The roles of bulk and surface thermodynamics in the selective adsorption of a confined azeotropic mixture
Katie L. Y. Zhou, Anna T. Bui, Stephen J. Cox

TL;DR
This study uses an ML-enhanced classical density functional theory to understand how bulk and surface thermodynamics influence the selective adsorption of azeotropic mixtures in confined spaces.
Contribution
It introduces a neural functional approach that combines a single-component reference with mean-field attractive interactions, enabling efficient evaluation of adsorption behavior.
Findings
Pore becomes unselective at the bulk azeotropic composition.
Unselective point persists in supercritical regimes.
Azeotropic composition aligns with equal partial molar volumes and extremum in compressibility.
Abstract
Fluid mixtures that exhibit an azeotrope cannot be purified by simple bulk distillation. Consequently, there is strong motivation to understand the behavior of azeotropic mixtures under confinement. We address this problem using an ML-enhanced classical density functional theory (cDFT) applied to a binary Lennard-Jones mixture that exhibits azeotropic phase behavior. As proof-of-principle of a "train once, learn many" strategy, our approach combines a neural functional trained on a single-component repulsive reference system with a mean-field treatment of attractive interactions, inspired by the connection between cDFT and local molecular field theory. The theory faithfully describes capillary condensation and results from grand canonical Monte Carlo simulations. Moreover, by taking advantage of a known accurate equation of state, the "neural LMFT" we present well-describes bulk…
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.
