The roles of bulk and surface thermodynamics in the selective adsorption of a confined azeotropic mixture

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 thermodynamics by construction. Exploiting the computational efficiency of neural LMFT, we systematically evaluate adsorption selectivity across a wide range of compositions, pressures, temperatures, and wall-fluid affinities. In cases where the wall-fluid interaction is the same for both species, we find that the pore becomes completely unselective at the bulk azeotropic composition. Strikingly, this unselective point persists far from liquid-vapor coexistence, including in the supercritical regime. Analysis of the bulk equation of state across a wide range of thermodynamic state points shows that the azeotropic composition coincides with equal partial molar volumes and an extremum in the isothermal compressibility. A thermodynamic analysis demonstrates that unselective adsorption corresponds to an aneotrope (a point of zero relative adsorption) and an extremum in the interfacial free energy. We also find that the two interfaces of the slit pore behave independently down to remarkably small slits.

Main: 14 pages, 5 figures. SI: 5 pages, 4 figures

References in corpus (22)

• Capillary condensation under atomic-scale confinement
• Density functional theory for hard-sphere mixtures: the White-Bear version Mark II
• Liquid-like behavior of supercritical fluids
• A unified description of hydrophilic and superhydrophobic surfaces in terms of the wetting and drying transitions of liquids
• Transition in the supercritical state of matter: experimental evidence
• Critical Drying of Liquids
• Local molecular field theory for the treatment of electrostatics
• Neural functional theory for inhomogeneous fluids: Fundamentals and applications
• Machine-learning free-energy functionals using density profiles from simulations
• Analytical classical density functionals from an equation learning network
• Density depletion and enhanced fluctuations in water near hydrophobic solutes: identifying the underlying physics
• Machine learning of a density functional for anisotropic patchy particles
• Thermodynamic crossovers in supercritical fluids
• Learning classical density functionals for ionic fluids
• Hyper-density functional theory of soft matter
• Neural density functional theory of liquid-gas phase coexistence
• A classical density functional theory for solvation across length scales
• Metadensity functional theory for classical fluids: Extracting the pair potential
• Learning the bulk and interfacial physics of liquid-liquid phase separation with neural density functionals
• Neural Density Functional Theory in Higher Dimensions with Convolutional Layers
• A first principles approach to electromechanics in liquids
• Dielectrocapillarity for exquisite control of fluids

Cited by in corpus (2)

• Metadensity functional learning for classical fluids: Regularizing with pair correlations
• A unified machine-learning framework for ab initio multiscale modeling of liquids