X-ray imaging with AI-driven super-resolution deep learning for investigating battery electrode microstructural properties over cycling

Mohammad‐Javad Shojaei; Guanting Li; Aditya Ramadas; Chun Huang

doi:10.1039/d5ta05257b

January 1, 2025Journal of Materials Chemistry AOpen Access

X-ray imaging with AI-driven super-resolution deep learning for investigating battery electrode microstructural properties over cycling

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Key Points

Microstructural properties indicate dynamic changes during cycling in cathodes.
Analysis confirmed using X-ray computed tomography, showcasing deep learning techniques.
Findings suggest pathways for improving battery technology and longevity.
Results underscore the importance of innovative imaging to advance energy storage materials.

Implication

Analysis reveals microstructural properties in battery cathodes during cycling, suggesting insights for LiNi 0.8 Mn 0.1 Co 0.1 O 2 performance.

Abstract

Rechargeable batteries are promising for transition to clean energy. This study investigates microstructural dynamics of LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)-based cathodes over cycling using X-ray computed tomography (XCT). There is a long-standing imaging...

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Cite This Study

Shojaei et al. (2025) studied this question.

synapsesocial.com/papers/6925572cc0ce034ddc35a81e https://doi.org/10.1039/d5ta05257b

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X-ray imaging with AI-driven super-resolution deep learning for investigating battery electrode microstructural properties over cycling

Key Points

Implication

Abstract

Discussion

Cite This Study

X-ray imaging with AI-driven super-resolution deep learning for investigating battery electrode microstructural properties over cycling

Key Points

Implication

Abstract

Discussion

Cite This Study

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