Deep‐Learning‐Enabled Crack Detection and Analysis in Commercial Lithium‐Ion Battery Cathodes (Adv. Funct. Mater. 39/2022). (26th September 2022)

• Record Type: Journal Article
• Authors: Fu, Tianyu; Monaco, Federico; Li, Jizhou; Zhang, Kai; Yuan, Qingxi; Cloetens, Peter; Pianetta, Piero; Liu, Yijin
• Is Part Of: Advanced functional materials. Volume 32:Number 39(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-26
• Subjects: crack detection -- deep learning -- Li‐ion batteries -- phase contrast -- X‐ray holo‐tomography; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202270218
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract : Deep Learning In article number 2203070, Jizhou Li, Kai Zhang, Qingxi Yuan, Yijin Liu, and co‐workers demonstrate a deep learning‐based approach to effectively extract the crack patterns from nanoscale hard X‐ray holo‐tomography data of a piece of cathode from an electrochemically abused 18650‐type commercial battery. It is found that the crack distributions are associated with the cathode packing densities. A potentially viable architectural design for suppressing the structural degradation is proposed.