Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-26T20:53:54.576Z Has data issue: false hasContentIssue false
  • English
  • Français

Classification of Metal Nanoclusters Using Convolutional Neural Networks

Published online by Cambridge University Press:  22 July 2022

Malcolm Dearg ,
Henry P Hoddinott ,
Yubiao Niu ,
Richard E Palmer  and
Thomas JA Slater
Malcolm Dearg*
Affiliation:
Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, UK ePSIC, Diamond Light Source, Oxfordshire, UK
Henry P Hoddinott
Affiliation:
Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK
Yubiao Niu
Affiliation:
Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK
Richard E Palmer
Affiliation:
Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK
Thomas JA Slater*
Affiliation:
Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, UK ePSIC, Diamond Light Source, Oxfordshire, UK Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK
*
*Corresponding author: [email protected], [email protected]
*Corresponding author: [email protected], [email protected]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3000 - 3001
Check for updates
Copyright
Copyright © Microscopy Society of America 2022

References

Wells, D. M., et al. , Nanoscale 7 6498 (2015). doi: 10.1039/C4NR05811ACrossRefGoogle Scholar
Plant, S. R., et al. , JACS 136 21 7559 (2014). doi: 10.1021/ja502769vCrossRefGoogle Scholar
Pratontep, S., et al. , Rev Sci. Instr. 76 045103 (2005). doi: 10.1063/1.1869332CrossRefGoogle Scholar
Ede, J. M., Mach. Learn.: Sci. Technol. 2 011004 (2021). doi: 10.1088/2632-2153/abd614Google Scholar
Dhillon, A. & Verma, G. K., Prog. Artif. Intell. 9 85 (2020). doi: 10.1007/s13748-019-00203-0CrossRefGoogle Scholar
Rangel DaCosta, L., et al. , Micron 151 103141 (2021). doi: 10.1016/j.micron.2021.103141CrossRefGoogle Scholar
Slater, T., Bell, C. G., & Danaie, M.. (2021). ePSIC-DLS/particlespy: v0.6.0 (v0.6.0). doi:10.5281/zenodo.5094360CrossRefGoogle Scholar
We thank Diamond Light Source for access and support in use of the electron Physical Science Imaging Centre (Instrument E02, proposal number MG28449).Google Scholar
The authors acknowledge funding from the EPSRC (EP/V029797/1).Google Scholar