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

Abstract

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 These applications share a common interest in analyzing the transport processes occurring at the pore-scale, since these ultimately control the observable macroscopic behavior 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 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.