Towards control of carbon nanotube synthesis process using prediction-based fast monte carlo simulations

Effective control of carbon nanotube (CNT) lengths is important for various industrial applications. The current in situ measurement techniques are expensive, and mostly provide intermittent (1 min) recordings. Atomistic Monte Carlo (MC) simulation of CNT synthesis process can be used to supplement these intermediate measurements with continuous growth estimates, necessary for precise control over lengths and other geometric features. However the current MC models can only simulate early growth stage due to the computational overhead involved. We present an approach to accelerate this MC simulation from a chemical vapor deposition process through predicting the nonlinear and nonstationary growth evolution. The fast MC simulation can be used to monitor length variation of CNTs during synthesis process, and detect the synthesis end-point for desired CNT length. Extensive simulation studies indicate that over 88% of the generated CNTs are within the 95% confidence interval of length specification.

Towards control of carbon nanotube synthesis process using prediction-based fast monte carlo simulations Conference Paper