In case study 1 we aim to show that this method can be used to detect damage due to cracking and void formation in NMC particles by comparing the pristine and 4.5 V SOC images. In this case the images were not registered, therefore particles grey-level cannot be tracked in specific particles at different SoC. Therefore, we are interested in the detection of damaged regions within particles, rather than the distribution of grey-levels between particles. This means we are interested in detecting regions within particles that have significantly lower grey-level than the surrounding material. For example, the dark patch in the centre of figure .d. In order to do this, radial greylevels from each particle were extracted and normalised such that the highest radial layer grey-level value is normalised to 1 and the radial grey-level at the particle surface is normalised to 0. Across the sample set of particles, a mean normalised grey-level for each radial layer was calculated. A plot of such normalised mean radial grey-levels is shown in figure . In this example the set of particles was sub-set to just include the largest quartile of particles. In figure there is a trend by which radial layers toward the centre of the cycled particles have, on average, relatively lower grey-levels compared to the same region in the pristine dataset. This is characteristic of damage in these materials, where we expect to see lower grey-level cracks and voids appear in the 4.5 V SoC particles, with these emanating from the particle centres . Furthermore, the SE in radial layer grey-levels in the 4.5 V SoC image is higher, particularly towards the centre of the particles, this shows that average grey-level in radial layers is quite inconsistent compared to the pristine state, suggesting a varying amount of damage between particles. This underlines the importance of using characterisation techniques with good sampling statistics in terms of the number of particles under study.