A major issue to achieve a net-zero-emission economy is the topic of efficient energy storage . As the development of advanced energy storage materials for Li-ion progresses, material purity and manufacturing quality, become more important regarding to performance and lifetime. The distribution of material within the electrode coatings, homogeneous coating layers, and a good cell balancing are favorable to achieve a homogenous current distribution, good utilization of the active material and a long service life. Variations in microstructure on different scales can be responsible for the reduced lifetime and limited performance of batteries. Interesting features are deeply buried inside the components and efficient workflows are necessary to identify regions of interest and concentrate high-resolution methods on those areas. High-resolution imaging techniques like light microscopy and electron microscopy help to analyze the morphology of the used materials in a cell or the battery microstructure. However, these techniques are not destruction free and need an advanced sample preparation.
X-ray microscopy (XRM) can help to face these challenges. Conventional x-ray systems use a simple geometric magnification. This magnification is determined by the distances between the source, the sample and the detector. For a high resolution, the distance between the source and the sample has to be as small as possible. The space needed for the sample rotation limits the maximum magnification. XRM systems work with an additional optical magnification. This enables also high resolutions when the distance between the source and the sample is increased. Voxel resolution below 1 µm can be achieved and open a wide field of application especially in for the investigation of Li-ion batteries.
We demonstrate possible workflows for XRM to investigate whole Li-ion round cells to analyze the microstructure and identify cell-to-cell variances originated in the manufacturing process. We also demonstrate how XRM can visualize the change of the battery microstructure during cell ageing. Additionally we show how XRM can help to identify specific regions of interest within lager samples like electrodes. This helps to concentrate time consuming and high-resolution techniques like scanning electron microscopy and sample preparation with focused ion beam. These correlative workflows help to increase sample throughput and help to identify important correlations between the microstructure and performance of a Li-ion battery.