P.C. Rossin College of
Engineering and Applied Science
Research paper is the first to come out of Lehigh’s Nano/Human Interface Presidential Engineering Research Initiative
In materials research, the ability to analyze massive amounts of data—often generated at the nanoscale—in order to compare materials’ properties is key to discovery and to achieving industrial use. Jeffrey M. Rickman, professor of materials science and engineering and also of physics, likens this process to candy manufacturing.
"If you are looking to create a candy that has, say, the ideal level of sweetness, you have to be able to compare different potential ingredients and their impact on sweetness in order to make the ideal final candy," Rickman said.
For several decades, nanomaterials—matter that is so small it is measured in nanometers (one nanometer = one-billionth of a meter) and can be manipulated at the atomic scale—have outperformed conventional materials in strength, conductivity and other key attributes. One obstacle to scaling up production is the fact that scientists lack the tools to fully make use of data—often in the terabytes, or trillions of bytes—to help them characterize the materials—a necessary step toward achieving "the ideal final candy."
What if such data could be easily accessed and manipulated by scientists in order to find real-time answers to research questions?
The promise of materials like DNA-wrapped single-walled carbon nanotubes could be realized. Carbon nanotubes are a tube-shaped material which can measure as small as one-billionth of a meter, or about 10,000 times smaller than a human hair. This material could revolutionize drug delivery and medical sensing with its unique ability to penetrate living cells.
A new paper takes a step toward realizing the promise of such materials. Authored by Rickman, the article describes a new way to map material properties relationships that are highly multidimensional in nature. Rickman employs methods of data analytics in combination with a visualization strategy called parallel coordinates to better represent multidimensional materials data and to extract useful relationships among properties. The paper, Data analytics and parallel-coordinate materials property charts, appears in NPJ Computational Materials, a Nature Research journal.
"We illustrate the utility of this approach by providing a quantitative way to compare metallic and ceramic properties—though the approach could be applied to any materials you want to compare," Rickman said.
It is the first paper to come out of Lehigh’s Nano/Human Interface Presidential Engineering Research Initiative, a multidisciplinary research initiative that proposes to develop a human-machine interface to improve the ability of scientists to visualize and interpret the vast amounts of data that are generated by scientific research. It was kickstarted by a $3-million institutional investment announced last year.
Read the full story at the Lehigh University News Center.