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Probing active sites in Cux Pdy cluster catalysts by machine-learning-assisted X-ray absorption spectroscopy

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    SYSNO ASEP0544599
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleProbing active sites in Cux Pdy cluster catalysts by machine-learning-assisted X-ray absorption spectroscopy
    Author(s) Liu, Y. (US)
    Halder, A. (US)
    Seifert, S. (US)
    Marcella, N. (US)
    Vajda, Štefan (UFCH-W) RID, ORCID
    Frenkel, A. I. (US)
    Source TitleACS Applied Materials and Interfaces. - : American Chemical Society - ISSN 1944-8244
    Roč. 13, č. 45 (2021), s. 53363-53374
    Number of pages12 s.
    Languageeng - English
    CountryUS - United States
    Keywordsdeep learning ; machine learning ; nanocatalysts ; nanoclusters ; size-selected clusters ; xanes
    Subject RIVCF - Physical ; Theoretical Chemistry
    OECD categoryPhysical chemistry
    Method of publishingLimited access
    Institutional supportUFCH-W - RVO:61388955
    UT WOS000752870800007
    EID SCOPUS85111204787
    DOI10.1021/acsami.1c06714
    AnnotationSize-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.
    WorkplaceJ. Heyrovsky Institute of Physical Chemistry
    ContactMichaela Knapová, michaela.knapova@jh-inst.cas.cz, Tel.: 266 053 196
    Year of Publishing2022
    Electronic addresshttp://hdl.handle.net/11104/0321438
Number of the records: 1  

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