Poster Presentation at Microscopy & Microanalysis 2024

Three-dimensional (3D) characterizations of battery electrodes and active materials are essential to understanding their microstructure. This in turn provides essential insight into the cell performance. During battery cycling, particle morphological change, including cracking and disintegration, can lead to cell degradation. These morphological degradations can range from micrometers to nanometers in scale. Plasma based focused ion beam-scanning electron microscopy (PFIB-SEM) tomography can access representative volume efficiently, while it is able to provide dataset with nm scale spatial resolution and hundreds of µm field of view. Though the tomography techniques are well developed, gaining quantitative information from the representative 3D volume data is still demanding.

In this work, a used commercial lithium-ion battery was disassembled, and its electrode stack was first mechanically cut and polished. Then, a stack of cross-sectional SEM images was collected using a ThermoFisher Helios 5 PFIB UXe. With this dataset, we demonstrated a machine-learning assisted imaging analysis approach to reveal the microstructure and nanostructure of a cathode electrode and individual particles from a lithium-ion battery. Potentially, this method can be applied to understand local heterogeneity within one battery to dig out the cell degradation mechanism. Additionally, it can also be used to compare the particle morphology of the same type of batteries before and after cycling to find out the courses to capacity fade.