  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. In the present paper, we consider several
approaches to segment 3D image data of two different cathodes into three
phases, namely active material, CBD and pores. More precisely, we focus on
global thresholding, a local closing approach based on EDX 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 FIB-SEM data representing the same electrode. We quantify the
impact of the considered segmentation approaches on morphological
characteristics as well as on the resulting performance by spatially-resolved
transport simulations. Furthermore, we use experimentally determined
electrochemical properties 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 ultra-thick 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.
