(704d) Multi-Scale Simulation of Ionic Polyimide Composite Membranes Based on Structural and Electrostatic Maps

Polyimides are at the forefront of advanced membrane materials for CO₂ capture and gas purification processes. Recently, ionic polyimides (i-PIs) have been reported as a new class of condensation polymers which combine structural components of both ionic liquids (ILs) and polyimides through covalent linkages. In this work, the CO₂ separation characteristics of ionic polyimides are modeled using a multi-scale simulation strategy by combining density-functional theory (DFT), molecular dynamics (MD) simulations, grand canonical Monte Carlo (GCMC), and kinetic Monte Carlo (KMC) simulations.

Based on a molecular-level description of the i-PI membrane, MD and GCMC simulations are used to generate a lattice model of the pore structure and the local electrostatic environment relevant to the diffusing gas molecules (CO₂/CH₄). This discretized membrane information is used to populate a comprehensive KMC model that can be used to make predictions of gas separation performance on experimentally-relevant time scales. Using this KMC model, we can predict permeability, selectivity, and other transport behavior and connect these metrics directly to the ongoing experimental synthesis work.

The performance of neat i-PI systems is evaluated, as well as composite structures containing both i-PIs and various ionic liquids (ILs). The i-PI+IL composites are based on combinations of 1-n-butyl-3-methylimidazolium ([C₄mim⁺]) cations with three common molecular anions: (bis(trifluoromethylsulfonyl)imide ([Tf₂N^-]), tetrafluoroborate ([BF₄^-]), and hexafluorophosphate ([PF₆^-]). It is found that 50 mol% IL inclusion can increase CO₂/CH₄ selectivity by 16% in [BF₄^-]-based materials and by 36% in [PF₆^-]-based materials from mixtures of 5% CO₂ / 95% CH₄. While the [BF₄^-]-based system shows higher CO₂/CH₄ selectivity, the [Tf₂N^-]-based system shows higher CO₂/N₂ gas selectivity. Overall, we find that some of the commonly-used design equations for membrane performance provide inconsistent predictions, as compared to our multi-scale simulation analysis that can capture important molecular-level details (pore blocking, neighbor interactions, pore percolation, etc.).