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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