An Efficient Algorithm for Molecular Density Functional Theory in Cylindrical Geometry: Application to Interfacial Statistical Associating Fluid Theory (iSAFT)

作者：Shun Xi, Jinlu Liu, Arjun Valiya Parambathu, Yuchong Zhang, Walter G. Chapman · 发表于：Industrial & Engineering Chemistry Research · 年份：2020 · DOI：10.1021/acs.iecr.9b06895 · 被引用次数：15 · 研究领域：Phase Equilibria and Thermodynamics、Material Dynamics and Properties、Rheology and Fluid Dynamics Studies

In this work, we present an efficient numerical algorithm for the solution of molecular density functional theory (DFT) in cylindrical geometry to facilitate the study of how curvature affects the microstructure and phase behavior of inhomogeneous fluids. The new solution algorithm is shown to have a better time scaling than the elliptic function method by Malijevskỳ [ J. Chem. Phys. 2007, 126, 134710 ] and the transform method by Lado [ J. Comput. Phys. 1971, 8, 417−433]. Convergence, performance, and stability of the numerical algorithm are discussed. We showcase two representative applications of the new method for modeling fluid adsorption and bottlebrush polymers using a specific DFT, interfacial statistical associating fluid theory (iSAFT). By comparing iSAFT with molecular simulation results, we found that iSAFT predicts layering transitions above the triple point for methane adsorption, and it captures power-law to parabolic transitions for polymer brush microstructure.