Prediction of Polymer Glass Transition Temperatures Using a General Quantitative Structure-Property Relationship Treatment (original) (raw)
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Methods of Polymers Analysis, 2020
In this research, some thermo-physical (glass transition temperature, Tg; melting point, Tm) and mechanical properties (tensile strength, TS; Young’s modulus, Y) of hydrophobic polymers were studied. The linear dependences between these properties and the specific cohesive energy were obtained. It was found that the studied properties of polymer materials correlate much better with the volume cohesive energy (Ev) than with the molar cohesive energy. The linear regression equations, Z = k Ev + C, with high correlation coefficients were calculated, where Z is property, k and C are coefficients. The dependences of various properties of polymers on Tg were also studied. It was shown that the obtained relationships allow to predict some properties of polymer materials with a sufficiently good reliability.
Predicting Glass Transition Temperatures of Polyarylethersulphones Using QSPR Methods
PLoS ONE, 2012
The technique of Quantitative Structure Property Relationships has been applied to the glass transition temperatures of polyarylethersulphones. A general equation is reported that calculates the glass transition temperatures with acceptable accuracy (correlation coefficients of between 90-67%, indicating an error of 10-30% with regard to experimentally determined values) for a series of 42 reported polyarylethersulphones. This method is quite simple in assumption and relies on a relatively small number of parameters associated with the structural unit of the polymer: the number of rotatable bonds, the dipole moment, the heat of formation, the HOMO eigenvalue, the molar mass and molar volume. For smaller subsets of the main group (based on families of derivatives containing different substituents) the model can be simplified further to an equation that uses the volume of the substituents as the principal variable.
Glass transition temperature prediction of polymers through the mass-per-flexible-bond principle
Polymer, 2007
A semi-empirical method based on the mass-per-flexible-bond (M/f ) principle was used to quantitatively explain the large range of glass transition temperatures (T g ) observed in a library of 132 L-tyrosine derived homo, co-and terpolymers containing different functional groups. Polymer class specific behavior was observed in T g vs. M/f plots, and explained in terms of different densities, steric hindrances and intermolecular interactions of chemically distinct polymers. The method was found to be useful in the prediction of polymer T g . The predictive accuracy was found to range from 6.4 to 3.7 K, depending on polymer class. This level of accuracy compares favorably with (more complicated) methods used in the literature. The proposed method can also be used for structure prediction of polymers to match a target T g value, by keeping the thermal behavior of a terpolymer constant while independently choosing its chemistry. Both applications of the method are likely to have broad applications in polymer and (bio)material science.
The nature and determination of the dynamic glass transition temperature in polymeric liquids
2014
The physical properties of polymers are very much dictated by where the operating temperature lies with respect to the transition temperature between glassy and rubbery states. The precise identification of this glass transition temperature, Tg, is critical in assessing the feasibility of a polymer for a given application. In this book, the behavior of polymers near their Tg and the capability of predicting Tg using theoretical and empirical models is assessed. While all polymers undergo structural relaxation at various temperatures both nearly above and below Tg, practical assessment of a single consistent Tg is successfully performed through consideration of only immediate thermal history and thermodynamic properties. The determination of Tg for a wide variety of polymers of theoretically infinite chain length has been found to be accurately performed through the use of novel quantitative structure-property relationship (QSPR) models. The supplementation of such values to configur...
Journal of Polymer Research, 2018
Glass transition temperatures of polymers were modelled by means of the CORAL software available on the Internet (http://www.insilico.eu/coral). The architecture of monomers was represented via simplified molecular input line entry systems (SMILES). Three random splits into the training and validation sets were tested to build up quantitative structure-property relationships (QSPRs). The index of Ideality of Correlation (IIC) represents a new measure of predictive potential. Application of the IIC as the criterion of predictive potential for the calibration set was tested in this work and resulted in correct recommendations for selection of the best model from three different models considered here.
Quantitative relationships between structure and glass transition temperature T g and of polyethylene analogues has been studied. The study was done by using molecular modeling of polymer assumed in trimeric compound in their indiotactic form and the calculation was performed by semiempirical AM1 method. The physicochemical properties of molecule was focused on 11 descriptors i.e. atomic net charges of carbon atom as the head and tail of the polymer chain (qC 1 and qC 2 ), polarizability (α), moment dipole (μ), refractivity index, partition coefficient of n-octanol-water (log P), molecular weight (MW), volume van der Waals (V VDW ), molecular surface area, Parachor index and solubility in the water (log SW). Correlation analysis of T g polymers to those predictors was based on statistical technique of multiple linear regression. The QSPR model resulted is relatively good in terms of accuracy of calculate T g values of polymer. However the QSPR model is still limited by the validity of the experimental data that were used to derive the regression coefficients of the QSPR equation.
Improving an EVM QSPR model for glass transition temperature prediction using optimal design
Chemometrics and Intelligent Laboratory Systems, 2002
An energy, volume and mass (EVM) model, involving four physico-chemical descriptor variables, i.e. van der Waals energy, internal energy, volume, and mass, to successfully predict the glass transition temperatures (T g ) of aliphatic acrylate and methacrylate polymers, has been previously described. The EVM model is as good, or better, as the previous models in terms of accuracy of calculated T g values of polymers. However, the classical EVM approach is still limited by the validity of the experimental data that were used to derive the regressor coefficients of the quantitative structure -properties relationship (QSPR). In fact, one major problem is the large variation in the experimental T g values that were reported in the literature. Deciding which values to use for modelling the relationship and the evaluation set of polymers is a problem to tackle with. For these reasons, an a priori design approach to the selection of a database of acrylate and methacrylate polymers for the evaluation of the EVM model has been adopted. In particular, the selection of the molecules to be considered was performed by two computed-assisted procedures based on the exchange algorithm for obtaining D-optimal design and the uniform method for finding experimental designs characterized by a stable structure. Based on the a priori design criteria, the selection of the optimal and uniform designs was ''carried out'', in particular according to G-optimality. D
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.