Precise Size Determination of Supported Catalyst Nanoparticles via Generative AI and Scanning Transmission Electron Microscopy.

Autor: Eliasson H; Electron Microscopy Center, Empa - Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, Dübendorf, 8600, Switzerland., Lothian A; Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, Linköping, 581 83, Sweden., Surin I; Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Vladimir-Prelog-Weg 1, Zürich, 8093, Switzerland., Mitchell S; Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Vladimir-Prelog-Weg 1, Zürich, 8093, Switzerland., Pérez-Ramírez J; Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Vladimir-Prelog-Weg 1, Zürich, 8093, Switzerland., Erni R; Electron Microscopy Center, Empa - Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, Dübendorf, 8600, Switzerland.
Jazyk: angličtina
Zdroj: Small methods [Small Methods] 2024 Oct 02, pp. e2401108. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1002/smtd.202401108
Abstrakt: 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 2 (Pt particles centered ≈2 nm) catalyst synthesized by impregnation, large datasets of experimental scanning transmission electron micrographs and physical image simulations are created to train a CycleGAN. A subsequent size-estimation network is developed to determine the nuclearity of imaged nanoparticles, providing plausible estimates for ≈70% of experimentally observed particles. This automatic approach enables precise size determination of supported nanoparticle-based catalysts overcoming crystal orientation limitations of conventional techniques, promising high accuracy with sufficient training data. Tools like this are envisioned to be of great use in designing and characterizing catalytic materials with improved atomic precision.
(© 2024 The Author(s). Small Methods published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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