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Predicting Carbon Nanotube Diameter Using Artificial Neural Network Along with Characterization and Field Emission Measurement

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Abstract

The growth process of carbon nanotubes (CNTs) by chemical vapor deposition is modeled using multilayered perceptron artificial neural networks technique. The model was trained using six growth parameters. Numerous values for these parameters were gathered from published articles. The six growth parameters were catalyst thickness, pressure of acetylene gas, temperature and time of pretreatment and growth process. These parameters were used as input data for the determination of the CNTs’ diameter as the output of the model. Experiments were run using the predicted output value, and the synthesized CNTs were evaluated by scanning electron microscopy and transmission electron microscopy. The grown CNTs were multiwall with a bamboo structure and tip-growth mechanism. Comparison between the model prediction and the experimental results showed a remarkable agreement with an insignificant error (approximately 7%). X-ray diffraction and Raman spectrometer analysis results showed that with increasing temperature and time of pretreatment, the structural defects of CNTs were reduced, and the purity and thus field emission properties will be increased.

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Acknowledgements

The authors would like to thank Iranian National Science Foundation (INSF) for their financial support of this research (Grant No. 90001088).

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Authors and Affiliations

  1. Materials Engineering Faculty, Sahand University of Technology, Tabriz, Iran

    S. Nasiri Khalil Abad

  2. Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran

    E. Ganjeh

  3. Advanced Materials and Nanotechnology Research Laboratory, Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran

    A. Zolriasatein

  4. Department of Electrical Engineering, Shiraz University, Shiraz, Iran

    F. Shabani-Nia

  5. Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran

    M. H. Siadati

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  1. S. Nasiri Khalil Abad
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  2. E. Ganjeh
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  3. A. Zolriasatein
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  4. F. Shabani-Nia
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  5. M. H. Siadati
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Correspondence to E. Ganjeh.

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Abad, S.N.K., Ganjeh, E., Zolriasatein, A. et al. Predicting Carbon Nanotube Diameter Using Artificial Neural Network Along with Characterization and Field Emission Measurement. Iran J Sci Technol Trans Sci 41, 151–163 (2017). https://doi.org/10.1007/s40995-017-0198-9

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  • DOI: https://doi.org/10.1007/s40995-017-0198-9

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