0544599 - ÚFCH JH 2022 RIV US eng J - Článek v odborném periodiku
Liu, Y. - Halder, A. - Seifert, S. - Marcella, N. - Vajda, Štefan - Frenkel, A. I.
Probing active sites in Cux Pdy cluster catalysts by machine-learning-assisted X-ray absorption spectroscopy.
ACS Applied Materials and Interfaces. Roč. 13, č. 45 (2021), s. 53363-53374. ISSN 1944-8244. E-ISSN 1944-8252
GRANT EU: European Commission(XE) 810310 - J. Heyrovsky Chair
Institucionální podpora: RVO:61388955
Klíčová slova: deep learning * machine learning * nanocatalysts * nanoclusters * size-selected clusters * xanes
Obor OECD: Physical chemistry
Impakt faktor: 10.383, rok: 2021 ; AIS: 1.608, rok: 2021
Způsob publikování: Omezený přístup
DOI: https://doi.org/10.1021/acsami.1c06714

Size-selected clusters are important model catalysts because of their narrow size and compositional distributions, as well as enhanced activity and selectivity in many reactions. Still, their structure-activity relationships are, in general, elusive. The main reason is the difficulty in identifying and quantitatively characterizing the catalytic active site in the clusters when it is confined within subnanometric dimensions and under the continuous structural changes the clusters can undergo in reaction conditions. Using machine learning approaches for analysis of the operando X-ray absorption near-edge structure spectra, we obtained accurate speciation of the CuxPdy cluster types during the propane oxidation reaction and the structural information about each type. As a result, we elucidated the information about active species and relative roles of Cu and Pd in the clusters.
Trvalý link: http://hdl.handle.net/11104/0321438