Bridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence

TL;DR

This paper reviews how advanced nano and micro-scale X-ray tomography combined with AI/ML techniques enhances battery material analysis, enabling detailed 3D imaging and predictive modeling for improved battery performance understanding.

Contribution

It introduces the integration of multi-scale X-ray CT imaging with AI/ML analysis to develop predictive models of battery behavior, bridging nano and micro-scale imaging techniques.

Findings

NanoCT systems achieve 50 nm resolution for battery imaging

AI/ML techniques enable detailed morphological analysis

Multi-scale CT imaging supports predictive battery models

Abstract

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.