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Quantitative assessment of machine-learning segmentation of battery electrode materials for active material quantification

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dc.contributor.authorBailey, Josh J.
dc.contributor.authorWade, Aaron
dc.contributor.authorBoyce, Adam M.
dc.contributor.authorZhang, Ye Shui
dc.contributor.authorBrett, Dan J.L.
dc.contributor.authorShearing, Paul R.
dc.contributor.institutionUniversity of Aberdeen.Engineeringen
dc.date.accessioned2023-08-18T14:51:00Z
dc.date.available2023-08-18T14:51:00Z
dc.date.issued2023-02-15
dc.descriptionFunding Information: This work was made possible by the facilities and support provided by the Research Complex at Harwell. The research was funded by The Faraday Institution [grant numbers: EP/S003053/1, FIRG015, FIRG025]. PRS and DJLB acknowledge the Royal Academy of Engineering for supporting their respective Research Chairs [CiET1718/59 and RCSRF2021/13/53]. Funding Information: This work was made possible by the facilities and support provided by the Research Complex at Harwell . The research was funded by The Faraday Institution [grant numbers: EP/S003053/1 , FIRG015 , FIRG025 ]. PRS and DJLB acknowledge the Royal Academy of Engineering for supporting their respective Research Chairs [ CiET1718/59 and RCSRF2021/13/53 ]. Publisher Copyright: © 2022 The Authorsen
dc.description.statusPeer revieweden
dc.format.extent12
dc.format.extent7556055
dc.identifier273238363
dc.identifier9491c00e-b6d4-474b-8c0c-27180a58572e
dc.identifier85144390328
dc.identifier.citationBailey, J J, Wade, A, Boyce, A M, Zhang, Y S, Brett, D J L & Shearing, P R 2023, 'Quantitative assessment of machine-learning segmentation of battery electrode materials for active material quantification', Journal of Power Sources, vol. 557, 232503. https://doi.org/10.1016/j.jpowsour.2022.232503en
dc.identifier.doi10.1016/j.jpowsour.2022.232503
dc.identifier.issn0378-7753
dc.identifier.otherORCID: /0000-0003-0095-3015/work/140899107
dc.identifier.urihttps://hdl.handle.net/2164/21501
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85144390328&partnerID=8YFLogxKen
dc.identifier.vol557en
dc.language.isoeng
dc.relation.ispartofJournal of Power Sourcesen
dc.subjectSDG 7 - Affordable and Clean Energyen
dc.subjectAnodesen
dc.subjectCathodesen
dc.subjectLithium-ion batteriesen
dc.subjectMachine-learning segmentationen
dc.subjectX-ray computed tomographyen
dc.subjectTA Engineering (General). Civil engineering (General)en
dc.subjectRenewable Energy, Sustainability and the Environmenten
dc.subjectEnergy Engineering and Power Technologyen
dc.subjectPhysical and Theoretical Chemistryen
dc.subjectElectrical and Electronic Engineeringen
dc.subject.lccTAen
dc.titleQuantitative assessment of machine-learning segmentation of battery electrode materials for active material quantificationen
dc.typeJournal articleen

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