Modeling of Selective Carbon Nanotubes Growth for Non-classical Memory Applications

Single wall carbon nanotubes (SWNT) have unique properties that make them potentially useful in wide variety of applications in nanoelectronics. However, these applications are feasible only if SWNTs have specific chirality. Therefore optimization of experimental conditions for Chemical Vapor Deposition (CVD) growth of SWNT in order to increase its selectivity is of great practical importance. This rational optimization is impossible without knowledge of mechanistic kinetics of CVD. It is not probably feasible to extract the information on mechanism for SWNT synthesis from experimental data. The chemical origin of the reaction barriers and intermediates, however, could be analyzed using molecular simulations. Here we propose multiscale computer modeling of CVD process. Our approach is to extract the structure of the intermediates from molecular dynamics trajectories, conduct the transition state search, predict the free energy activation barriers, build the kinetic model of the growth process, and implement it in kinetic Monte Carlo algorithm to predict the optimal experimental conditions necessary to produce desired chirality of SWNT.