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- 1.0544599 - ÚFCH JH 2022 RIV US eng J - Journal Article
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
EU Projects: European Commission(XE) 810310 - J. Heyrovsky Chair
Institutional support: RVO:61388955
Keywords : deep learning * machine learning * nanocatalysts * nanoclusters * size-selected clusters * xanes
OECD category: Physical chemistry
Impact factor: 10.383, year: 2021
Method of publishing: Limited access
Permanent Link: http://hdl.handle.net/11104/0321438File Download Size Commentary Version Access 0544599.pdf 5 5.5 MB Publisher’s postprint require 0544599preprint.pdf 0 5.3 MB Author’s postprint open-access - 2.0543566 - ÚFCH JH 2022 RIV US eng J - Journal Article
Halder, A. - Lenardi, C. - Timoshenko, J. - Mravak, A. - Yang, B. - Kolipaka, L. K. - Piazzoni, C. - Seifert, S. - Bonačić-Koutecký, V. - Frenkel, A. I. - Milani, P. - Vajda, Štefan
CO2 Methanation on Cu-Cluster Decorated Zirconia Supports with Different Morphology: A Combined Experimental In Situ GIXANES/GISAXS, Ex Situ XPS and Theoretical DFT Study.
ACS Catalysis. Roč. 11, č. 10 (2021), s. 6210-6224. ISSN 2155-5435. E-ISSN 2155-5435
EU Projects: European Commission(XE) 810310
Institutional support: RVO:61388955
Keywords : CO conversion 2 * copper cluster * dft * gisaxs * reaction mechanism * support effect * xanes * xps
OECD category: Physical chemistry
Impact factor: 13.700, year: 2021
Method of publishing: Limited access
Permanent Link: http://hdl.handle.net/11104/0320753File Download Size Commentary Version Access 0543566.pdf 3 8.6 MB Publisher’s postprint require - 3.0510111 - ÚFCH JH 2020 RIV US eng J - Journal Article
Liu, Y. - Marcella, N. - Timoshenko, J. - Halder, A. - Yang, B. - Kolipaka, L. - Pellin, M. J. - Seifert, S. - Vajda, Štefan - Liu, P. - Frenkel, A. I.
Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning.
Journal of Chemical Physics. Roč. 151, č. 16 (2019), č. článku 164201. ISSN 0021-9606. E-ISSN 1089-7690
EU Projects: European Commission(XE) 810310
Institutional support: RVO:61388955
Keywords : XANES spectra * Copper alloys * metal ions
OECD category: Physical chemistry
Impact factor: 2.991, year: 2019
Method of publishing: Limited access
Permanent Link: http://hdl.handle.net/11104/0300663File Download Size Commentary Version Access 0510111.pdf 1 2.6 MB open access Publisher’s postprint open-access