posted on 2022-03-25, 14:40authored bySharon Mitchell, Ferran Parés, Dario Faust Akl, Sean M. Collins, Demie M. Kepaptsoglou, Quentin M. Ramasse, Dario Garcia-Gasulla, Javier Pérez-Ramírez, Núria López
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.