Automated image analysis for single-atom detection in catalytic materials by transmission electron microscopy

DOI: 10.1021/jacs.1c12466

Authors: Sharon Mitchell (ETH Zurich) , Ferran Parés (Barcelona Supercomputing Center (BSC)) , Dario Faust Akl (ETH Zurich) , Sean M. Collins (University of Leeds) , Demie M. Kepaptsoglou (SuperSTEM Laboratory; University of York) , Quentin M. Ramasse (SuperSTEM Laboratory; University of Leeds) , Dario Garcia-Gasulla (Barcelona Supercomputing Center (BSC)) , Javier Pérez-Ramírez (ETH Zurich) , Núria López (nstitute of Chemical Research of Catalonia; The Barcelona Institute of Science and Technology)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Journal Of The American Chemical Society

State: Published (Approved)
Published: March 2022
Diamond Proposal Number(s): 17997

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.

Journal Keywords: Catalysts; Imaging; Platinum; Metals; Materials

Subject Areas: Chemistry, Materials

Diamond Offline Facilities: Electron Physical Sciences Imaging Centre (ePSIC)
Instruments: E02-JEM ARM 300CF

Added On: 28/03/2022 09:20

Discipline Tags:

Physical Chemistry Catalysis Chemistry Materials Science

Technical Tags:

Microscopy Electron Microscopy (EM) Scanning Transmission Electron Microscopy (STEM)