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

Area of Science:

• Materials Science
• Catalysis
• Renewable Energy

Background:

• Photocatalytic CO2 reduction coupled with water oxidation offers a sustainable route to solar fuels.
• Low efficiency due to kinetic-thermodynamic constraints and lengthy catalyst development cycles hinder progress.

Purpose of the Study:

• To develop a descriptor-based methodology for efficient screening and design of metal-loaded covalent organic frameworks (COFs) for photocatalytic CO2 reduction.
• To identify optimal catalysts by evaluating conduction band minimum (CBM) and Gibbs-free energy (ΔG) for COOH intermediate formation.

Main Methods:

• Computational screening of metal-loaded COFs using two key descriptors (CBM and ΔG).
• Investigation of excited-state properties of candidate catalysts.
• Experimental synthesis and characterization of the most promising candidate (Rh-TMP-COF).

Main Results:

• Rh-loaded COF (Rh-TMP-COF) identified as an optimal photocatalyst through descriptor-based screening.
• Experimental CO production rate of 421 μmol g⁻¹ h⁻¹, demonstrating high performance for CO2 reduction.
• Rh loading enhances photogenerated electron migration and reduces reaction energy barriers.

Conclusions:

• A descriptor-based approach significantly accelerates the discovery of efficient photocatalysts.
• Rh-TMP-COF represents a highly effective photocatalyst for overall CO2 reduction.
• This methodology provides a strategic framework for designing advanced photocatalysts for solar fuel production.