Publication date: 28th August 2024
The electrochemical reduction of CO2 into industrially valuable chemicals like ethylene and ethanol is one of the most promising strategies to mitigate the greenhouse gas effect and tackle global energy challenges. Currently, the production of these crucial C2 molecules is only achieved using Cu-based electrocatalysts. However, obtaining high selectivity and efficiency towards specific reduction products remains challenging due to the complex reaction pathways involved. In this regard, the adsorption of CO onto the catalyst surface represents a crucial step since it corresponds to a key intermediate for the formation of C-C bonds.
In this work, we first employ machine learning models (ML) to predict the adsorption energies of CO on Cu/M alloy surfaces, where M indicates different types of metal atom impurities. These ML models, trained on a dataset of adsorption energies calculated via Density Functional Theory (DFT) calculations, help us understand the interaction of CO with various alloy compositions and identify potential candidates for high-performance catalysts. Secondly, we investigate the kinetics and thermodynamics of CO dimerization on these alloy surfaces using DFT simulations at constant potential, by including an explicit water layer wetting the catalyst surface. Our results enable the identification of scaling relations for this critical reaction step by varying the metal species in the alloy, which in turn facilitates the rational design of more efficient and selective copper-based catalysts.
This project has received funding from the EU’s Horizon 2021 programme under the Marie Skłodowska-Curie Doctoral Networks (MSCA-DN) grant agreement No 101072830.
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