Depth‐Aware Machine Learning for 3D Reconstruction of Fibrous Porous Electrodes by FIB‐SEM Tomography

ABSTRACT

Quantitative three‐dimensional reconstruction of porous polymers with sub‐micrometer feature sizes is central to understanding and designing electrochemical interfaces and membranes. However, focused ion beam–scanning electron microscopy tomography combined with conventional segmentation techniques cannot readily reconstruct complex disordered morphologies such as fibers. Standard segmentation workflows misclassify out‐of‐plane material as in‐plane material, distorting the reconstructed pore network, fiber connectivity, and interface area. Here, we introduce an annotation‐free, depth‐aware machine‐learning framework that combines monocular depth estimation, trained on macroscale images, with unsupervised clustering to separate in‐plane polymer cross‐sections from subsurface contributions in serial FIB–SEM tomography images. Applied to copper‐coated electrospun poly(vinylidene fluoride‐co‐hexafluoropropylene) fiber mats, the approach produces 3D reconstructions that preserve fibrous morphology more faithfully than conventional supervised and unsupervised segmentation. Quantitative validation against capillary flow porometry, pycnometry, and high‐resolution wide‐angle X‐ray computed tomography demonstrates improved agreement across pore cross‐section diameter, fiber volume ratio, and Cu depth decrease metrics. This work establishes depth‐aware segmentation as a transferable strategy from real‐scale to nanoscale imaging, requiring no labeled training data and enabling accurate 3D segmentation and reconstruction from SEM images. This approach opens a path toward more reliable structure–property relationships in disordered porous polymer materials.