2026-10-10T09:05:49Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/wx033rkaeuzp5ynr2026-10-09T11:00:23Zradar4kit

https://dx.doi.org/10.35097/wx033rkaeuzp5ynr Almeida de Campos, Leonardo Sheppard, Thomas L. Grunwaldt, Jan-Dierk Pore Network Model Metrics Extraction Tool Using SNOW2 and OpenPNM from Segmented 3D Volumes Karlsruhe Institute of Technology 2026 Chemistry dataset Software info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc/4.0/legalcode This dataset contains a Python-based workflow for extracting pore network model (PNM) metrics from segmented 3D tomographic volumes. The workflow integrates PoreSpy (SNOW2 algorithm) for pore network extraction and OpenPNM for the computation of structural and transport properties. The tool allow the calculation of porosity, pore size Distribution, throat size, and coordination number from labeled volumetric data. Input data must be provided as 3D labeled TIFF volumes, where labels correspond to: 0 = outside domain, 1 = solid phase, and 2 = pore phase. This repository represents the archived version associated with the corresponding scientific work. The actively maintained version of the code is available in the linked GitHub repository. LINK: "https://github.com/Leonardolac97/pore-network-model-metrics-extraction-tool-using-snow2-and-openpnm-from-segmented-3d-volumes.git" The workflow is implemented in a Jupyter Notebook and uses a single input file: a 3D TIFF (segmented volume). Data can be loaded from local directories, and an output folder can be specified to generate results (e.g., plots, statistics, and HTML files). pore network modeling SNOW2 OpenPNM X-ray tomography porous materials pore metrics https://publikationen.bibliothek.kit.edu/1000192032 application/x-tar