polymerdatabase.com offers thermo-physical data for the most common polymers. The data have been compiled from an extensive literature search and have been converted into SI units. The data apply only to high molecular weight polymers that are in the amorphous state and are fully atactic.
The amount of data available for polymers is still modest in comparison with the enormous amount of data available for low-molecular weight compounds. Only for the most common polymers do comprehensive thermo-physical data exist. Finding these data can be often a challenge. Besides scientific journals and polymer handbooks, patents, product data sheets and product brochures are excellent sources.
Please send us an email to let us know what data entry is incorrect. Your comments will be reviewed immedialtely, and the data record will be updated if necessary. We appreciate your feedback. Your suggestions, knowledge and expertise will improve our data and is useful to validate errors.
All entries were reviewed carefully, to ensure the highest quality and relevance. Unfortunately, for some compounds we could not find experimental data or the data were unreliable. However, we were able to calculate for almost all polymers thermo-physical data. The predicted data are also useful to validate experimental data, particularly, when the data from different sources are conflicting.
Over the years, many methods for calculating glass transition temperatures have been developed. The most widely used semi-empirical prediction methods are van Krevelen's group contribution method (GCM), Askadskii's atomistic method (ACM), and Bicerano's topological method (TM). In recent years, a fourth method, based on group interaction modelling (GIM), has become popular. We believe, that GIM is the most versatile method. It is CROW's prefered method for calculating glass transition temperatures. A second method that has been used to estimate glass transition temperatures is based on correlating experimental glass transition temperatures for one (sub)class of polymers to other properties and interpolating or extrapolating those that are unknown.
There are a number of prediction methods for solubility parameters and cohesive energies. Most of these methods have been olptimized for low molecular weight compounds and are not very accurate when applied to polymers. Some of the best GC methods have been developed by Krevelen and Hoy. CROW's GC method gives values of similar accuracy but is more versatile.
Over the years, several prediction methods have been developed for estimating heat capacities of polymers in the glass and rubber state. We investigated three GC methods and found they all give results that are in good agreement with the experimental results compiled by Wunderlich et al. The new method developed by CROW, has more GC terms than older methods and, therefore, is more powerful and also more accurate.