Precise Size Determination of Supported Catalyst Nanoparticles via Generative AI and Scanning Transmission Electron Microscopy.
ceria
cyclegan
generative AI
heterogeneous catalysis
nanoparticles
platinum
scanning transmission electron microscopy
Journal
Small methods
ISSN: 2366-9608
Titre abrégé: Small Methods
Pays: Germany
ID NLM: 101724536
Informations de publication
Date de publication:
02 Oct 2024
02 Oct 2024
Historique:
revised:
04
09
2024
received:
19
07
2024
medline:
3
10
2024
pubmed:
3
10
2024
entrez:
3
10
2024
Statut:
aheadofprint
Résumé
Transmission electron microscopy (TEM) plays a crucial role in heterogeneous catalysis for assessing the size distribution of supported metal nanoparticles. Typically, nanoparticle size is quantified by measuring the diameter under the assumption of spherical geometry, a simplification that limits the precision needed for advancing synthesis-structure-performance relationships. Currently, there is a lack of techniques that can reliably extract more meaningful information from atomically resolved TEM images, like nuclearity or geometry. Here, cycle-consistent generative adversarial networks (CycleGANs) are explored to bridge experimental and simulated images, directly linking experimental observations with information from their underlying atomic structure. Using the versatile Pt/CeO
Identifiants
pubmed: 39359026
doi: 10.1002/smtd.202401108
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2401108Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 200021_196381
Informations de copyright
© 2024 The Author(s). Small Methods published by Wiley‐VCH GmbH.
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