Probing active sites in Cux Pdy cluster catalysts by machine-learning-assisted X-ray absorption spectroscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F21%3A00544599" target="_blank" >RIV/61388955:_____/21:00544599 - isvavai.cz</a>

Výsledek na webu

<a href="http://hdl.handle.net/11104/0321438" target="_blank" >http://hdl.handle.net/11104/0321438</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acsami.1c06714" target="_blank" >10.1021/acsami.1c06714</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Probing active sites in Cux Pdy cluster catalysts by machine-learning-assisted X-ray absorption spectroscopy

Popis výsledku v původním jazyce

Size-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.

Název v anglickém jazyce

Probing active sites in Cux Pdy cluster catalysts by machine-learning-assisted X-ray absorption spectroscopy

Popis výsledku anglicky

Size-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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ACS Applied Materials and Interfaces

ISSN

1944-8244

e-ISSN

1944-8252

Svazek periodika

13

Číslo periodika v rámci svazku

45

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

53363-53374

Kód UT WoS článku

000752870800007

EID výsledku v databázi Scopus

2-s2.0-85111204787