Machine learning accelerates identification of lithiated phases in X-ray images of battery hosts

Santos et al. (2022) propose a machine learning-based approach to identify various lithiated phases across lengthscales in X-ray images of battery particles, thus enabling automatic interpretation of such information in much bigger datasets and creating opportunities to unravel previously inaccessib...

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Detalles Bibliográficos
Autores principales: Mistry, Aashutosh, Srinivasan, Venkat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768674/
https://www.ncbi.nlm.nih.gov/pubmed/36569544
http://dx.doi.org/10.1016/j.patter.2022.100654

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768674/
https://www.ncbi.nlm.nih.gov/pubmed/36569544
http://dx.doi.org/10.1016/j.patter.2022.100654

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