A High-Performance Rh-TMP-COF Photocatalyst for CO₂-to-CO Conversion with H₂O Vapor: From Descriptor Prediction to...

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.