Imaging Catalytic Activation of CO2 on Cu2O (110) : A First-Principles Study
Balancing global energy needs against increasing greenhouse gas emissions requires new methods for efficient CO2 reduction. While photoreduction of CO2 is promising, the rational design of photocatalysts hinges on precise characterization of the surface catalytic reactions. Cu2O is a promising next-...
| Veröffentlicht in: | Chemistry of materials : a publication of the American Chemical Society. - 1998. - 30(2018) vom: 19. |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Chemistry of materials : a publication of the American Chemical Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Balancing global energy needs against increasing greenhouse gas emissions requires new methods for efficient CO2 reduction. While photoreduction of CO2 is promising, the rational design of photocatalysts hinges on precise characterization of the surface catalytic reactions. Cu2O is a promising next-generation photocatalyst, but the atomic-scale description of the interaction between CO2 and the Cu2O surface is largely unknown, and detailed experimental measures are lacking. In this study, density-functional theory (DFT) calculations have been performed to identify the Cu2O (110) surface stoichiometry that favors CO2 reduction. To facilitate interpretation of scanning tunneling microscopy (STM) and X-ray absorption near-edge structures (XANES) measurements, which are useful for characterizing catalytic reactions, we present simulations based on DFT-derived surface morphologies with various adsorbate types. STM and XANES simulations were performed using the Tersoff-Hamann approximation and Bethe-Salpeter equation (BSE) approach, respectively. The results provide guidance for observation of CO2 reduction reaction on, and rational surface engineering of, Cu2O (110). They also demonstrate the effectiveness of computational image and spectroscopy modeling as a predictive tool for surface catalysis characterization |
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| Beschreibung: | Date Revised 25.02.2020 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 0897-4756 |
| DOI: | 10.1021/acs.chemmater.7b04803 |