Optimizing Diameter of Carbon Nanotubes in CVD Processing with Neural Network

Carbon nanotubes are forth allotrope of, which have various properties such as: high strength, thermal and electrical conductivity, high young modulus and high corrosion resistibility. Th is noble physical and chemical properties can lead them to different applications in industrial, medicine and etc. Different methods exist to synthesis carbon nanotubes; such as laser ablation, arc discharge and chemical vapor deposition. Chemical vapor deposition (cvd) is the attractive way to produce carbon nanotubes. Most properties of carbon nanotubes such as: electrical, mechanical and magnetic properties; depend on length and diameter of them and on the other hand; artificial neural networks (Ann) technique is a method for calculating and processing database bases to achieve desired output parameters. In this paper, predict diameter of carbon, which synthesized via chemical vapor deposition with low percentage error (7%) by optimizing production primary parameters and sensitivity analysis of effective factors determined.