A Flexible Open-Source Mass Transport Framework for Analyzing Dynamic Outgassing Experiments

Mass transport modeling plays a critical role in characterizing volatile organic compound (VOC) emissions from materials in applications ranging from indoor air quality to food packaging. Despite their widespread use, existing models often lack accessibility because source code is not provided or lack flexibility due to the reliance on Laplace transform methods that cannot accommodate complex experimental conditions. Here, we present a versatile open-source framework in Python that addresses these limitations. The framework employs a mass-conservative finite-volume formulation of the method of lines (MOL) with efficient ODE solvers to model diffusion and mass transport in slab, rod, and spherical geometries with arbitrarily time-varying temperature, flow, feed concentration, and chemical generation. We validate the framework against analytical solutions and reproduce prior literature results, demonstrating that extending the model to include a finite interfacial transfer coefficient can meaningfully improve fits to experimental sorption data. We further illustrate the framework’s utility through comprehensive analysis of multiple headspace extraction (MHE) and temperature-programmed desorption (TPD) experiments, leveraging its computational efficiency to perform high-resolution parameter sweeps and global multi-temperature fitting. Together, these capabilities make the framework an adaptable open-source tool for designing experiments and interpreting material emission and sorption data.