(539c) Predictive Modeling of Microporous Polymeric Adsorbents of Complex Sorbates | AIChE

(539c) Predictive Modeling of Microporous Polymeric Adsorbents of Complex Sorbates


Anstine, D. - Presenter, University of Florida
Tang, D., Georgia Institute of Technology
Sholl, D., Georgia Tech
Colina, C., University of Florida
While the use of molecular simulations is standard practice in many research areas, studies focusing on microporous polymeric adsorbents are often limited to a few amorphous frameworks, a handful of adsorbates, and typically restricted to rigid framework methods. This talk will highlight our recent work using molecular simulations to assess microporous polymer structure rearrangements and adsorption performance across dilute, swollen, and plasticized loading conditions. Despite the prevalence of species diversity in industrial gas mixtures, a broader understanding of the effect that chemical identity has on polymeric adsorbent restructuring is lacking. In light of this, we report a screening study to assess 15 polymers of intrinsic microporosity (PIMs) in combination with 23 diverse adsorbate species, which results in 345 distinct single-component isotherms adsorption isotherms and a database of over 240,000 polymer conformations. The simulations were conducted using a combined Monte Carlo and molecular dynamics approach that allows for the analysis of sorption-induced changes to microporous structural features. Using this data, we have developed correlations that can provide rapid approximations of polymer swelling and fractional free volume dilation with appreciable accuracy. These approximations are a function of readily obtainable adsorbate properties (critical temperature and molar mass) and adsorbent features (surface area and pore volume), which is indicative of their potential to be broadly applied in both computational and experimental efforts. Additionally, the separation selectivity of the 253 unique binary pairs at dilute loading conditions is reported and high-performing adsorbent systems are identified. Overall, our study reports the largest dataset of simulated adsorption for amorphous polymeric materials to date with framework rearrangement accounted for. This provides key first steps toward achieving screening studies of amorphous polymers that rival the magnitude of ordered adsorbents.