Bridging Nano- and Microscale X-Ray Tomography for Battery Research by Leveraging Artificial Intelligence

Jonathan Scharf, Mehdi Chouchane, Donal Finegan, Bingyu Lu, Christopher Redquest, Min-cheol Kim, Weiliang Yao, Alejandro Franco, Dan Gostovic, Zhao Liu, Mark Riccio, Frantisek Zelenka, Jean-Marie Doux, Ying Meng

Research output: Contribution to journalArticlepeer-review

67 Scopus Citations

Abstract

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials’ absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

Original languageAmerican English
Pages (from-to)446-459
Number of pages14
JournalNature Nanotechnology
Volume17
Issue number5
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Limited.

NREL Publication Number

  • NREL/JA-5700-79500

Keywords

  • diagnostics
  • Li-ion batteries
  • machine learning
  • X-ray imaging

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