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Adsorbate Configurations in Ni Single-Atom Catalysts during CO2 Electrocatalytic Reduction Unveiled by Operando XAS, XES, and Machine Learning

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Martini,  Andrea       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Timoshenko,  Janis       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Grosse,  Philipp       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Rettenmaier,  Clara       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Hursán,  Dorottya       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Jeon,  Hyosang
Interface Science, Fritz Haber Institute, Max Planck Society;

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Bergmann,  Arno       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Roldan Cuenya,  Beatriz       
Interface Science, Fritz Haber Institute, Max Planck Society;

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PhysRevLett.133.228001.pdf
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Citation

Martini, A., Timoshenko, J., Grosse, P., Rettenmaier, C., Hursán, D., Deplano, G., et al. (2024). Adsorbate Configurations in Ni Single-Atom Catalysts during CO2 Electrocatalytic Reduction Unveiled by Operando XAS, XES, and Machine Learning. Physical Review Letters, 133(22): 228001. doi:10.1103/PhysRevLett.133.228001.


Cite as: https://hdl.handle.net/21.11116/0000-0010-4066-3
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 CO2⁢(CO2⁢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 CO2RR 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 L3-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 CO2⁢RR functionality from the effect of radiation-induced damage, providing direct insight into the bond formation between the Ni centers and CO2⁢RR intermediates such as CO adsorbates.