Quantitative Comparison of Different Approaches for Reconstructing the Carbon-Binder Domain from Tomographic Image Data of Cathodes in Lithium-Ion Batteries and Its Influence on Electrochemical Properties

It is well known that the spatial distribution of the carbon-binder domain (CBD) offers a large potential to further optimize lithium-ion batteries. However, it is challenging to reconstruct the CBD from tomographic image data obtained by synchrotron tomography. Herein, several approaches are considered to segment 3D image data of two different cathodes into three phases, namely, active material, CBD, and pores. More precisely, it is focused on global thresholding, a local closing approach based on energy-dispersive X-ray spectroscopy data, a k-means clustering method, and a procedure based on a neural network that has been trained by correlative microscopy, i.e., based on data gained by synchrotron tomography and focused ion beam scanning electron microscopy data representing the same electrode. The impact of the considered segmentation approaches on morphological characteristics as well as on the resulting performance by spatially resolved transport simulations is quantified. Furthermore, experimentally determined electrochemical properties are used to identify an appropriate range for the effective transport parameter of the CBD. The developed methodology is applied to two differently manufactured cathodes, namely, an ultrathick unstructured cathode and a two-layer cathode with varying CBD content in both layers. This comparison elucidates the impact of a specific structuring concept on the 3D microstructure of cathodes.