Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems: Increasing the time efficiency and reproducibility for particle analysis of in situ catalysis data

Hamish Cavaye; Manfred Erwin Schuster

doi:10.1595/205651324X17060084998569

ISSN: 2056-5135

oa Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems

Increasing the time efficiency and reproducibility for particle analysis of in situ catalysis data
Authors: Hamish Cavaye¹ and Manfred Erwin Schuster²
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Affiliations: ¹ ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Chilton, Didcot, OX11 0QX, UK ² Johnson Matthey, Blounts Court, Sonning Common, Reading, RG4 9NH, UK
Email: *[email protected]; ^§[email protected]
Source: Johnson Matthey Technology Review, Volume 69, Issue 1, Jan 2025, p. 112 - 122
DOI: https://doi.org/10.1595/205651324X17060084998569
- Received: 07 Nov 2023
- Accepted: 22 Jan 2024

Abstract

The data analysis workflow for in situ electron microscopy experiments can require a significant amount of human-intensive and repetitive effort. The generation of Python-based scripts that incorporate simple machine learning algorithms are quite well established in biological sciences but not often utilised in the study of catalytic systems. Such scripted analysis is not only more efficient, but readily reproducible and allows a wide range of quantitative results to be reported, including but not limited to average and total particle size, particle counting and particle size distributions. In this work we utilise these tools to examine the effect of cycling reducing and oxidising atmospheres on copper oxide nanoparticles.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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/content/journals/10.1595/205651324X17060084998569

Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems

Johnson Matthey Technology Review 69, 112 (2025); https://doi.org/10.1595/205651324X17060084998569

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oa Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems

Increasing the time efficiency and reproducibility for particle analysis of in situ catalysis data

Abstract

Supplementary Material

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