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Classification of metal nanoclusters using convolutional neural networks

DOI: 10.1017/S1431927622011230 DOI Help

Authors: Malcolm Dearg (Cardiff University) , Henry P. Hoddinott (Swansea University) , Yubiao Niu (Swansea University) , Richard E. Palmer (Swansea University) , Thomas J. A. Slater (Diamond Light Source)
Co-authored by industrial partner: No

Type: Conference Paper
Conference: Microscopy & Microanalysis 2022: C04 - Artificial Intelligence, Instrument Automation, and High-dimensional Data Analytics for Microscopy and Microanalysis
Peer Reviewed: No

State: Published (Approved)
Published: August 2022
Diamond Proposal Number(s): 28449

Abstract: Catalysis happens only at the surface of materials, this makes nanoparticles of particular interest in the field of catalysis because of their high surface-to-volume ratio. The exact atomic structure of nanoparticle surfaces is of particular importance in catalysis, and the expression of surface facets is largely governed by their overall structure. Typically, small metal nanoparticles will take one of three major structural isomers: decahedron, icosahedron or cuboctahedron. Determination of the structural isomer of a nanoparticle can be performed using high-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM). In this study, we are investigating size-selected gas-condensation magnetron sputtered clusters. In particular, we are interested in so-called “magic number” nanoparticles, which have a complete closed outer “shell” of atoms. Previous studies have attempted to manually count the number of each structural isomer, to calculate the relative abundance of each structure and therefore determine their relative potential energies. This is of interest to understand the magnetron conditions required to make specific surface facets for catalytic applications.

Subject Areas: Information and Communication Technology, Materials, Chemistry

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

Added On: 27/07/2022 09:51

Discipline Tags:

Artificial Intelligence Physical Chemistry Catalysis Information & Communication Technologies Chemistry Materials Science Nanoscience/Nanotechnology Data processing

Technical Tags:

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