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Revealing the structure of the active sites for the electrocatalytic CO2 reduction to Co over Co single atom catalysts using operando XANES and machine learning

MPS-Authors
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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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Rüscher,  Martina
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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Monteiro,  Mariana C. O.       
Interface Science, Fritz Haber Institute, Max Planck Society;

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Liberra,  Eric
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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Citation

Martini, A., Timoshenko, J., Rüscher, M., Hursán, D., Monteiro, M. C. O., Liberra, E., et al. (2024). Revealing the structure of the active sites for the electrocatalytic CO2 reduction to Co over Co single atom catalysts using operando XANES and machine learning. Journal of Synchrotron Radiation, 31(4), 741-750. doi:10.1107/S1600577524004739.


Cite as: https://hdl.handle.net/21.11116/0000-000F-54F8-B
Abstract
Transition-metal nitrogen-doped carbons (TM-N-C) are emerging as a highly
promising catalyst class for several important electrocatalytic processes,
including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a
challenging task due to the fluctional, dynamic nature of these catalysts, and
scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray
absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.