Predictive Modeling of Multilayer Graphene Growth by Chemical Vapour Deposition on Co-Ni/Al2O3 Substrate using Artificial Neural Network

Muslim, May A.; Yousif, Zainab; Abdel Ghany, Mohamed A.

doi:10.30684/etj.37.1C.18

	Predictive Modeling of Multilayer Graphene Growth by Chemical Vapour Deposition on Co-Ni/Al2O3 Substrate using Artificial Neural Network
Engineering and Technology Journal
Article 18, Volume 37, 1C, 2019, Pages 113-119 PDF (735.91 K)
Document Type: Research Paper
DOI: 10.30684/etj.37.1C.18
Authors
May A. Muslim; Zainab Yousif; Mohamed A. Abdel Ghany
Department of Chemical Engineering, University of Technology, Baghdad - Iraq
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.
Keywords
artificial neural network; Chemical Vapour Deposition; Multilayer Graphene; Predictive modeling; Cobalt-Nickel substrate

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