[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123586-en":3,"doc-seo-123586-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123586,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Quantitative assessment of machine-learning segmentation of battery electrode materials for active material quantification - Optimised methodology for robust active material quantification","X-ray computed tomography (CT) is used to study lithium-ion battery electrode microstructures, yet reliable results depend on robust segmentation for valid active material volume fraction quantification. Multiple approaches using accessible machine-learning segmentation software are evaluated to minimize user-to-user variation across three annotators. Iterative manual training on limited tomogram cross-sectional slices is identified as an optimal variance–interaction balance and tested on lab CT data, with comparisons to correlated FIB/SEM slice-and-view tomography.","Journal of Power Sources 557 (2023) 232503  \nContents lists available at ScienceDirect  \nJournal of Power Sources  \njournal [homepage: www.elsevier.com/locate/jpowsour](homepage: www.elsevier.com/locate/jpowsour)  \n| Quantitative assessment of machine-learning segmentation of battery electrode materials for active material quantification |  |  |  |\n| --- | --- | --- | --- |\n| Josh J. Bailey a, b, c, Aaron Wade a, c, Adam M. Boyce a, c, Ye Shui Zhang a, c, d, Dan J.L. Brett a, c, Paul R. Shearing a, c, *\u003Cbr>a Electrochemical Innovation Lab, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK b School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, Northern Ireland, BT7 1NN, UK c The Faraday Institution, Quad One, Becquerel Ave, Harwell Campus, Didcot, OX11 0RA, UK\u003Cbr>d School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, UK |  |  |  |\n| H I G H L I G H T S |  |  |  |\n| • Machine-learning used to segment X-ray tomograms of lithium-ion battery electrodes.\u003Cbr>• Focused-ion-beam/scanning electron microscopy used as correlative imaging technique.\u003Cbr>• Phase fraction variation between users reduced compared with traditional methods.\u003Cbr>• 10–25% coverage on 5% of tomogram sufficient to reduce variation in phase fraction. |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Lithium-ion batteries\u003Cbr>X-ray computed tomography Machine-learning segmentation Cathodes\u003Cbr>Anodes |  | X-ray computed tomography (CT) is an important tool for studying battery electrode microstructures but relies on robust segmentation for validity. Here, several approaches to applying accessible machine-learning segmentation software to segment open-source lithium-ion battery (LIB) electrode tomograms are followed to identify the optimised methodology that minimises variation in active material volume fraction quantification across three users. Iterative, manual training across seven cross-sectional slices (\u003C5%) of a tomogram is identified as an optimal balance between variance and user interaction, where 10–25% of each slice was trained. This approach is applied to lab-based X-ray CT data and compared with data obtained by focused-ion beam/scanning electron microscopy slice-and-view tomography. Variation in active material volume fraction between users is lower for at least one of these two approaches (10% or 25%) when applied to raw LIB cathode tomograms, versus unsupervised techniques such as simple and watershed segmentations. On average, the absolute volume fraction values are closer to that acquired by the correlated technique, most closely matching for high-resolution data. The present analysis provides an optimised approach for using open-source software to apply machine-learning segmentation when quantifying active material volume fractions in cutting-edge LIB electrodes, providing a more robust route to active material quantification. |  |\n\n1. Introduction  \nLithium-ion batteries (LIB) play an increasingly vital role in our everyday lives, from personal electronics to portable power, but they also feature highly in our global transition towards net-zero emissions, in the face of climate change, energy security concerns, and rising local air pollution [1]. LIBs are a mature technology that will be even more  \nwidely applied as an energy storage solution as more intermittent renewable energy generation is implemented, alongside increasing demand from electric vehicle uptake [2]. Understanding how their microstructure and material properties influence performance and durability, in terms of thermal, electrochemical, and mechanical aspects, is vital in the pursuit of next-generation batteries that remain safe and affordable but have greater energy and power densities [3].  \nAbbreviations: LIB, Lithium-ion batteries; CT, Computed tomography; ML, Machine Learning.  \n* Corresponding author. Electrochemical Innovation Lab, Department of Chemical Engineering, Unive","cbCaiuEUmw6EhVy8","https://ap.wps.com/l/cbCaiuEUmw6EhVy8","pdf",7556059,1,12,"English","en",105,"# Highlights\n## Machine-learning segmentation and correlative imaging\n## Active material volume fraction quantification\n# Article Information\n## Keywords and abstract\n# Introduction\n## Importance of microstructure and tomographic validation\n## Tomography methods and limitations of stereology","[{\"question\":\"How do the ML segmentation results compare with correlated FIB-SEM imaging?\",\"answer\":\"For raw LIB cathode tomograms, at least one trained-slice option (10% or 25%) yields lower between-user variation than unsupervised techniques. Absolute volume fraction values are on average closer to those from the correlated FIB-SEM slice-and-view tomography, especially for high-resolution data.\"}]","Quantitative assessment of machine-learning segmentation of battery electrode materials for active material quantification - Optimised methodology for robust active material quantification | PDF",1785817476,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"quantitative-assessment-of-machine-learning-segmentation-of-battery-electrode-materials-for-active-material-quantification-optimised-methodology-for-robust-active-material-quantification","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/quantitative-assessment-of-machine-learning-segmentation-of-battery-electrode-materials-for-active-material-quantification-optimised-methodology-for-robust-active-material-quantification/123586/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How do the ML segmentation results compare with correlated FIB-SEM imaging?","Question",{"text":75,"@type":76},"For raw LIB cathode tomograms, at least one trained-slice option (10% or 25%) yields lower between-user variation than unsupervised techniques. Absolute volume fraction values are on average closer to those from the correlated FIB-SEM slice-and-view tomography, especially for high-resolution data.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":113},"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]