Deconstructing electrode pore network to learn transport distortion

The central premise of porous electrodes is to make more surface area available for reactions. However, the convoluted pore network of such reactors exacerbates the transport of reacting species. Tortuosity is a measure of such transport distortion and is conventionally expressed in terms of porosity (the fraction of electrode volume occupied by liquid-filled pores). Such an approach is overly simplistic and falls short of accounting for spatial variabilities characteristic of electrode samples. These networks are defined by multiple features such as size distribution, connectivity, and pore morphology, none of which are explicitly considered in a porosity based interpretation, thus limiting predictability. We propose a recourse using a two-point correlation function that deconstructs the pore network into its essential attributes. Such a quantitative representation is mapped to the transport response of these networks. Given the explicit treatment of pore network geometry, this approach provides a consistent treatment of three-dimensionalities such as inhomogeneity and anisotropy. Three-dimensional (3D) tomograms of Li-ion battery electrodes are studied to characterize the efficacy of the proposed approach. The proposed approach is applicable to abstracting effective properties related to different transport modes in porous fluid networks.