Počet záznamů: 1
Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning
- 1.
SYSNO ASEP 0510111 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning Tvůrce(i) Liu, Y. (US)
Marcella, N. (US)
Timoshenko, J. (US)
Halder, A. (US)
Yang, B. (US)
Kolipaka, L. (US)
Pellin, M. J. (US)
Seifert, S. (US)
Vajda, Štefan (UFCH-W) RID, ORCID
Liu, P. (US)
Frenkel, A. I. (US)Číslo článku 164201 Zdroj.dok. Journal of Chemical Physics. - : AIP Publishing - ISSN 0021-9606
Roč. 151, č. 16 (2019)Poč.str. 7 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova XANES spectra ; Copper alloys ; metal ions Vědní obor RIV CF - Fyzikální chemie a teoretická chemie Obor OECD Physical chemistry Způsob publikování Omezený přístup Institucionální podpora UFCH-W - RVO:61388955 UT WOS 000500362000031 EID SCOPUS 85074148488 DOI 10.1063/1.5126597 Anotace Understanding the origins of enhanced reactivity of supported, subnanometer in size, metal oxide clusters is challenging due to the scarcity of methods capable to extract atomic-level information from the experimental data. Due to both the sensitivity of X-ray absorption near edge structure (XANES) spectroscopy to the local geometry around metal ions and reliability of theoretical spectroscopy codes for modeling XANES spectra, supervised machine learning approach has become a powerful tool for extracting structural information from the experimental spectra. Here, we present the application of this method to grazing incidence XANES spectra of size-selective Cu oxide clusters on flat support, measured in operando conditions of the methanation reaction. We demonstrate that the convolution neural network can be trained on theoretical spectra and utilized to “invert” experimental XANES data to obtain structural descriptors—the Cu–Cu coordination numbers. As a result, we were able to distinguish between different structural motifs (Cu2O-like and CuO-like) of Cu oxide clusters, transforming in reaction conditions, and reliably evaluate average cluster sizes, with important implications for the understanding of structure, composition, and function relationships in catalysis. Pracoviště Ústav fyzikální chemie J.Heyrovského Kontakt Michaela Knapová, michaela.knapova@jh-inst.cas.cz, Tel.: 266 053 196 Rok sběru 2020 Elektronická adresa http://hdl.handle.net/11104/0300663
Počet záznamů: 1