A novel model to predict infinite dilution solubility coefficients in glassy polymers

Abstract

Infinite dilution gas solubility coefficients in glassy polymers of diverse chemical structure are predicted on the basis of a simple computational model of short polymer chain conformations. In contrast to existing models that describe the infinite dilution solubility coefficient S of various gases in certain polymers or S for the particular gas in different polymers, our approach allows to deal with the broad variety of gases and glassy polymers of different structure. With the van der Waals volume, accessible surface area, and some derivative geometrical descriptors being the predicting variables, the multivariate linear regression on the testing set gives correlation 0.89 with the logarithm of the solubility coefficient for the considered classes of relatively low‐permeable polymers (the mean relative error of solubility coefficient S prediction is 68%). Regression accuracy can be improved up to correlation 0.95 and the mean relative error 39% when a separate regression is constructed for each penetrant gas. Implications are discussed to the analysis of solubility selectivity. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017, 55, 228–244

Molecular Dynamics Simulation of Nanostructure of High Free Volume Polymers with SiMe3 Side Groups

“…In fact, this is frequently applied approach because for simple gases such interactions are not very important. An exception is carbon dioxide; its interactions with some groups of polymers are relevant in simulation of gas sorption, as the studies of CO 2 –polyimide systems indicate. , Such phenomena will be more important in subsequent studies of gas sorption and diffusion in the same polymers maybe using a novel approach described in our recent paper …”

“…41,47 Such phenomena will be more important in subsequent studies of gas sorption and diffusion in the same polymers maybe using a novel approach described in our recent paper. 48 ■ ASSOCIATED CONTENT * S Supporting Information…”

Structure-Property Relationship on the Example of Gas Separation Characteristics of Poly(Arylene Ether Ketone)s and Poly(Diphenylene Phtalide)

“…This paper continues the development of practical application of the quantitative structure-property relationships (QSPR)-based short polymer chain surface-based prediction (SPCSBP) method from [42,43,63]. The method consists of modeling of the polymer macromolecule conformations with a size of about several hundred atoms.…”

“…Therefore, the use of neural networks for the adequate prediction of the transport parameters of polymers is currently limited. More attractive for the existing datasets [15,16] are the various variants of the group contribution method that was used for these datasets before [28,34,35,37], or the developed methods of geometrical indices [42,43], which takes into account the geometric parameters of the model fragments of polymer chains. However, when using any prediction methods, it is necessary to verify these methods for new polymers that were not included in the dataset, on the basis of which these contributions or indices were determined.…”

“…The extensive information accumulated to date on the transport parameters of polymers with different chemical structures of the elementary unit [ 15 , 16 ], allows one to not only search for empirical relationships between the chemical structure of the elementary unit with the transport characteristics of polymers [ 7 , 8 , 13 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], but also to predict transport parameters from the chemical structure of the elementary unit [ 29 ]. Such predictions are possible both by the group contribution methods [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], but also by using elements of the graph theory [ 29 , 40 , 41 , 42 ] and geometrical indices [ 43 , 44 ], as well as by using neural networks [ 29 , 45 , 46 ] or computer modeling [ 29 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ,…”