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

A deep learning framework converts X-ray computed tomography (XCT) images with low-resolution and large field of view (FoV) into XCT images with high-resolution and large FoV, enabling visualization of fine microstructure over representative areas.

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Publication Details

Journal
Journal of Materials Chemistry A
Published
2025-11-11
DOI
https://doi.org/10.1039/d5ta05257b
Primary Topic
Advancements in Battery Materials
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Journal of Materials Chemistry A
Advancements in Battery Materials
article

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

article en

Abstract

A deep learning framework converts X-ray computed tomography (XCT) images with low-resolution and large field of view (FoV) into XCT images with high-resolution and large FoV, enabling visualization of fine microstructure over representative areas.

Journal of Materials Chemistry AVol. 14(2)
Rutherford Appleton Laboratory (GB), Research Complex at Harwell (GB), The Faraday Institution (GB), Imperial College London (GB)
UK Research and Innovation, Imperial College London, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 63%
Advancements in Battery Materials
DOIOpen Access PDFOpenAlex
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X-ray imaging with AI-driven super-resolution deep learning for investigating battery electrode microstructural properties over cycling · Journal of Materials Chemistry A (2025) | TGRS Research Map | TGRS