4D Imaging of Lithium-Batteries Using Correlative Neutron and X-Ray Tomography with a Virtual Unrolling Technique

Donal Finegan, Ralf Ziesche, Tobias Arlt, Thomas Heenan, Alessandro Tengattini, Daniel Baum, Nikolay Kardjilov, Henning Markotter, Ingo Manke, Winfried Kockelmann, Dan Brett, Paul Shearing

Research output: Contribution to journalArticlepeer-review

104 Scopus Citations

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 languageAmerican English
Article numberArticle No. 777
Number of pages11
JournalNature Communications
Volume11
Issue number1
DOIs
StatePublished - 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

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