Prediction of the glass transition temperature of polymers

Weyland, H.G.; Hoftyzer, P. J.; Krevelen, D.W. Van

doi:10.1016/0032-3861(70)90028-5

Cited by 77 publications

(30 citation statements)

References 4 publications

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“…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].…”

Section: Accuracy Of the Predictionmentioning

confidence: 64%

“…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.…”

Section: Polymer Flexibility and T Gmentioning

confidence: 99%

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

Schut

Bolikal

Khan

et al. 2007

Polymer

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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.

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Section: Accuracy Of the Predictionmentioning

confidence: 64%

Section: Polymer Flexibility and T Gmentioning

confidence: 99%

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

Schut

Bolikal

Khan

et al. 2007

Polymer

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“…The greatest change occurs in the vicinity of the glass transition temperature (T g ) so that a useful aid in understanding behavior is a knowledge of the effect of humidity on T g . This is complicated because of variation with the method of calculation or measurement (Table 6), but it is generally true that nylons with fewer CONH groups and lower water adsorption have a lower dry T g but show less change of T g with relative humidity [19,121,[185][186][187][188][189][190].…”

Section: Properties Of Unmodified Polyamidesmentioning

confidence: 99%

Polyamides

Herzog¹,

Kohan²,

Mestemacher³

et al. 2013

Ullmann's Encyclopedia of Industrial Chemistry

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The article contains sections titled: 1. Introduction 2. Nomenclature 3. General Considerations in Polyamidation 3.1. Molecular Mass 3.2. Equilibrium and Rate Constants 3.3. Effects of Monomer Structure 3.4. Amide Interchange 3.5. Structure‐Property Considerations 4. Other Polymerization Techniques 4.1. Variants of Hydrolytic Polymerization 4.2. Interfacial and Solution Polymerization 4.3. Ionic Polymerization 4.4. Solid‐Phase Polymerization 5. Commercial Processes 5.1. PA 46 5.2. PA 66 5.2.1. Batch Production 5.2.2. Continuous Production 5.3. Other AABB‐Polyamides 5.4. PA 6 5.5. PA 11 5.6. PA 12 6. Properties 6.1. Properties of Unmodified Polyamides 6.2. Copolymerization 6.3. Modification by Additives 6.3.1. Filled and Reinforced Polyamides 6.3.2. Blends and Alloys 6.3.3. Flame‐Retardant Polyamides 6.3.4. Conductive Polyamides 6.3.5. Other Formulations 7. Processing 8. Uses 9. Ecological Aspects and Toxicology 9.1. Recycling 9.2. Polyamide Monomers 9.3. Other Aspects 10. Economic Aspects

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“…In forthcoming papers, we will report our results on more elaborated acrylates al. 11,12 have demonstrated the value of this approach to predict polymer properties. Using and methacrylates [19][20][21] as well as various vinyl type polymers (i.e., polystyrenes 22,23 ) where the group contributions yields a model that has built-in information about chemical structure;…”

Section: Introductionmentioning

confidence: 98%