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 mitigating 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 reported data and reaction pathways makes it challenging to bring this technology from lab scale to practical applications. In this review, we provide a clear understanding of the possible mechanisms for the production of C₁ and C₂ value-added compounds from CO₂ feedstocks. We discuss the important parameters involved in the photocatalytic CO₂ reduction process. Overall, step-by-step collective efforts are made in this review to explore the photocatalytic CO₂ reduction process, thereby guiding the scale-up process from fundamental lab-scale to large-scale practical applications. Then, we discuss the step-by-step process of designing photocatalysts through density functional theory (DFT) simulations and machine learning (ML). We emphasize that the integration of DFT, ML, and experiments is a great solution for identifying the optimal photocatalysts for enhanced CO₂ conversion. We discuss some important strategies to increase the product selectivity and energy conversion efficiency. Finally, future research directions are proposed in terms of experimental design, theoretical calculations, big-data analytics, and practical implementation.