| Résumé | Strategies to capture and sequester ever-increasing anthropogenic CO₂ emissions include adsorbing CO₂ onto inorganic substrates and then storing it in reservoirs, changing land use to promote forestry, and converting CO₂ to chemicals and fuels. The reverse water–gas shift (RWGS) reaction is a conversion strategy for producing CO from CO₂ that provides the highest technology readiness level. Cu and alkali metals promote CO₂ adsorption, Fe improves the thermal stability, and reducible supports like CeO2 accelerate the reaction rate. Density functional theory (DFT) is a practical modeling tool for evaluating the catalytic properties of materials at the atomic scale. The active phases of the Cu- and Fe-based catalysts, the effect of bimetallic compositions, the presence of promotors, and the influence of the support material are evaluated using observations from DFT simulations and experimental data. An optimal RWGS catalyst favors (1) CO₂ adsorption, (2) the dissociation of CO₂ or intermediate carbonate species to CO, and (3) CO desorption. Typically, a single-component catalytic plane is unfavorable for all these criteria, thus necessitating the design of an optimal multicomponent RWGS catalyst. Future DFT research is directed toward multifacet catalytic systems to understand the structural configuration of a highly active RWGS system. Experimental and characterization results complement DFT studies in the design of the optimal RWGS catalyst. Machine learning trained by literature data provides an automated approach for the inverse design of high-performance, stable, and economic catalysts for the RWGS reaction. This review encompasses experimental and computational approaches to understand the activity of RWGS catalysts. |
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