Finally, it may seem naive to expect to be able to reliably detect particle-level fluctuations of only a few 8-bit grey-level values when this is so close to the intensity resolution of an 8-bit image. However, it is important to consider that the values being reported here are average values calculated over many pixels in a particle. Many fluctuations in grey-level within the particle images are much larger, as shown in figure . In this figure, the pixel grey-level change vs. pristine for the same particle at different SoC is shown. The displayed images were calculated from first re-registering the particle onto itself at the different SoC (even though the image was already registered, re-registering just the particle bounding volume can be more accurate), then smoothing the images with a Gaussian filter, and finally subtracting the intersecting volumes of the pristine image from the SoC image. From this visualization of intraparticle damage it was observed that damage primarily occurred in the centre of the particle and that this damage was locally characterised by relatively large decreases in grey-level (∼ 40 grey-levels), thus demonstrating the ability of this technique to resolve intra-particle damage and showing that the technique is not limited by the intensity resolution of an 8-bit image. Understanding uncertainty is critical when analysing this data. Error bars are included in the figures (e.g., Figures and) to visually represent uncertainty. For example, in Figure , ±3 standard errors (SE) are plotted, indicating a 99.7% confidence interval for the population mean. Uncertainty in this study is categorized into two main types: measurement uncertainty and sampling uncertainty. These are combined to the total uncertainty (SE tot ) using the specific equations described below.