Creating successful nanocomposites means pinpointing the right components that will lead to the desired properties, whether it be strength, weight, color, or some combination of properties. But with so many possibilities, the issue is finding the right mix. A large part of the search is understanding what effect a particular nanoparticle's shape will have on a polymer. To help make the process easier and more efficient, researchers have turned to math to predict the results.
Researchers at the National Institute of Standards and Technology (NIST) dealt with this issue by exploiting an idea from a 70-year-old math paper by Shizuo Kakutani, who suggested a way of more realistically modeling particle shapes in material property calculations. Using his ideas for practical materials science would have required far more number-crunching power than was available in Kakutani's day, but modern computers make this class of problems easier to handle. The team first created virtual nanoparticles that have the same physical shape as the real-world particles they want to analyze, and they then calculated the relevant properties using a publicly available software package (ZENO) developed partly at NIST.
NIST materials scientist Jack Douglas noted in a press released that his team "generate thousands of examples of the shapes we want, enough to represent variation in the real world. That gives us enough information to make general statements about their behavior in the mix."
Since polymer nanocomposites are central to many developing technologies relating to the energy, auto and airline industries, Douglas says, this theoretical effort promises to have an appreciable impact. The team's paper focuses on mixing CNTs or graphene with polymers, but the math has wider application.
To learn more, see the press release and their published work.