Adsorbate Configurations in Ni Single-Atom Catalysts during <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>CO</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> Elec

Nickel and nitrogen co-doped carbon (Ni-N-C) catalysts are attracting attention due to their exceptionally high performance in the electrocatalytic reduction of CO_{2}(CO_{2}RR) to CO. However, the direct experimental insight into the working mechanism of these catalysts is missing, hindering our fundamental understanding and their further improvement. This work sheds light on the nature of adsorbates forming under CO_{2}RR at singly dispersed Ni sites. In particular, operando high energy resolution fluorescence detected x-ray absorption near edge structure (HERFD-XANES) at the Ni K-edge together with valence-to-core x-ray emission spectroscopy (vtc-XES) and x-ray absorption (XAS) at the Ni L_{3}-edge were employed to unveil the structure and electronic properties of the reaction intermediates. These techniques, coupled with unsupervised and supervised machine learning methodologies and density functional theory, enabled a comprehensive characterization of the local atomistic and electronic structure of the working Ni-N-C catalysts. Specifically, we were able to distinguish between the structural and electronic changes of the Ni sites associated with the CO_{2}RR functionality from the effect of radiation-induced damage, providing direct insight into the bond formation between the Ni centers and CO_{2}RR intermediates such as CO adsorbates.

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Publication Details

Journal
Physical Review Letters
Published
2024-11-25
DOI
https://doi.org/10.1103/physrevlett.133.228001
Citations
10
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.17

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article

Adsorbate Configurations in Ni Single-Atom Catalysts during <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>CO</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> Elec

Arno Bergmann, Janis Timoshenko, Philipp Grosse, Clara Rettenmaier et al.
10 citations
Physical Review Letters
Machine Learning in Materials Science
1.17
article

Adsorbate Configurations in Ni Single-Atom Catalysts during <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>CO</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> Elec

Arno Bergmann, Janis Timoshenko, Philipp Grosse, Clara Rettenmaier, Beatriz Roldán Cuenya, Gabriele Deplano, Dorottya Hursán, Hyo Sang Jeon, Andrea Martini
article en
10 citations

Abstract

Nickel and nitrogen co-doped carbon (Ni-N-C) catalysts are attracting attention due to their exceptionally high performance in the electrocatalytic reduction of CO_{2}(CO_{2}RR) to CO. However, the direct experimental insight into the working mechanism of these catalysts is missing, hindering our fundamental understanding and their further improvement. This work sheds light on the nature of adsorbates forming under CO_{2}RR at singly dispersed Ni sites. In particular, operando high energy resolution fluorescence detected x-ray absorption near edge structure (HERFD-XANES) at the Ni K-edge together with valence-to-core x-ray emission spectroscopy (vtc-XES) and x-ray absorption (XAS) at the Ni L_{3}-edge were employed to unveil the structure and electronic properties of the reaction intermediates. These techniques, coupled with unsupervised and supervised machine learning methodologies and density functional theory, enabled a comprehensive characterization of the local atomistic and electronic structure of the working Ni-N-C catalysts. Specifically, we were able to distinguish between the structural and electronic changes of the Ni sites associated with the CO_{2}RR functionality from the effect of radiation-induced damage, providing direct insight into the bond formation between the Ni centers and CO_{2}RR intermediates such as CO adsorbates.

Physical Review LettersVol. 133(22)
Fritz Haber Institute of the Max Planck Society (DE), University of Turin (IT)
Alexander von Humboldt-Stiftung, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 18%
Machine Learning in Materials Science
1.17
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