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Polymer molecular modeling at integrated length/time scales

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A crystal ball for polymer properties

Polymers form the backbone of manufacturing industry due to their multifunctional nature. A team of scientists has devised a software package whereby the type of polymer can be predicted based on the designer's required properties.

Industrial Technologies

The range of polymers in relation to structure and function is seemingly infinite, from synthetic hair to heat-resistant cookware through to waste pipes. Macroproperties of the polymer reflect the changes undergone when the material is subjected to pressure, temperature, light, electrical and magnetic forces. Included also are its behaviour when dissolved in a solvent or its chemical reactivity. To vary these properties, the chemical engineer has many means including changes in monomer identity, chain length, branching and composition of branch, as well as in the degree of crystallinity. The result is a bewildering choice of materials for the manufacturer. The European project PMILS set about to make the life of the industrial polymer user easier. Partners designed modelling tools whereby the performance of polymers and the mechanisms involved can be predicted through the advanced software tools. More specifically, a team of scientists within the project designed the Computer Aided Polymer Design (CAPD). This analyses the user's required target properties and gives output in terms of the required polymer units and their number, in addition to branch details including length and molecular weight. As such, it gives the production engineer a list of candidate polymers that can then be tested to identify the final choice of polymer. Two tools were developed, one for generating property predictions and another for generating the molecular structures of the chemicals. The CAPD tool is user-friendly being Windows-based and has an interface which is easy to use. The CAPD tool is capable of assisting in polymer product development by allowing the designer to concentrate on the most promising candidates for a successful product. Further development of this technology could refine the tool further for a more specifically defined output.

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