On the use of the IAST method for gas separation studies in porous materials with gate-opening behavior
Guillaume Fraux (1), Anne Boutin (2), Alain H. Fuchs (1),, Fran\c{c}ois-Xavier Coudert (1) ((1) Chimie ParisTech, PSL Research, University, CNRS, Institut de recherche de Chimie Paris, 75005 Paris, France,, (2) \'Ecole Normale Sup\'erieure, PSL Research University

TL;DR
This paper critically examines the use of the IAST method for gas separation in flexible porous materials, revealing that it often overestimates selectivity and fails to account for gate-opening behavior, thus challenging its reliability in such systems.
Contribution
The study demonstrates that incorporating framework flexibility into thermodynamic models significantly alters predictions, highlighting limitations of the classical IAST approach for flexible materials.
Findings
IAST does not capture gate-opening behavior in flexible materials.
Standard IAST can overestimate selectivity by up to two orders of magnitude.
Framework flexibility must be included for accurate separation predictions.
Abstract
Highly flexible nanoporous materials, exhibiting for instance gate opening or breathing behavior, are often presented as candidates for separation processes due to their supposed high adsorption selectivity. But this view, based on "classical" considerations of rigid materials and the use of the Ideal Adsorbed Solution Theory (IAST), does not necessarily hold in the presence of framework deformations. Here, we revisit some results from the published literature and show how proper inclusion of framework flexibility in the osmotic thermodynamic ensemble drastically changes the conclusions, in contrast to what intuition and standard IAST would yield. In all cases, the IAST method does not reproduce the gate-opening behavior in the adsorption of mixtures, and may overestimates the selectivity by up to two orders of magnitude.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
