Electrochemical reduction of CO 2 to value-added products is an attractive strategy to address issues of increasing atmospheric CO 2 concentration. Cu is the only pure metal catalyst known to electrochemically convert CO 2 to appreciable amounts of oxygenates and hydrocarbons such as C 2 H 5 OH, CH 4, and C 2 H 4, but the Faraday efficiencies are too low and the onset potentials are too high. To discover electrocatalytic systems better than Cu, we use in silico strategies based on new grand canonical potential (GCP) methods, but the complexity of the electrode–electrolyte interface makes it difficult to validate the accuracy of GCP. Operando electrochemical polarization-modulation infrared spectroscopy (PMIRS) provides a performance benchmark for theoretical tools that account for the vibrational stretching frequencies of surface-bound CO, ν CO, as a function of pH and applied potential U. We show here that GCP calculations of the surface coverages of H*, OH*, and CO* on Cu(100) as a function of U lead to excellent predictions of the potential-dependent ν CO and its shift with pH from 1 to 13. This validation justifies the use of GCP for predicting the performance of catalyst designs.
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Baricuatro et al. (2021) studied this question.
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