We report a selective and stable electrocatalyst utilizing non-noble metals consisting of Cu and Sn for the efficient and selective reduction of CO2 to CO over a wide potential range. The bimetallic electrode was prepared through the electrodeposition of Sn species on the surface of oxide-derived copper (OD-Cu). The Cu surface, when decorated with an optimal amount of Sn, resulted in a Faradaic efficiency (FE) for CO greater than 90% and a current density of −1.0 mA cm−2 at −0.6 V vs. RHE, compared to the CO FE of 63% and −2.1 mA cm−2 for OD-Cu. Excess Sn on the surface caused H2 evolution with a decreased current density. X-ray diffraction (XRD) suggests the formation of Cu-Sn alloy. Auger electron spectroscopy of the sample surface exhibits zero-valent Cu and Sn after the electrodeposition step. Density functional theory (DFT) calculations show that replacing a single Cu atom with a Sn atom leaves the d-band orbitals mostly unperturbed, signifying no dramatic shifts in the bulk electronic structure. However, the Sn atom discomposes the multi-fold sites on pure Cu, disfavoring the adsorption of H and leaving the adsorption of CO relatively unperturbed. Our catalytic results along with DFT calculations indicate that the presence of Sn on reduced OD-Cu diminishes the hydrogenation capability—i.e., the selectivity towards H2 and HCOOH—while hardly affecting the CO productivity. While the pristine monometallic surfaces (both Cu and Sn) fail to selectively reduce CO2, the Cu-Sn bimetallic electrocatalyst generates a surface that inhibits adsorbed H*, resulting in improved CO FE. This study presents a strategy to provide a low-cost non-noble metals that can be utilized as a highly selective electrocatalyst for the efficient aqueous reduction of CO2.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): Dr. Shahid Rasul
Acknowledgements: The research reported in this publication was supported by King Abdullah University of Science
and Technology (KAUST). Furthermore, we are also thankful to Dr. Shahid Rasul at KAUST for
the helpful discussions. We are also grateful for the computational resources acquired from
KAUST Supercomputing Laboratory using the supercomputer Shaheen II under the project