The microstructural degradation of a composite silicon electrode at different stages in its cycle life was investigated in 3D using X-ray nano-computed tomography. A reconstructed volume of 36 μm × 27 μm × 26 μm from the composite electrode was imaged in its pristine state and after 1, 10 and 100 cycles. Particle fracturing and phase transformation was observed within the electrode with increased cycling. In addition, a distinct, lower X-ray attenuating phase was clearly resolved, which can be associated with surface film formation resulting from electrolyte breakdown and with silicon particle phase transformation. Changes in quantified microstructural properties such as phase volume fraction and particle specific surface area were tracked. Electrode performance loss is associated with loss of active silicon. These imaging results further highlight the capability of high resolution X-ray tomography to investigate the role of electrode microstructure in battery degradation and failure.
How Amira-Avizo Software is used
Image processing and volume rendering of each of the reconstructed electrode datasets was carried out using the Avizo software package (v9.1). From within each 3D electrode dataset, a volume of interest (36 μm × 27 μm × 26 μm) was extracted for further analysis. An anisotropic diffusion filter was applied to the cropped 3D greyscale image datasets to minimize random image noise while preserving significant image features, after which a segmentation procedure combining thresholding and 3D region growing was implemented to distinguish between the solid and pore phases based on their greyscale intensity values.
3D quantification of the segmented datasets was also performed in Avizo software; phase volume fraction and volume-specific surface area values, which are important morphological parameters that determine electrode performance, were calculated. Phase volume fraction was calculated using a voxel counting approach as the ratio of the total number of voxels in a particular phase to the total number of voxels in the analysed volume. For surface area calculations, triangulated surface meshes were generated from the segmented image datasets using a marching cubes algorithm , and then subsequently smoothed using sub-voxel weights.