Multiscale-multiphysics predictive modeling of chemical vapor deposition processes for carbon nanotube synthesis

The synthesis of carbon nanotubes has attracted considerable interest due to their unique physical and structural properties. Despite notable experimental advancements, particularly in chemical vapor deposition (CVD) techniques, a significant gap remains in developing comprehensive mechanistic models that correlate nanotube growth dynamics with gas-phase composition. The CVD involves a complex interplay of multiscale phenomena, including hydrocarbon transport within the reactor and surface reactions on catalyst nanoparticles, collectively contributing to nucleation and growth. This paper introduces a computational modeling framework that integrates these phenomena by leveraging density functional theory energy data, microkinetic modeling, and computational fluid dynamics. The proposed approach addresses the challenges inherent in this multiscalemultiphysics problem, providing insights into nanotube growth as a function of gas composition and transport, temperature, and catalyst properties. The simulation results show strong agreement with experimental trends, highlighting the significance of gas-phase reactions in a mixed hydrocarbon feedstock and the effects of catalyst deactivation.