Work

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

Year: 2025

Type: article

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.

Source: Journal of Materials Chemistry A

DOI: 10.1039/d5ta05257b

Authors: Mohammad Javad Shojaei, Guanting Li, Aditya Ramadas, Chun Huang

Institutions: Imperial College London, Rutherford Appleton Laboratory, Research Complex at Harwell, The Faraday Institution

Language: English

Topic: Advanced X-ray Imaging Techniques

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funders: UK Research and Innovation, Faraday Institution, Imperial College London, Engineering and Physical Sciences Research Council