Carbon nanotubes (CNTs) have received much attention from both the scientific and industrial communities due to their structural properties and unique morphology. There has also been growing interest in vertically aligned single walled carbon nanotubes (VA-SWNTs) because of their suitability for building devices such as hydrogen storage and super capacitors. Various methods including chemical vapor deposition (CVD) have been developed for growing VA-SWNTs. Among them is alcohol catalytic CVD which is well known for its economic viability, comprehensive substrates selectivity and good yield of VA-SWNTs. In order to fully understand the growth mechanism of those CNTs, an examination of the role of inputs like hydrocarbon flow rate, reaction time, chamber temperature, and pressure is essential. This work studies the controllability of VA-SWNTs growth by a hybrid process model of an experimental design and an artificial neural network (ANN). Process analysis shows that CVD pressure and temperature are the most significant input factors. In addition, interactions and response surface plots confirm these results and add that higher temperature and pressure will yield VA-SWNTs with high probability.

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