A machine learning technique, namely, support vector regression, is implemented to enhance single-walled carbon nanotube (SWCNT) thin-film performance for transparent and conducting applications. We collected a comprehensive data set describing the influence of synthesis parameters (temperature and CO 2 concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi-industrial aerosol (floating-catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the data set shows principal applicability of the method for refining synthesis conditions toward the advanced optoelectronic performance of multiparameter processes such as nanotube growth. Further doping of the improved carbon nanotube films with HAuCl 4 results in the equivalent sheet resistance of 39 Ω/□one of the lowest values achieved so far for SWCNT films.
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 residence time and temperature) to control the performance of produced nanotube films (yield, mean and standard deviation of 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.
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