CDS&E: Development of Methods for Molecular Simulation of Enantiomeric Separation and Metal-oxide Formation

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

Professor J. Daniel Gezelter of the University of Notre Dame is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop novel computer algorithms that model or predict molecular motion. These new methods will predict how molecules that are left- or right-handed versions of each other can be separated with relatively simple and inexpensive liquid flow devices. Gezelter’s lab also models the behavior of metallic surfaces in contact with reactant gases and solvents, particularly when there is a heat or energy imbalance between the two sides of the surface. These topics are of interest in the areas of energy efficiency (involving systems like catalytic converters), materials science (involving metallic nanoparticles), and specifically in the pharmaceutical industry, where important drug molecules often must be separated from their left- or right-handed forms to isolate the useful molecular component from one that is either ineffective or dangerous. In addition, Professor Gezelter is developing a new program for first-year science and engineering students who are either first generation college students or come to college from under-served populations. Some of these students are now conducting research on Notre Dame’s campus (including in Professor Gezelter's research group). Dr. Gezelter and his group are developing theoretical and computational methods for investigating enantiomeric separation, coupled charge and energy transport properties (e.g. thermoelectric properties and electron-phonon effects) and oxide-layer growth at interfaces. Three complementary areas of research are under investigation: methods for predicting and simulating enantiomeric separation in vortex flows, utilizing an overlapping bead model to predict rotation-translation coupling tensors for molecules in fluid flows, methods to include electrical current densities in molecular simulations using reverse non-equilibrium molecular dynamics (RNEMD), and a density readjusting embedded atom method (DR-EAM) for modeling oxide-layer growth on metal surfaces. The applications of these methods include separations of racemic mixtures of pharmaceutical molecules in shear-flow and vortex-flow devices, the growth of metal-oxide layers on functional catalysts at operating conditions, and the thermal conductance measurements on metallic particles that have been ligated and solvated in water solvent after excitation at the plasmon resonance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.