Formic acid is a liquid, safe, and energy-dense carrier for fuel cells. Above all, it can be sustainably produced from the electroreduction of CO2. The formic acid market is currently saturated, and it requires alternative applications to justify additional production capacity. Fuel cell technologies offer a chance to expand it, while creating an opportunity for sustainability in the energy sector. Formic acid-based fuel cells represent a promising energy supply system in terms of high theoretical open-circuit voltage (1.48 V). Compared to common fuel cells running on H2 (e.g., proton-exchange membrane fuel cells), formic acid has a lower storage cost and is safer. This review focuses on the sustainable production of formic acid from CO2 and on the detailed analysis of commercial examples of formic acid-based fuel cells, in particular direct formic acid fuel cell stacks. Designs described in the literature are mostly at the laboratory scale, still, with 301 W as the maximum power output achieved. These case studies are fundamental for the scale-up; however, additional efforts are required to solve crossover and increase performance.

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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This work investigates how batch reactors can be optimized to increase the yield of a desired product coupling two appealing techniques for process control and optimization: the nonlinear model predictive control

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