Advanced battery cathode microstructure analysis through operando synchrotron X-ray computed tomography and super-resolution deep learning
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Lithium ion batteries are pivotal for clean energy storage and mitigating climate change. In this study, we employ operando synchrotron X-ray computed tomography to investigate the dynamic evolution of battery cathode microstructure. We focus on tracking changes in porosity and pore size distribution at the microscale and cathode thickness at the macroscale during the lithiation and delithiation processes within a commercially configured battery. Image quality was enhanced using both conventional image processing methods and a Super-Resolution Convolutional Neural Network (SRCNN) model. Our findings revealed a slight increase in the cathode solid volume fraction and specific surface area as the battery transitioned from its pristine state to fully lithiated, followed by a reduction during delithiation. This behavior was attributed to the expansion of the cathode material and phase transitions during lithiation, which split larger pores into smaller ones, as evidenced by the increase in surface area. Cathode thickness also exhibited expansion during lithiation and contraction during delithiation. These results offer valuable insights into the structural changes that contribute to battery aging, helping researchers better understand how these different parameters change over time. This understanding is crucial for designing more durable and sustainable batteries in the future, both in terms of specific design and material selection, to enhance resistance during charge and discharge cycles to improve performance and longevity.
Date Issued
2025-04-01
Date Acceptance
2025-02-25
Citation
Solid State Ionics, 2025, 422
ISSN
0167-2738
Publisher
Elsevier
Journal / Book Title
Solid State Ionics
Volume
422
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.ssi.2025.116818
Publication Status
Published
Article Number
116818
Date Publish Online
2025-03-05