Physical model, glass polymers

The understanding of the relation between the macroscopic film properties of an acrylic coating and the polymer composition has been advanced significantly by the successful application and interpretation of two physical models (1) the glass transition temperature, Tg (2) the solubility parameter, 6. 

Robertson et al. [1984] developed a stochastic model for predicting the kinetics of physical aging of polymer glasses. The equilibrium volume at a given temperature, the hole fraction, and the fluctuations in free volume were derived from the S-S cell-hole theory. The rate of volume changes was assumed to be related to the local free volume content thus, it varied from one region to the next according to a probability function. The model predictions compared favorably with the results from Kovacs laboratory. Its evolution and recent advances are discussed by Simha and Robertson in Chapter 4. 

C. G Sell and G. B. McKenna, Influence of Physical Ageing on the Yield Response of Model DGEBA/Pol3KPropylene Oxide) Epoxy Glasses , Polymer 33, 2103—2113 (1992). 

The past decade has seen steadily growing activity in the detailed atomistic modeling of polymer melts and glasses using energy minimisation and molecular dynamics simulation.These studies have been aimed at achieving an atomistic level understanding of a variety of physical properties such as stress-strain behavior, diffusion of small solute molecules and local chain motions. Because of its relative simplicity, polyethylene has come under a... 

In this approach, connectivity indices were used as the principle descriptor of the topology of the repeat unit of a polymer. The connectivity indices of various polymers were first correlated directly with the experimental data for six different physical properties. The six properties were Van der Waals volume (Vw), molar volume (V), heat capacity (Cp), solubility parameter (5), glass transition temperature Tfj, and cohesive energies ( coh) for the 45 different polymers. Available data were used to establish the dependence of these properties on the topological indices. All the experimental data for these properties were trained simultaneously in the proposed neural network model in order to develop an overall cause-effect relationship for all six properties. 

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