Comprehensive models of non-covalent aromatic interactions

University Of South Carolina At Columbia, Columbia SC

Investigators

Abstract

The weak attractive forces between molecules determine many properties of matter. Examples include melting temperature, degree of expansion upon heating, hardness, and resistance to breaking. Prof. Ken D. Shimizu of the University of South Carolina aims to develop methods to accurately measure and predict these attractive forces between molecules. This project provides research training for graduate and undergraduate students, in particular via collaboration with a faculty member at a women's college and her students. The project also supports the development of an educational website targeted at teaching undergraduate students organic chemistry via a problem based approach. The goal of this project supported by the Macromolecular, Supramolecular and Nanochemistry Program is to develop models that can accurately predict the non-covalent interaction energies of aromatic surfaces such as stacking, edge-to-face, and CH-pi interactions. An innovative aspect of this project is the use of specifically designed molecules to measure these interactions. The versatility of the 'molecular balances' enables the study of a broader range of molecular surfaces and environments and the direct comparison of their attractive or repulsive forces. First, an expansive database of experimentally measured interactions is collected using a 'molecular balance'. The 'balance' flips between two conformational states in which an intramolecular aromatic interaction is formed and broken. Thus, the conformational ratio provides a sensitive and accurate measure of these weak interactions. Second, the importance and magnitude of individual contributing forces to the aromatic interactions is evaluated. Of particular interest are the dispersion and solvophobic terms as their role in solution is still unclear. Finally, a multivariate approach is employed using multiple molecular and solvent descriptors to develop a more accurate predictive model and also to assess the relative strengths of the contributing forces.