Ilett, M, Wills, J, Rees, P et al. (5 more authors) (2020) Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media. Journal of Microscopy, 279 (3). pp. 177-184. ISSN 0022-2720
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Agglomeration; automated imaging; machine learning; nanoparticles |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/P00122X/1 EPSRC (Engineering and Physical Sciences Research Council) EP/R043388/1 EU - European Union 685817 |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Jan 2020 10:49 |
Last Modified: | 25 Jun 2023 22:06 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/jmi.12853 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154943 |