Mathematical modelling predicts polymer network properties

Researchers from Ghent University (UGent), Queensland University of Technology (QUT) and Stanford University have developed a powerful mathematical modelling tool that will allow scientists to predict the properties of polymer networks before they are even created.

Polymer networks are made up of long chains of molecules, like a string of pearls or spaghetti, and have many applications including rubbers, coatings, adhesives and cosmetics. The researchers’ new model, published in the journal Nature Materials, predicts the connections between the spaghetti-like strands.

“For the first time, this is a predictive tool for material properties of networks — from the smallest building block of the molecule up to how hard is the material, is it impact resistant or is it just a soft blob,” said UGent Professor Dagmar R D’hooge. UGent’s Dr De Keer added that the tool will aid in the design of new supermolecular polymers in areas such as drug delivery, gene transfection and biomedical applications.

Professor Christopher Barner-Kowollik, from QUT’s Centre for Materials Science, said the team developed the model using advanced mathematics and molecular simulations, bringing together researchers from computational modelling, synthetic chemistry and materials science.

“Recent chemistry developments have included unconventional properties such as self-healing, conductivity and stimuli-responsiveness in polymer networks, giving them a large potential in advanced applications such as recycling, drug delivery, tissue engineering scaffolds, gas storage, catalysis and electronic materials,” Prof Barner-Kowollik said.

“It’s a huge task to characterise polymer networks — it’s really difficult.

“Here we are making a real step forward by fusing expertise from theoretical modelling to experimental chemists who provide examples by which the model can be tested.”

Prof Barner-Kowollik said the ultimate dream for experimental chemists is to have a computer program that takes the unknown out of experiments.

“Imagine if you could have a supercomputer that, even before you hit the lab, would be able to say what the likely outcome would be,” he said. “This is a step in towards that.”

Professor Reinhold Dauskardt at Stanford University said the work “represents a tour de force of fundamental materials chemistry” and “shows how molecular building blocks can be assembled both temporally and spatially to create accurate materials structures including defects and resulting structure-property relationships”.

“This combination of both kinetics and molecular spatial assembly has not been achieved before,” Prof Dauskardt said.

Image credit: ©stock.adobe.com/au/Photobank

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