Nanoparticle research provides fuel for future catalyst designs.

The new year offers promising discoveries in a relatively new science, nanotechnology. While sci-fi writers predict nanorobots will be able to invade our cells in order to conquer the world for the forces of evil, physicists in the University of Pennsylvania's chemistry department and Laboratory for Research on the Structure of Matter apply complex nanotechnology simulations to pollution control for the forces of good. They predict they will be able to help clean our environment by continuing research on the properties of metal nanoparticles, also known as "nanoislands" or "clusters," which could someday soon lower emission rates in automobiles' catalytic converters. Metal nanoparticles might also prove useful in hydrogen fuel cells, systems proposed by many as a future alternative to combustion engines.

Andrew Rappe, who leads this research project, is no stranger to this realm. Since 1995, he and his team have been conducting computational experiments on nanosurfaces using NCSA resources. His team includes graduate students Valentino Cooper, Sara Mason, and Myung-Won Lee; post-docs Yashar Yourdshahyan, Na Sai, and Ilya Grinberg; and Russel Kauffman, a physics professor at Muhlenberg College in Allentown, Pennsylvania.

"Without NCSA resources, we would not be as effective," Rappe says.

Through their first-principles computations, Rappe's team explores the effect of materials modification on the properties of metal nanoparticles supported on oxide substrates. The main goal is to gain a fundamental understanding of how particle size and composition influence the structural, electronic, and chemical properties of the supported particle. Supported nanoparticles are an important subject because they can be used to gain insight into complex real-world systems, bridging the gap between fundamental research and future catalyst applications. Currently, the team can model the fundamental chemical reactions that a catalytic converter must perform, in the presence of a simplified but fairly realistic model of a nanoparticle catalyst. This process allows the team to predict with confidence which particle sizes and compositions could lead to better catalysts than exist today.

Access Online | Posted 7-15-2003