Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts.
Journal
Chemical science
ISSN: 2041-6520
Titre abrégé: Chem Sci
Pays: England
ID NLM: 101545951
Informations de publication
Date de publication:
06 Oct 2021
06 Oct 2021
Historique:
received:
10
08
2021
accepted:
27
08
2021
entrez:
27
10
2021
pubmed:
28
10
2021
medline:
28
10
2021
Statut:
epublish
Résumé
Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C
Identifiants
pubmed: 34703540
doi: 10.1039/d1sc04390k
pii: d1sc04390k
pmc: PMC8494033
doi:
Types de publication
Journal Article
Langues
eng
Pagination
12546-12555Informations de copyright
This journal is © The Royal Society of Chemistry.
Déclaration de conflit d'intérêts
There are no conflicts of interest to declare.
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