PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images

Porous materials play a central role in many technologies, from micron-thick electrodes used in batteries and fuel-cells (Karan, 2017) to kilometer-long geologic formations of interest in oil recovery, contaminant transport and CO2 sequestration (Kumar et al., 2005).These applications share a common interest in analyzing the transport processes occurring at the pore-scale, since these ultimately control the observable macroscopic behavior (Anovitz & Cole, 2015).Images of porous materials are a valuable source of information, since performance and structure are intimately linked through the topology and geometry of the media.A variety of techniques are available for imaging a material’s internal pore structure with sub-micron resolution, including X-ray tomography (Coles et al., 1998), ptychography (Dierolf et al., 2010), and FIB-SEM sectioning (Heaney, Vicenzi, Giannuzzi, & Livi, 2001).Each of these tools can provide exquisitely detailed images, and retrieving quantitative information from these images has become a vital tool in all areas of porous media research.