Multi-modality imaging of temperature effects during processing on electrode microstructure and electrochemical performance in lithium-ion batteries
File(s)
Published version
Author(s)
Shojaei, Mohammad Javad
Zhao, Zhen
Huang, Chun
Type
Journal Article
Abstract
Electrode microstructure strongly influences the performance of lithium-ion batteries, yet linking manufacturing parameters, microstructure, and electrochemical behaviour remains challenging. Here, we develop a multi-modality imaging approach that combines in-line visible light imaging, X-ray computed tomography (XCT), and scanning electron microscopy (SEM). Real-time visible light imaging captures binder migration and transformation of the wet coating as it evolves into semi-solid structures. SEM reveals fine surface features of carbon-binder domain (transparent in XCT) and small pores, while XCT captures the three-dimensional rearrangement of active material particles. SEM–XCT image registration enables direct spatial correlation between the two modalities, highlighting the complementary advantages of each technique. A data-driven method is developed to generate realistic 3D electrode microstructure from a single 2D slice, showing good agreement with full 3D ground truth datasets. We further develop a super-resolution convolutional neural network (SRCNN) model to recover high-resolution detail from low-resolution scans. The SRCNN-resolved images reproduce the porosity and tortuosity to within 2.3% and 1.5%, respectively, compared with the experimental high-resolution ground truth. The trained SRCNN model is transferred to unseen dataset of an electrode dried at a different temperature without retraining, offering a route to shortening high-resolution acquisition. Among the LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes under initial drying temperatures of 40, 80, and 120 °C, we show 80 °C provides a balance between particle connectivity, pore-network continuity, structural stability, highest rate capability, and lowest impedance. This framework offers an efficient way for optimising electrode manufacturing conditions.
Date Issued
2026-10-01
Date Acceptance
2026-07-13
Citation
Materials Characterization, 2026, 240 (Part A)
ISSN
1044-5803
Publisher
Elsevier
Journal / Book Title
Materials Characterization
Volume
240
Issue
Part A
Copyright Statement
© 2026 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.matchar.2026.116782
Publication Status
Published
Article Number
116782
Date Publish Online
2026-07-21