Elucidating the structure-dependent selectivity towards methane and ethanol of CuZn in the CO2 electroreduction using tailored Cu/ZnO precatalysts

Understanding the catalyst compositional and structural features that control selectivity is of uttermost importance to target desired products in chemical reactions. In this joint experimental–computational work, we leverage tailored Cu/ZnO precatalysts as a material platform to identify the intrinsic features of methane-producing and ethanol-producing CuZn catalysts in the electrochemical CO2 reduction reaction (CO2RR). Specifically, we find that Cu@ZnO nanocrystals, where a central Cu domain is decorated with ZnO domains, and ZnO@Cu nanocrystals, where a central ZnO domain is decorated with Cu domains, evolve into Cu@CuZn core@shell catalysts that are selective for methane (∼52%) and ethanol (∼39%), respectively. Operando X-ray absorption spectroscopy and various microscopy methods evidence that a higher degree of surface alloying along with a higher concentration of metallic Zn improve the ethanol selectivity. Density functional theory explains that the combination of electronic and tandem effects accounts for such selectivity. These findings mark a step ahead towards understanding structure–property relationships in bimetallic catalysts for the CO2RR and their rational tuning to increase selectivity towards target products, especially alcohols.

Identifier
Source https://archive.materialscloud.org/record/2021.165
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1051
Provenance
Creator Varandili, Seyedeh Behnaz; Stoian, Dragos; Vavra, Jan; Rossi, Kevin; Pankhurst, James R; Guntern, Yannick; Lopez, Nuria; Buonsanti, Raffaella
Publisher Materials Cloud
Publication Year 2021
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering