Prediction of the Glass Transition Temperature of Multicyclic and Bulky Substituted Acrylate and Methacrylate Polymers Using the Energy, Volume, Mass (EVM) QSPR Model

Abstract

Described here is a QSPR equation for calculating glass transition temperatures for acrylate and methacrylate polymers, especially those with bulky ester substituents. This approach is based on molecular mechanics calculations and exclusively involves a force field to describe a particular polymer system; i.e., no group additivity values are required. Results from two different force fields yielded similar results, indicating that this model is not dependent on a particular force field parameter set but rather on the atomic properties that the force field describes. The molecular mechanics calculation results (energy term), the repeat unit mass, and a measure of the volume surrounding the polymer segment (TSSV) were used to determine an energy density function that is related to experimental T g values. This energy density function is important because it illustrates that the glass transition temperature of an amorphous polymer is related not only to the volume surrounding the polymer segment but also to its conformational energy. Limitations of other QSPR approaches (stemming from not having a particular group or bond connectivity described within the given model) are not present in this approach.

Molecular dynamics simulation on glass transition temperature of isomeric polyimide

“…For comparison, we also applied group contribution method to predict T g of PIs [13]. This method is based on the contribution of each functional group to thermodynamic properties of polymeric materials, and it has been widely used in alkyl polymers [23,24]. However, in this study, the T g values of two PIs are the same using this method for it can not resolve the position effect of the imide group.…”

Glass transition temperature prediction of polymers through the mass-per-flexible-bond principle

“…These values are remarkably low considering the simplicity of the model, the large data scatter in the T g vs. M/f plots, and the structural variety in the library. In fact, this accuracy is better than most of the results from (semi-) empirical T g prediction methods found in the literature [30][31][32][33][34][35][36][37][38][39][40][41][42][43]50].…”

“…Other commonly used methods for T g prediction are: ab initio quantum mechanical calculations [21,22], Monte Carlo [23,24], and molecular dynamics simulations [25][26][27][28][29] and semi-empirical or empirical methods based on group contribution methods, often using the QSPR approach through neural network computation [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. While some of these methods yield good results by predicting glass transition temperatures with errors as good as 3-10 K, most predictive accuracies are on the order of 20-100K.…”

Prediction of the Formulation Dependence of the Glass Transition Temperature for Amine-Epoxy Copolymers Using a Quantitative Structure-Property Relationship Based on the AM1 Method

“…A designer correlation equation was developed by Camelio et al (9,11) to investigate the role of bulky substituents in determining the T g of substituted methacrylates and acrylates. The Camelio QSPR model, referred to as the energy, volume, and mass (EVM) model, was developed to describe the effects of bulky substituents in terms of energetic barriers to rotation along the polymer backbone as affected by the proximity of the center of mass and volume of substituents to the polymer backbone.…”