A High-Performance Rh-TMP-COF Photocatalyst for CO 2 -to-CO Conversion with H 2 O Vapor: From Descriptor Prediction to Experimental Validation

Inspired by natural photosynthesis, photocatalytic CO2 reduction coupled with water oxidation presents a promising approach for producing sustainable solar fuels and chemicals. However, persistently low efficiency stems from coupled kinetic-thermodynamic constraints within the photocatalytic system. Developing novel photocatalysts holds the key to overcoming these challenges, yet traditional trial-and-error approaches suffer from lengthy development cycles. Herein, we propose using two descriptors to evaluate the catalytic activity of metal-loaded covalent organic frameworks (COFs) for photocatalytic CO2 reduction: the catalyst's conduction band minimum (CBM) and the Gibbs-free energy change (ΔG) for forming the COOH intermediate during CO2-to-CO conversion. Through the descriptor-based screening of a series of metal-loaded COFs and the computational investigation of their excited-state properties, the Rh-loaded COF is identified as optimal. Experimentally synthesized Rh-TMP-COF exhibits a CO production rate of 421 μmol g-1 h-1, which positions it among the best-performing photocatalysts for overall CO2 reduction. Theoretical calculations and experimental verification further demonstrate that the Rh loading not only facilitates directional photogenerated electron migration from water oxidation sites to CO2 reduction sites but also significantly reduces the reaction energy barrier, thereby enhancing the reaction rate. This work establishes a descriptor-based methodology for predicting photocatalytic activity, providing a strategic framework for efficient photocatalyst development for overall CO2 reduction.