"The advantage of graphene nanowiggles is that they can easily and quickly be produced very long and clean." Nanowiggles were only recently discovered by a group led by scientists at EMPA, Switzerland. These particular nanoribbons are formed using a bottom-up approach, since they are chemically assembled atom by atom. This represents a very different approach to the standard graphene material design process that takes an existing material and attempts to cut it into a new structure. The process often creates a material that is not perfectly straight, but has small zigzags on its edges.
Meunier and his research team saw the potential of this new material. The nanowiggles could be easily manufactured and modified to display exceptional electrical conductive properties. Meunier and his team immediately set to work to dissect the nanowiggles to better understand possible future applications.
"What we found in our analysis of the nanowiggles' properties was even more surprising than previously thought," Meunier said.
The scientists used computational analysis to study several different nanowiggle structures. The structures are named based on the shape of their edges and include armchair, armchair/zigzag, zigzag, and zigzag/armchair. All of the nanoribbon-edge structures have a wiggly appearance like a caterpillar inching across a leaf. Meunier named the four structures nanowiggles and each wiggle produced exceptionally different properties.
They found that the different nanowiggles produced highly varied band gaps. A band gap determines the levels of electrical conductivity of a solid material. They also found that different nanowiggles exhibited up to five highly varied magnetic properties. With this knowledge, scientists will be able to tune the bandgap and magnetic properties of a nanostructure based on their application, according to Meunier.
Meunier would like the research to inform the design of new and better devices. "We have created a roadmap that can allow for nanomaterials to be easily built and customized for applications from photovoltaics to semiconductors and, importantly, spintronics," he said.
By using CCNI, Meunier was able to complete these sophisticated calculations in a few months.
"Without CCNI, these calculations would still be continuing a year later and we would not yet have made this exciting discovery. Clearly this research is an excellent example illustrating the key role of CCNI in predictive fundamental science," he said.
|Contact: Gabrielle DeMarco|
Rensselaer Polytechnic Institute