  X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging
technique where contrast originates from the materials' absorption
coefficients. Novel battery characterization studies on increasingly
challenging samples have been enabled by the rapid development of both
synchrotron and laboratory-scale imaging systems as well as innovative analysis
techniques. Furthermore, the recent development of laboratory nano-scale CT
(NanoCT) systems has pushed the limits of battery material imaging towards
voxel sizes previously achievable only using synchrotron facilities. Such
systems are now able to reach spatial resolutions down to 50 nm. 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 powerful Artificial Intelligence (AI)/Machine
Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific
morphological parameters enable the development of predictive physics-based
models that can provide valuable insights for battery engineering. These models
can predict the impact of the electrode microstructure on cell performances or
analyze the influence of material heterogeneities on electrochemical responses.
In this work, we review the increasing role of X-ray CT experimentation in the
battery field, discuss the incorporation of AI/ML in analysis, and provide a
perspective on how the combination of multi-scale CT imaging techniques can
expand the development of predictive multiscale battery behavioral models.
