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学術論文

Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning

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

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Haase,  Felix
Interface Science, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons267176

Rüscher,  Martina
Interface Science, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons227603

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

/persons/resource/persons232519

Herzog,  Antonia
Interface Science, Fritz Haber Institute, Max Planck Society;

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Hejral,  Uta
Interface Science, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons214068

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

/persons/resource/persons22020

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

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jacs.2c11824.pdf
(出版社版), 8MB

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引用

Timoshenko, J., Haase, F., Saddeler, S., Rüscher, M., Jeon, H., Herzog, A., Hejral, U., Bergmann, A., Schulz, S., & Roldan Cuenya, B. (2023). Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning. Journal of the American Chemical Society, 145(7), 4065-4080. doi:10.1021/jacs.2c11824.


引用: https://hdl.handle.net/21.11116/0000-000C-ACF6-D
要旨
Bimetallic transition-metal oxides, such as spinel-like CoxFe3–xO4 materials, are known as attractive catalysts for the oxygen evolution reaction (OER) in alkaline electrolytes. Nonetheless, unveiling the real active species and active states in these catalysts remains a challenge. The coexistence of metal ions in different chemical states and in different chemical environments, including disordered X-ray amorphous phases that all evolve under reaction conditions, hinders the application of common operando techniques. Here, we address this issue by relying on operando quick X-ray absorption fine structure spectroscopy, coupled with unsupervised and supervised machine learning methods. We use principal component analysis to understand the subtle changes in the X-ray absorption near-edge structure spectra and develop an artificial neural network to decipher the extended X-ray absorption fine structure spectra. This allows us to separately track the evolution of tetrahedrally and octahedrally coordinated species and to disentangle the chemical changes and several phase transitions taking place in CoxFe3–xO4 catalysts and on their active surface, related to the conversion of disordered oxides into spinel-like structures, transformation of spinels into active oxyhydroxides, and changes in the degree of spinel inversion in the course of the activation treatment and under OER conditions. By correlating the revealed structural changes with the distinct catalytic activity for a series of CoxFe3–xO4 samples, we elucidate the active species and OER mechanism.