicted bandgap values and gas-phase production rates, with preparation methods and cocatalyst strategies identified as key features. Complementing the data-driven analysis, experimental studies were conducted on Pt and Cu-loaded TiO2 photocatalysts, with and without ionic liquids, under gas and liquid-phase conditions. While gas-phase tests yielded limited CO2 reduction, liquid-phase experiments demonstrated measurable CH4 evolution, particularly over IL-modified Cu/TiO2, highlighting the synergistic effect of Cu and IL in enhancing selectivity and activity under water-based, sacrificial agent-free conditions.