Dynamical fluctuations in proteins, organic semiconductors, and nanoparticle catalysts

Cornell University, Ithaca NY

Investigators

Abstract

Roger Loring of Cornell University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry division to develop detailed models and perform calculations that interpret and motivate new measurements of molecular motions. The motions of molecules determine chemical and biological processes, as well as mechanical and electronic properties of materials. Recent experimental techniques obtain information regarding these motions on ever smaller length scales and shorter time scales. Loring and graduate student coworkers model mechanical vibrations and energy flow in biomolecules such as proteins, individual events in chemical reactions catalyzed by the surface of small gold particles, and charge carrier transport in molecular semiconductors with the potential for use in plastic electronics. Graduate students contribute to K-12 education through teacher training and enrichment programs coordinated by Cornell University. Multidimensional vibrational spectroscopy probes structure and relaxation in biomolecules, but full interpretation requires modeling the measurement with an atomic level of detail. New methods for computing nonlinear vibrational response functions based on the semiclassical mean-trajectory approach approximate quantum dynamics using input from classical simulations. Electric force microscopy has the capacity to measure charge carrier dynamics in a disordered organic semiconductor. Calculations from classical statistical mechanics and classical electrodynamics of the observables of electric force microscopy, noncontact friction and probe frequency noise, reveal the connections between these quantities and charge transport in an organic electronic device. Single-turnover studies of nanoparticle catalysis measure fluctuations from the mean kinetics observed in a bulk measurement. Loring and coworkers develop statistical measures of correlations in time and space among chemical events at different active sites on metal nanostructures, elucidating the analogs in inorganic catalysts of allosteric effects in enzymes.