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Probing Active Sites in CuxPdy Cluster Catalysts by Machine-Learning-Assisted X‑ray Absorption Spectroscopy
journal contribution
posted on 2021-07-13, 16:15 authored by Yang Liu, Avik Halder, Soenke Seifert, Nicholas Marcella, Stefan Vajda, Anatoly I. FrenkelSize-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.