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.
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We acknowledge funding support from the US Department of Energy, Office of Basic Energy Sciences, under award no. DE-SC0002357 (programme manager J. Zhu). A.A.F. and M.C. acknowledge the European Union’s Horizon 2020 research and innovation programme for funding support through the European Research Council (grant agreement 772873, ‘ARTISTIC’ project). A.A.F. acknowledges the Institut Universitaire de France for the support. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DEAC36-08GO28308. Funding was provided by the US DOE Office of Vehicle Technology Extreme Fast Charge Program, programme manager S. Gillard. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. We also acknowledge the support of M. Scharf in the design of the illustrations. For the collection of the zinc battery CT data, we acknowledge the National Center for Microscopy and Imaging Research (NCMIR) technologies and instrumentation supported by grant R24GM137200 from the National Institute of General Medical Sciences. The AgO and Zn used in this work were provided by Riot Energy Inc., and LiNi0.5Mn1.5O4 was supplied by Haldor Topsoe. We also acknowledge support for the LiNi0.5Mn1.5O4 electrode fabrication by the Ningbo Institute of Materials Technology and Engineering (NIMTE) in China. This work was performed in part at the San Diego Nanotechnology Infrastructure (SDNI) of UCSD, NANO3, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (grant ECCS-1542148).
The authors declare no competing interests.
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Scharf, J., Chouchane, M., Finegan, D.P. et al. Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence. Nat. Nanotechnol. 17, 446–459 (2022). https://doi.org/10.1038/s41565-022-01081-9
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