Modified Group Contribution Scheme to Predict the Glass-Transition Temperature of Homopolymers through a Limiting...

Previous studies on glass-transition temperature (T _g) prediction mainly focus on developing diverse methods with higher regression accuracy, but very little attention has been paid to the dataset. Generally, a large range of T _g values of a specified polymer could be found in the literature but which one should be selected into a dataset merely depends on the implicit preference rather than a recognized and clear criterion. In this paper, limiting glass-transition temperature (T _g(∞)), a constant value obtained at the infinite number-average molecular weight M _n, was validated to be an adequate bridge index in the T _g prediction models. Furthermore, a new dataset containing 198 polymers was established to predict T _g(∞) using the improved group contribution method and it showed a good correlation (R ² = 0.9925, adjusted R ² = 0.9894). The method could also generate T _g-M _n curves by introducing the T _g(∞) function and provide more information to polymer scientists and engineers for material selection, product design, and synthesis.