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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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last_indexed 2020-10-29T04:07:29Z
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spelling 2020-10-28T11:27:07.7437453 v2 53134 2020-01-07 Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2020-01-07 MEDE 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. Journal Article Journal of Microscopy 279 3 177 184 Wiley 0022-2720 1365-2818 agglomeration, automated imaging, machine learning, nanoparticles 1 9 2020 2020-09-01 10.1111/jmi.12853 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2020-10-28T11:27:07.7437453 2020-01-07T13:58:01.5083995 College of Engineering Engineering M. ILETT 1 J. WILLS 2 Paul Rees 0000-0002-7715-6914 3 S. SHARMA 4 S. MICKLETHWAITE 5 A. BROWN 6 R. BRYDSON 7 N. HONDOW 8 53134__16220__09fc8811b3604257b0df91ba99b69862.pdf ILETT2019.pdf 2020-01-07T14:01:58.0356590 Output 2225319 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
spellingShingle Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
Paul Rees
title_short Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
title_full Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
title_fullStr Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
title_full_unstemmed Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
title_sort Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
author_id_str_mv 537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv 537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Paul Rees
author2 M. ILETT
J. WILLS
Paul Rees
S. SHARMA
S. MICKLETHWAITE
A. BROWN
R. BRYDSON
N. HONDOW
format Journal article
container_title Journal of Microscopy
container_volume 279
container_issue 3
container_start_page 177
publishDate 2020
institution Swansea University
issn 0022-2720
1365-2818
doi_str_mv 10.1111/jmi.12853
publisher Wiley
college_str College of Engineering
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hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
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description 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.
published_date 2020-09-01T04:07:19Z
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