Abstract
Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (Python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the data origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: Brunauer–Emmett–Teller and Langmuir surface area, t and αs plots, pore size distribution calculations (Barrett–Joyner–Halenda, Dollimore–Heal, Horvath–Kawazoe, DFT/NLDFT kernel fitting), isosteric enthalpy calculations, ideal adsorbed solution theory calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and SQLite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.
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This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 641887 (Project Acronym: DEFNET).
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Source code: https://github.com/pauliacomi/pyGAPS; Documentation: https://pygaps.readthedocs.io/
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Iacomi, P., Llewellyn, P.L. pyGAPS: a Python-based framework for adsorption isotherm processing and material characterisation. Adsorption 25, 1533–1542 (2019). https://doi.org/10.1007/s10450-019-00168-5
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DOI: https://doi.org/10.1007/s10450-019-00168-5