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Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media

M. ILETT, J. WILLS, Paul Rees Orcid Logo, S. SHARMA, S. MICKLETHWAITE, A. BROWN, R. BRYDSON, N. HONDOW

Journal of Microscopy, Volume: 279, Issue: 3, Pages: 177 - 184

Swansea University Author: Paul Rees Orcid Logo

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DOI (Published version): 10.1111/jmi.12853

Abstract

For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe limitations in the analysis of the nanoparticle di...

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Published in: Journal of Microscopy
ISSN: 0022-2720 1365-2818
Published: Wiley 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53134
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Abstract: For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe limitations in the analysis of the nanoparticle dispersion by light scattering techniques. Here we present an alternative analysis and associated workflow which utilises electron microscopy. The need to collect large, statistically relevant datasets by imaging vacuum dried, plunge frozen aliquots of suspension was accomplished by developing an automated STEM imaging protocol implemented in an SEM fitted with a transmission detector. Automated analysis of images of agglomerates was achieved by machine learning using two free open‐source software tools: CellProfiler and ilastik. The specific results and overall workflow described enable accurate nanoparticle agglomerate analysis of particles suspended in aqueous media containing other potential confounding components such as salts, vitamins and proteins.
Keywords: agglomeration, automated imaging, machine learning, nanoparticles
College: Faculty of Science and Engineering
Issue: 3
Start Page: 177
End Page: 184

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