Semiempirical Method for the Prediction of the Theta (Lower Critical Solution Temperature) in Polymer Solutions

Abstract

Here, a semiempirical method for predicting the solubility limit of polymer solutions at high temperatures is proposed. This method has been developed for infinite-chain-length polymers dissolved in Θ solvents. The development is based on several data for polystyrene solutions, and it has been tested also for polyethylene and polypropylene solutions. This method only requires the critical density and temperature of the solvent for the prediction of the hightemperature miscibility limit. The prediction appears to be reasonable considering the simplicity of the correlation model and gives better prediction than earlier comparably simple methods.

Accurate Prediction of θ (Lower Critical Solution Temperature) in Polymer Solutions Based on 3D Descriptors and Artificial Neural Networks

“…The positive sign of H2v in the present MLRA model indicates greater atomic van der Waals volumes of solvents being unfavorable for θ (LCST) in polymer solutions. Imre et al24 proposed three simple correlations for θ (LCST) in PS, PE and PP solutions: where T c and ρ c are the critical temperature and density of the solvent, respectively; and, P 1 , P 2 and P 3 are the regression coefficients In particular, P 1 and P 2 are positive. From these correlations, the systems with solvents of greater ρ c would have higher θ (LCST) values, which is consistent with the contributions of H2v to θ (LCST) since greater atomic van der Waals volumes are unfavorable for ρ c of solvents.…”

“…The predictive ability of these methods for new systems is limited. The second are those methods using empirical correlations relating θ (LCST) to other physiochemical properties, such as density, critical properties, or solubility parameters 24–27. These correlations are simple and have reasonable accuracy, but they cannot be used to systems for which the required physicochemical properties are not available.…”

A New Accurate Neural Network Quantitative Structure- Property Relationship for Prediction of θ (Lower Critical Solution Temperature) of Polymer Solutions

“…The ability of these methods for prediction of new systems is unknown. The second group of methods for estimating ) (LCST θ is the empirical models which relates ) (LCST θ to other physicochemical properties [9][10][11][12]13]. These correlations are simple and have reasonable accuracy, but they cannot be used to systems for which the required physicochemical properties are not available.…”

A general QSPR model for the prediction of θ (lower critical solution temperature) in polymer solutions with topological indices

“…Partially miscible polymer solutions often exhibit two solubility boundaries [2], the upper critical solution temperature (UCST) and the lower critical solution temperature (LCST). Both the UCST and LCST depend on the molar mass and pressure; however, the u temperatures, which are the critical solution temperatures at infinite chain length, are not affected by polymer molar mass.…”

“…The first group of models is those methods that have a solid theoretical background but that require vapor-liquid or liquid-liquid experimental data to adjust the unknown parameters, resulting in limited predictive ability [4][5][6][7][8]. The second group of models is empirical correlations relating u (LCST) to other physiochemical properties, such as density, critical properties, or solubility parameters [2,[9][10][11]. These correlations are simple and have reasonable accuracy, but they cannot be applied to systems for which the required physicochemical properties are not available.…”