Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy.

Journal: Journal of the American Chemical Society
PMID:

Abstract

Single-atom catalytic sites may have existed in all supported transition metal catalysts since their first application. Yet, interest in the design of single-atom heterogeneous catalysts (SACs) only really grew when advances in transmission electron microscopy (TEM) permitted direct confirmation of metal site isolation. While atomic-resolution imaging remains a central characterization tool, poor statistical significance, reproducibility, and interoperability limit its scope for deriving robust characteristics about these frontier catalytic materials. Here, we introduce a customized deep-learning method for automated atom detection in image analysis, a rate-limiting step toward high-throughput TEM. Platinum atoms stabilized on a functionalized carbon support with a challenging irregular three-dimensional morphology serve as a practically relevant test system with promising scope in thermo- and electrochemical applications. The model detects over 20,000 atomic positions for the statistical analysis of important properties for establishing structure-performance relations over nanostructured catalysts, like the surface density, proximity, clustering extent, and dispersion uniformity of supported metal species. Good performance obtained on direct application of the model to an iron SAC based on carbon nitride demonstrates its generalizability for single-atom detection on carbon-related materials. The approach establishes a route to integrate artificial intelligence into routine TEM workflows. It accelerates image processing times by orders of magnitude and reduces human bias by providing an uncertainty analysis that is not readily quantifiable in manual atom identification, improving standardization and scalability.

Authors

  • Sharon Mitchell
    Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
  • Ferran Parés
    Barcelona Supercomputing Center (BSC), Plaça d'Eusebi Güell 1-3, 08034 Barcelona, Spain.
  • Dario Faust Akl
    Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
  • Sean M Collins
    School of Chemical and Process Engineering and School of Chemistry, University of Leeds, Leeds LS2 9JT, U.K.
  • Demie M Kepaptsoglou
    SuperSTEM Laboratory, SciTech Daresbury Campus, Daresbury WA4 4AD, U.K.
  • Quentin M Ramasse
    SuperSTEM Laboratory, SciTech Daresbury Campus, Daresbury WA4 4AD, U.K.
  • Dario Garcia-Gasulla
    Knowledge Engineering and Machine Learning Group, Universitat Politècnica de Catalunya (UPC), C/Jordi Girona 1-3, E-08034, Barcelona, Spain. dariog@cs.upc.edu.
  • Javier Pérez-Ramírez
    Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
  • Núria López
    Institute of Chemical Research of Catalonia and The Barcelona Institute of Science and Technology, 43007 Tarragona, Spain.