The morphology of the electrolyte‐filled pore space in lithium‐ion batteries is determined by the solid microstructure formed by μm‐sized active material particles and the smaller‐featured carbon binder domain (CBD). Tomographic reconstructions have largely neglected the CBD, resulting in inadequately defined pore space morphologies at odds with experimental ionic tortuosity values. We present a three‐phase reconstruction of a LiCoO₂ composite cathode by focused ion‐beam scanning electron microscopy tomography. Morphological analysis proves that the reconstruction, which combines an unprecedented volume (20 μm minimum edge length) with the hitherto highest resolution (13.9×13.9×20 nm³ voxel size), represents the cathode's pore space morphology. Pore‐scale diffusion simulations show consideration of the resolved CBD as indispensable to reproduce ionic tortuosity values from electrochemical impedance spectroscopy. Our results reveal the CBD as a convoluted network that dominates the pore space morphology and limits Li⁺ transport through tortuous and constricted diffusion pathways.

Summary: Combining scanning electron microscopy with serial slicing by a focused ion beam yields spatial image data of materials structures at the nanometer scale. However, the depth of field of the scanning electron microscopic images causes unwanted effects when highly porous structures are imaged. Proper spatial reconstruction of such porous structures from the stack of microscopic images is a tough and in general yet unsolved segmentation problem. Recently, machine learning methods have proven to yield solutions to a variety of image segmentation problems. However, their use is hindered by the need of large amounts of annotated data in the training phase. Here, we therefore replace annotated real image data by simulated image stacks of synthetic structures – realizations of stochastic germ–grain models and random packings. This strategy yields the annotations for free, but shifts the effort to choosing appropriate stochastic geometry models and generating sufficiently realistic scanning electron microscopic images.