(190r) Molecular Simulations of Network Polymers of Intrinsic Microporosity: Structure Generation by a Simulated Polymerization Algorithm and Gas Adsorption Studies | AIChE

(190r) Molecular Simulations of Network Polymers of Intrinsic Microporosity: Structure Generation by a Simulated Polymerization Algorithm and Gas Adsorption Studies

Authors 

Lin, P. - Presenter, Pennsylvania State University
Colina, C. M. - Presenter, Pennsylvania State University


Polymers of intrinsic microporosity (PIMs) are a new class of amorphous, microporous materials that derive their porosity by the inclusion of rigid and nonlinear units, which prevent space-efficient packing. Network PIMs provide an enhanced rigidity, which results in greater microporosity. These microporous materials, with interconnected pores of sizes smaller than 2 nm, are of interest for a variety of applications including separations, adsorption, gas storage, and catalysis.

The performance of microporous materials is dependent on their porosity, which is difficult to characterize experimentally. Therefore, molecular simulations offer a unique perspective by allowing examination of the structures on the molecular level. This detail provided by the simulations, such as geometric measures of the pore structures, offers additional insight into understanding the nature of PIMs and assists in the design and tailoring of these materials for specific applications.

In this work, we apply a recently developed structure generation procedure to network PIMs, including those derived from hexaazatrinaphthylene (HATN) and cyclotricatechylene (CTC). This approach utilizes a simulated polymerization algorithm to form networks by connecting repeat units close together in a periodic box. The resulting simulated structures are characterized by densities, surface areas, pore volumes, pore size distributions, and structure factors. Additionally, gas adsorption isotherms are calculated using grand canonical Monte Carlo (GCMC) simulations to further study their adsorptive properties. The results show qualitative agreement to available experimental data.