Molecular Simulation and Computational Modeling of Gas Separation through Polycarbonate/p-Nitroaniline/Zeolite 4A Mixed Matrix Membranes

Abstract

We develop novel methods to estimate the gas permeation within the mixed matrix membranes (MMMs) fabricated by polycarbonate (PC), p-nitroaniline (pNA), and zeolite 4A. To this end, molecular simulation including molecular dynamics and grand ganonical Monte Carlo methods were utilized to determine the diffusivity and solubility coefficients of H 2 , CH 4 , CO 2 , O 2 , and N 2 gas molecules within the PC/pNA/zeolite 4A membranes. Coupled with other analysis such as Xray diffraction, fractional free volume, and radial distribution function, structural and separation features like glass-transition temperature, mean square displacement, density, and adsorption isotherms define the solution-diffusion separation mechanism. Two state-of-the-art and accurate artificial intelligence (AI) models, viz ant colony optimization−adaptive neuro-fuzzy inference system (ACO−ANFIS) and genetic programming (GP) are developed to estimate the permeability of mentioned gas molecules within the MMMs. The R 2 value for ACO−ANFIS and GP was obtained as 0.97 and 0.96, respectively. The mean squared error value for ACO−ANFIS and GP was obtained as 0.41 and 0.51, respectively. Although the accuracy of ACO−ANFIS is higher, both models can be regarded as efficient models. The findings of the study demonstrate that the AI models are accurate. In addition, the effects of various key parameters like zeolite and pNA loading and kinetic diameter of gases are investigated. Also, it was concluded that the simple PC membrane indicated the best performance compared to other prepared MMMs.

Modelling Amorphous Nanoporous Polymers Doped with an Ionic Liquid via an Adaptable Computational Procedure

“…To examine the nanoscopic structuring occurring during the polymerization processes within ANP materials, computational tools can be helpful means to reveal the variations of specific morphological and surface energy properties. For instance, MD simulations can enable us to build and test these polymers and relate their performance, in terms of thermo-mechanical properties or gas separation performance, to molecular-level structuring. In a related study, Jiang et al suggested a computational procedure for making amorphous organic cages, which were generated out of simulation cells and randomly packed in a simulation cell, followed by a series of MD simulations for equilibration.…”