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Electronics
are getting smaller and smaller, flirting with new devices at the atomic scale.
However, many scientists predict that the shrinking of our technology is
reaching an end. Without an alternative to silicon-based technologies, the
miniaturization of our electronics will stop. One promising alternative is
graphene. Pure graphene is not a semiconductor, but it can be altered to
display exceptional electrical behavior. Finding the best graphene-based
nanomaterials could usher in a new era of nanoelectronics, optics, and spintronics.
Scientists
at Rensselaer Polytechnic Institute have used the capabilities of one of the
world’s most powerful university-based supercomputers, the Rensselaer Center
for Nanotechnology Innovations (CCNI), to uncover the properties of a promising
form of graphene, known as graphene nanowiggles. What they found was that
graphitic nanoribbons can be segmented into several different surface
structures called nanowiggles. Each of these structures produces highly
different magnetic and conductive properties. The findings provide a blueprint
that scientists can use to literally pick and choose a graphene nanostructure
that is tuned and customized for a different task or device. The work provides
an important base of knowledge on these highly useful nanomaterials.
The
findings were published in Physical
Review Letters in a paper titled “Emergence of Atypical Properties
in Assembled Graphene Nanoribbons.”
“Graphene
nanomaterials have plenty of nice properties, but to date it has been very
difficult to build defect-free graphene nanostructures. So these
hard-to-reproduce nanostructures created a near insurmountable barrier between
innovation and the market,” said Vincent Meunier, the Gail and Jeffrey L.
Kodosky ’70 Constellation Professor of Physics, Information Technology, and
Entrepreneurship at Rensselaer. “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.
