Abstract
The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the ‘unrolling’ provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.
Original language | American English |
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Article number | Article No. 777 |
Number of pages | 11 |
Journal | Nature Communications |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - 1 Dec 2020 |
Bibliographical note
Publisher Copyright:© 2020, The Author(s).
NREL Publication Number
- NREL/JA-5400-75811
Keywords
- image processing
- lithium ion batteries
- neuron imaging
- x-ray computed tomography