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Researchers develop new model to predict optical properties of nanostructures

By R&D Editors | March 25, 2011

UBC chemists have developed a new model to
predict the optical properties of non-conducting ultra-fine particles.

The finding could help inform the design of
tailored nanostructures, and be of utility in a wide range of fields, including
the remote sensing of atmospheric pollutants and the study of cosmic dust
formation.

Aerosols and nanoparticles play a key role
in atmospheric processes as industrial pollutants, in interstellar chemistry
and in drug delivery systems, and have become an increasingly important area of
research. They are often complex particles made up of simpler building blocks.

Now research published by UBC chemists
indicates that the optical properties of more complex non-conducting nanostructures
can be predicted based on an understanding of the simple nano-objects that make
them up. Those optical properties in turn give researchers and engineers an
understanding of the particle’s structure.

“Engineering complex nanostructures
with particular infrared responses typically involves hugely complex
calculations and is a bit hit and miss,” says Thomas Preston, a researcher
with the UBC Department of Chemistry.

“Our solution is a relatively simple
model that could help guide us in more efficiently engineering nanomaterials
with the properties we want, and help us understand the properties of these
small particles that play an important role in so many processes.”

The findings were published in the Proceedings of the National Academy of
Sciences
.

“For example, the properties of a more
complex particle made up of a cavity and a core structure can be understood as
a hybrid of the individual pieces that make it up,” says UBC Professor
Ruth Signorell, an expert on the characterization of molecular nano-particles
and aerosols and co-author of the study.

The experiment also tested the model
against CO2 aerosols with a cubic shape, which play a role in cloud
formation on Mars.

The research was supported by the Natural
Sciences and Engineering Research Council of Canada and the Canada Foundation
for Innovation.

SOURCE

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