Predictive modeling of multilayer graphene growth by chemical Vapour deposition on Co-NiAl2O3 substrate using artificial neural network

Joint Authors

Abd al-Ghani, Muhammad A.
Ayodele, Bamidele V.
Muslim, May A.
Shunayn, Zaynab Yusuf

Source

Engineering and Technology Journal

Publisher

University of Technology

Publication Date

2019-01-31

Country of Publication

Iraq

No. of Pages

7

Main Subjects

Information Technology and Computer Science

Topics

• Mathematical models
• Graphene
• Neural networks(Computer science)
• Chemical vapor deposition

English Abstract

The uniqueness of multilayer graphene as extremely high carrier mobility, tune-able band gap and high elasticity has made it be considered as a high prospect engineering material that can be employed for several applications such as solar cells, field effect transistors, super-capacitors, batteries and sensors.

In this study, the application of Artificial Neural Networks (ANN) for the predictive modeling of multilayer graphene (MLG) growth by chemical vapor deposition (CVD) on Co-Ni/Al2O3 substrate was investigated.

Data comprises temperature, catalyst compositions, ethanol flowrates were generated using central composite experimental design and employed to obtain the MLG yield as the response.

The data were subsequently used for predictive modeling using ANN.

The findings show that the predictive values of the MLG yields were in good agreement with those obtained from the experimental runs having a coefficient of determination (R2) of 0.988.

Data Type

Conference Papers

Record ID

BIM-895049

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