Journal
CATALYSIS TODAY
Volume 336, Issue -, Pages 3-21Publisher
ELSEVIER
DOI: 10.1016/j.cattod.2018.10.071
Keywords
Operando XANES; Structure determination; Time dependent DFT; Finite difference method; Multivariate curve resolution; Machine learning
Funding
- Megagrant of the Russian Federation Government [14.Y26.31.0001]
- Russian Foundation for Basic Research [18-32-00856]
- Innovation Fund Denmark [5190-00018B]
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In the last decade the appearance of progressively more sophisticated codes, together with the increased computational capabilities, has made XANES a spectroscopic technique able to quantitatively confirm (or discard) a structural model, thus becoming a new fundamental diagnostic tool in catalysis, where the active species are often diluted metal centers supported on a matrix. After providing a brief historical introduction and the basic insights on the technique, in this review article, we provide a selection of four examples where operando XANES technique has been able to provide capital information on the structure of the active site in catalysts of industrial relevance: (i) Phillips catalyst for ethylene polymerization reaction; (ii) TS-1 catalyst for selective hydrogenation reactions; (iii) carbon supported Pd nanoparticles for hydrogenation reactions; (iv) Cu-CHA zeolite for NH3-assisted selective reduction of NOx and for partial oxidation of methane to methanol. The last example testifies how the multivariate curve resolution supported by the alternating least-squares algorithm applied to a high number of XANES spectra collected under operando conditions allows to quantitatively determine different species in mutual transformation. This approach is particularly powerful in the analysis of experiments where a large number of spectra has been collected, typical of time- or space-resolved experiments. Finally, machine learning approaches (both indirect and direct) have been applied to determine, from the XANES spectra, the structure of CO, CO2 and NO adsorbed on Ni2+ sites of activated CPO-27-Ni metal-organic framework.
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