Mohamed Amro M O, Economou Ioannis G, Jeong Hae-Kwon
Chemical Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Doha 122104, Qatar.
Artie McFerrin Department of Chemical Engineering, Texas A&M University, 3122 TAMU, College Station, Texas 77843-3122, United States.
J Phys Chem B. 2025 May 15;129(19):4765-4780. doi: 10.1021/acs.jpcb.4c04595. Epub 2025 Apr 30.
In this study, we introduce a set of coarse-grained (CG) force field parameters for simulating a series of 6FDA-based polyimides. Utilizing atomistic descriptors, we developed CG models that accurately predict the specific volume of the polymers under investigation. Our findings suggest that certain parameters, particularly those associated with specific diamines, can be employed to predict properties such as density using a multiple linear regression. Our study further explores the halogenation of diamines and proposes methods for estimating intermolecular interaction parameters. Our calculations refer to various structural properties, including the radius of gyration, end-to-end distance, glass transition temperature, and diffusion coefficients. Utilizing the newly developed CG force field parameters, we conducted gas separation simulations for 6FDA-DAM polyimide, particularly to predict both sorption- and diffusion-separation mechanisms within the polymer. These simulations provided excellent agreement with experimental data on solubility, diffusion, and permeability selectivity for CO/CH, O/N, and propylene/propane. The results contribute significantly to our understanding of polyimide behavior, and the parameters proposed here offer a promising tool for the development of new materials with tailored properties for targeted applications.
在本研究中,我们引入了一组粗粒度(CG)力场参数,用于模拟一系列基于6FDA的聚酰亚胺。利用原子描述符,我们开发了能够准确预测所研究聚合物比容的CG模型。我们的研究结果表明,某些参数,特别是那些与特定二胺相关的参数,可用于通过多元线性回归预测诸如密度等性质。我们的研究进一步探讨了二胺的卤化,并提出了估计分子间相互作用参数的方法。我们的计算涉及各种结构性质,包括回转半径、端到端距离、玻璃化转变温度和扩散系数。利用新开发的CG力场参数,我们对6FDA-DAM聚酰亚胺进行了气体分离模拟,特别是为了预测聚合物内部的吸附和扩散分离机制。这些模拟结果与关于CO/CH、O/N和丙烯/丙烷的溶解度、扩散和渗透选择性的实验数据高度吻合。这些结果对我们理解聚酰亚胺的行为有很大贡献,这里提出的参数为开发具有针对特定应用定制性能的新材料提供了一个有前景的工具。