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  • Review Article
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Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence

Nature Nanotechnology volume 17, pages 446–459 (2022)Cite this article

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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.

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Fig. 1: History and trends of CT.
Fig. 2: Experimental trends of CT in the battery field.
Fig. 3: CT segmentation and analysis of battery systems.
Fig. 4: Relationship between experimental tomography data, cell model and computation of electrochemical data in battery systems.
Fig. 5: Correlative workflow analysis and modelling.

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Acknowledgements

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).

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Authors and Affiliations

  1. Department of Nano-Engineering, University of California San Diego, La Jolla, CA, USA

    Jonathan Scharf, Bingyu Lu, Min-cheol Kim, Jean-Marie Doux & Ying Shirley Meng

  2. Laboratoire de Réactivité et Chimie des Solides (LRCS), Université de Picardie Jules Verne, UMR CNRS 7314, Hub de l’Energie, Amiens, France

    Mehdi Chouchane & Alejandro A. Franco

  3. Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, Amiens, France

    Mehdi Chouchane & Alejandro A. Franco

  4. National Renewable Energy Laboratory, Golden, CO, USA

    Donal P. Finegan

  5. Department of Chemical Engineering, University of California San Diego, La Jolla, CA, USA

    Christopher Redquest

  6. Department of Materials Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Weiliang Yao

  7. Alistore-ERI European Research Institute, FR CNRS 3104, Hub de l’Energie, Amiens, France

    Alejandro A. Franco

  8. Institut Universitaire de France, Paris, France

    Alejandro A. Franco

  9. Thermo Fisher Scientific, Waltham, MA, USA

    Dan Gostovic, Zhao Liu, Mark Riccio & František Zelenka

  10. Sustainable Power and Energy Center (SPEC), University of California San Diego, La Jolla, CA, USA

    Ying Shirley Meng

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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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