Artificial neural network for predictive synthesis of single-walled carbon nanotubes by aerosol CVD method

We propose to use artificial neural networks to process the experimental data and to predict the performance of the aerosol CVD synthesis of single-walled carbon nanotubes based on Boudouard reaction. We employ five key input parameters of the growth (pressures of CO, CO2 and ferrocene as well as the residence time and the growth temperature) to control the performance of produced nanotube films (yield, mean and standard deviation of the diameter distribution, and defectiveness). The prediction errors were found to be comparable with the corresponding experimental errors. We believe the proposed approach is of great interest for the synthesis of nanocarbons with tailored characteristics. A schematic representation of the hierarchical architecture of the experimental setup and artificial neural network connection for controlled and predictive SWCNT synthesis. [Display omitted]