A variety of transition metal binary compounds, whose reaction mechanism involves intercalation-initiated conversion, have been extensively studied as anode materials in lithium ion batteries (LIBs). Although the introduction of carbonaceous materials such as carbon nanotubes, graphene, or a carbon layer solved issues arising from the conversion reaction during repetitive cycles, a perfect electrical contact of the carbonaceous material with the discharge products on a few-nanometer scale has been rarely accomplished. Moreover, most of the previous studies have focused on maximizing the electrochemical performance without an in-depth understanding of the fundamental effect of each component in the nanocomposite. Herein, an ordered mesoporous tungsten oxide/carbon composite with ultra-highly dispersed carbon over a few-nanometer scale is prepared by the self-assembly of a block copolymer with inorganic/carbon precursors. The confinement effect of tungsten oxide within the nanowalls (∼10 nm) is comprehensively investigated by electrochemical transient analysis and various ex situ analytic methods including X-ray diffraction and X-ray absorption spectroscopy. The resulting electrode provides an excellent cycle and rate performance owing to the highly conductive and stable matrix that endures repetitive conversion reactions.

The optimization of geometrical pore control in high-capacity Ni-based cathode materials is required to enhance the cyclic performance of lithium-ion batteries. Enhanced porosity improves lithium-ion mobility by increasing the electrode–electrolyte contact area and reducing the number of ion diffusion pathways. However, excessive porosity can diminish capacity, thus necessitating optimizing pore distribution to compromise the trade-off relation. Accordingly, a statistically meaningful porosity estimation of electrode materials is required to engineer the local pore distribution inside the electrode particles. Conventional scanning electron microscopy (SEM) image-based porosity measurement can be used for this purpose. However, it is labor-intensive and subjected to human bias for low-contrast pore images, thereby potentially lowering measurement accuracy. To mitigate these difficulties, we propose an automated image segmentation method for the reliable porosity measurement of cathode materials using deep convolutional neural networks specifically trained for the analysis of porous cathode materials. Combined with the preprocessed SEM image datasets, the model trained for 100 epochs exhibits an accuracy of > 97% for feature segmentation with regard to pore detection on the input datasets. This automated method considerably reduces manual effort and human bias related to the digitization of pore features in serial section SEM image datasets used in 3D electron tomography.Graphical abstract