Monte Carlo simulation of carbon nanotube nucleation and growth using nonlinear dynamic predictions

The computation time for Monte Carlo (MC) simulation of a nanostructure growth process was shown to be reduced by an order of magnitude compared to conventional atomistic and meso-scale models through the prediction of the structure evolution ahead of every growth step. This approach used to grow of one of the longest (∼194 nm) reported carbon nanotubes (CNTs) from atomistic simulations. The key to the approach is the finding from simulation experiments that the CNT synthesis process exhibits nonlinear and recurring near-stationary dynamics. © 2012 Elsevier B.V. All rights reserved.

Monte Carlo simulation of carbon nanotube nucleation and growth using nonlinear dynamic predictions Academic Article