Bailey, Josh J.Wade, AaronBoyce, Adam M.Zhang, Ye ShuiBrett, Dan J.L.Shearing, Paul R.2023-08-182023-08-182023-02-15Bailey, 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.2022.2325030378-7753ORCID: /0000-0003-0095-3015/work/140899107https://hdl.handle.net/2164/21501Funding 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 Authors127556055engSDG 7 - Affordable and Clean EnergyAnodesCathodesLithium-ion batteriesMachine-learning segmentationX-ray computed tomographyTA Engineering (General). Civil engineering (General)Renewable Energy, Sustainability and the EnvironmentEnergy Engineering and Power TechnologyPhysical and Theoretical ChemistryElectrical and Electronic EngineeringTAJournal article10.1016/j.jpowsour.2022.232503https://www.scopus.com/pages/publications/85144390328