pyIAST: Ideal adsorbed solution theory (IAST) Python package

Abstract

Ideal adsorbed solution theory (IAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of purecomponent adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of components. pyIAST supports several common analytical models to characterize the pure-component isotherms from experimental or simulated data. Alternatively, pyIAST can use numerical quadrature to compute the spreading pressure for IAST calculations by interpolating the purecomponent isotherm data. pyIAST can also perform reverse IAST calculations, where one seeks the required gas phase composition to yield a desired adsorbed phase composition.

On the use of the IAST method for gas separation studies in porous materials with gate-opening behavior

“…We performed the pure IAST calculations using the PyIAST Python package. [33] For the IAST calculations, we did not fit the isotherms to a specific model, but rather the IAST equations were solved by numerical integration and interpolation between experimental data points. At partial pressures higher than the last point in the experimental isotherm, that last point was used as saturation uptake.…”

Noria: A Highly Xe‐Selective Nanoporous Organic Solid

“…IAST can accurately predict binary Xe/Kr adsorption isotherms in MOFs from pure‐component Xe and Kr adsorption isotherms. Herein, we employ IAST to estimate the Xe/Kr adsorption selectivity for noria (see the Supporting Information, section 5) by using pyIAST . For a 20/80 mol % Xe/Kr mixture at 1 bar and 298 K, relevant to replacing the conventional cryogenic distillation process to obtain pure Xe and pure Kr, the IAST Xe/Kr selectivity of Noria is 9.4.…”

“…[12] IAST can accurately predict binaryX e/Kr adsorption isotherms in MOFs from pure-component Xe and Kr adsorption isotherms.H erein, we employI AST to estimate the Xe/Kr adsorption selectivity for noria (see the Supporting Information, section 5) by using pyIAST. [13] For a2 0/80 mol % Xe/Kr mixture at 1bar and 298 K, relevant to replacingt he conventional cryogenic distillation process to obtain pure Xe and pure Kr,t he IAST Xe/Kr selectivityo fN oria is 9.4. At dilute conditions (e.g.,4 0ppm Xe, 400 ppm Kr,r elevant to UNF reprocessing), we took the ratio of the Henry coefficients of Xe and Kr in noria to predict itsX e/Kr selectivity,w hich is consistent with IAST.N oria exhibits aX e/Kr selectivity of 9.4 at dilute conditions (see the Supporting Information, section5), higher than benchmark MOFs Ni-MOF-74, [8l] HKUST-1, [8d] [8b] and IRMOF-1 [8l] (Figure 3) and slightly lower than CC3 and SBMOF-1, the two leadings orbents for Xe/Kr separation from nuclearr eprocessing applications.N oria also has ar easonably high Xe Henry coefficient compare to CC3 and SBMOF-1.…”

The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation

“…(Such a use is already envisioned via example tools in the NIST-ISODB application that integrate its isotherm API functions with the pyIAST software package. 300,301 ) Similarly, integration of NIST-ISODB and NIST-MATDB with chemical insight into adsorbents (e.g., via the CSD) could be leveraged to identify families of MOFs that could be the starting point for computational material evolution toward specific performance metrics via genetic algorithm-driven mutation of those MOF coupled with computational evaluation of the offspring materials for various material properties and adsorption characteristics. Such approaches are similar in principal to the computational screening of the hMOFs set by Snurr and co-workers, 213,302,303 though with experimental adsorption isotherm data as a starting point.…”