Optimizing Diameter of Carbon Nanotubes in CVD Processing with Neural Network
Publish place: Development on Science and Chemical Industry Conference
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
SCIC01_077
تاریخ نمایه سازی: 10 تیر 1396
Abstract:
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. This 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
Keywords:
diameter of carbon nanotubes predict , Artificial Neural Network , chemical vapor deposition , sensitivity analysis
Authors
Seyed Oveis Mirabootalebi
Material science and engineering, Shahid Bahonar university of kerman,
Reza Mirahmadi Babaheydari
Material science and engineering, Shahid Bahonar university of kerman,
Gholam Reza Khayati
Department of Materials Science and Engineering, Shahid Bahonar University of Kerman,
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