Metal-Organic Frameworks for Photocatalytic CO2 Conversion: Bridging Fundamental Insights to Practical Solutions

Abstract

Conversion of CO2 into fuels and other valuable compounds is a favorable technology for the solution to global energy and environmental problems. Metal-Organic Frameworks (MOFs), a category of crystalline materials with precisely engineered structures and high surface areas, have garnered significant attention in recent years for their potential in photocatalytic applications, particularly in CO2 reduction. The lack of fundamental understanding of the data reporting and reaction pathways makes a challenging to bring the technology from lab scale to practical applications. In this review, we have provided a clear understanding of possible mechanisms for the production of C1 and C2 value-added compounds from CO2 feedstock. We have discussed the important parameters involved in the photocatalytic CO2 reduction process. Overall, step-by-step collective effort have been made in this review to explore the photocatalytic CO2 reduction process from fundamental lab scale to large scale practical applications. Then, we have discussed the step-by-step process of designing photocatalyst through density functional theory (DFT) simulations, and machine learning (ML). We emphasized that the integration of DFT, ML, and experiments is highlighted as a great solution for identifying optimal photocatalysts for enhanced CO2 conversion. We have discussed some important strategies to increase the selectivity of the product and energy conversion efficiency. Finally, future research directions are proposed in terms of experimental design, theoretical calculations, big-data analytics, and practical implementation.