Modelling tool developed to predict polymer properties

polymer

An interdisciplinary team of researchers from Queensland University of Technology (QUT), Stanford University and Ghent University (UGent) has developed a powerful mathematical modelling tool that can predict the properties of polymer networks before they are even created. 

Polymers networks are made up of long chains of molecules. This new model predicts the connections between the strands. 

Polymer networks have many applications including rubbers, coatings, adhesives and cosmetics, UGent Professor Dagmar R. D’hooge said. 

“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,” D’hooge said. 

The tool outlined in the research was an aid in the design of new supermolecular polymers in areas such as drug delivery, gene transfection and biomedical applications. 

The researchers 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,” QUT Centre for Materials Science professor Christopher Barner-Kowollik said. 

“Giving them a large potential in advanced applications such as recycling, drug delivery, tissue engineering scaffolds, gas storage, catalysis and electronic materials. 

“It’s a huge task to characterise polymer networks. 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.” 

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.” 

Stanford University professor Reinhold Dauskardt said he was “super excited” about the work. 

“It represents a tour-de-force of fundamental materials chemistry and demonstrates what can be achieved from an international team with diverse backgrounds,” Dauskardt said. 

The work 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,” Dauskardt said. 

The study to predict the properties of polymer networks was published in Nature Materials.