2020
DOI: 10.1039/d0sc00382d
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Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks

Abstract: A neural network is used to reveal composition-dependent structural evolution under operando conditions in CuZn nanocatalysts for CO2 electroreduction.

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Cited by 66 publications
(96 citation statements)
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“…For example, Figure 1 a shows an absorption edge at ∼9.0 keV due to the absorption of X-ray photons by K-shell (1s) electrons of Cu in an oxidized Cu nanoparticle (NP). 25 In this case, the X-ray photons absorbed excite the core electrons of Cu to available unoccupied states in the valence band, resulting in the absorption edge, which then is referred to as Cu K-edge. The position of the absorption edge E 0 is element-specific, with the corresponding energies scaling approximately as Z 2 .…”
Section: Xas: Physical Principlesmentioning
confidence: 99%
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“…For example, Figure 1 a shows an absorption edge at ∼9.0 keV due to the absorption of X-ray photons by K-shell (1s) electrons of Cu in an oxidized Cu nanoparticle (NP). 25 In this case, the X-ray photons absorbed excite the core electrons of Cu to available unoccupied states in the valence band, resulting in the absorption edge, which then is referred to as Cu K-edge. The position of the absorption edge E 0 is element-specific, with the corresponding energies scaling approximately as Z 2 .…”
Section: Xas: Physical Principlesmentioning
confidence: 99%
“… (a) Cu K-edge XAS data for an as-prepared Cu nanocatalyst used for CO2RR (from ref ( 25 )). Schematics of the photoelectron scattering process, and pre-edge at the Cu K-edge are shown in the insets.…”
Section: Xas: Physical Principlesmentioning
confidence: 99%
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