Understanding battery materials using advanced X-ray and correlative imaging methods
Paul Shearing a b
a The ZERO Institute, The University of Oxford, Holywell House, Oxford OX2 0EST
b The Faraday Institution, Quad One Becquerel Avenue Harwell, Didcot OX11 0RA
Proceedings of 24th International Conference on Solid State Ionics (SSI24)
London, United Kingdom, 2024 July 14th - 19th
Organizers: John Kilner and Stephen Skinner
Keynote, Paul Shearing, presentation 485
Publication date: 10th April 2024

Energy storage is critical to achieve ambitious targets for Net Zero where Li-ion and post Li-ion chemistries will be a cornerstone of decarbonisation efforts in a range of sectors.

 

To support the acceleration of energy storage technologies, the past decade has seen the rapid development and proliferation of three-dimensional X-ray imaging tools applied to batter materials and devices This provides a framework to improve the understanding of electrode morphology and its influence on transport processes, electrochemistry and mechanical behaviour. Moreover, through the application of image-based modelling tools, it has become possible to simulate a range of phenomena using a computational framework derived directly from tomographic images.

 

The non-destructive, and multi-scale characteristics of X-ray imaging tools provide benefits to quantify hierarchical complexity from the particle to the electrode and device level, where the portfolio of techniques available includes: absorption and phase contrast CT to understand multi-scale morphology; XRD-CT to reconcile chemical, crystallographic and morphological behaviour and, Bragg Coherent Diffraction Imaging to access sub-particle behaviour. Meanwhile complementary neutron, electron and ion beam imaging techniques can also leverage the benefits of the alternative contrast modes possible.

 

Here, we will review this progress and reflect on recent developments using multi-modal methods to understand the performance of advanced batteries. In concert, the portfolio of imaging and modelling tools provides a platform to explore the performance, degradation and failure of Li-ion batteries and to accelerate the development of post Li-ion chemistries.

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