A team of researchers has developed a powerful mathematical modeling tool that will allow researchers to predict the properties of polymer networks before they are even created. Polymers networks are made up of long chains of molecules, like a string of pearls or spaghetti. This new model predicts the connections between the spaghetti-like strands.
Predictive Tool for Material Properties
The researchers from Ghent University (UGent), QUT, and Stanford University, developed the method for predicting polymer properties. Dagmar R. D’hooge, of UGent, Belgium, said polymer networks had many applications including rubbers, coatings, adhesives, and cosmetics.
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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.
Dr. De Keer, of UGent, said the tool outlined in the research was an aid in the design of new super molecular polymers in areas such as drug delivery, gene transfection, and biomedical applications.
Along with Dagmar R. D’hooge and Dr De Keer, UGent researchers involved in the study include Paul Van Steenberge, Marie-Françoise Reyniers, Lode Daelemans and Karen De Clerck.
Advanced Mathematics and Molecular Simulations
The researchers developed the model using advanced mathematics and molecular simulations, bringing together researchers from computational modeling, synthetic chemistry, and materials science.
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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,” Christopher Barner-Kowollik, from QUT’s Centre for Materials Science, said.
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It’s a huge task to characterize polymer networks - it’s really difficult. Here we are making a real step forward by fusing expertise from theoretical modeling to experimental chemists who provide examples by which the model can be tested.”
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. This is a step in towards that,” Barner-Kowollik said.
Reinhold Dauskardt at Stanford University said,
“It represents a tour-de-force of fundamental materials chemistry and demonstrates what can be achieved from an international team with diverse backgrounds.”
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.
Source: QUT