Bar?? ?im?ek. TOPSIS based Taguchi design optimization for CVD growth of graphene using different carbon sources: Graphene thickness, defectiveness and homogeneity[J]. 中国化学工程学报(英文版), 2019,27(3):685-694.
Barış Şimşek. TOPSIS based Taguchi design optimization for CVD growth of graphene using different carbon sources: Graphene thickness, defectiveness and homogeneity[J]. Chin.J.Chem.Eng., 2019, 27(3): 685-694.
Bar?? ?im?ek. TOPSIS based Taguchi design optimization for CVD growth of graphene using different carbon sources: Graphene thickness, defectiveness and homogeneity[J]. 中国化学工程学报(英文版), 2019,27(3):685-694.DOI: 10.1016/j.cjche.2018.08.004.
Barış Şimşek. TOPSIS based Taguchi design optimization for CVD growth of graphene using different carbon sources: Graphene thickness, defectiveness and homogeneity[J]. Chin.J.Chem.Eng., 2019, 27(3): 685-694.DOI: 10.1016/j.cjche.2018.08.004.
TOPSIS based Taguchi design optimization for CVD growth of graphene using different carbon sources: Graphene thickness, defectiveness and homogeneity
Chemical inhomogeneity of chemical vapor deposition (CVD) grown graphene compromises its usage in highperformance devices. In this study
TOPSIS based Taguchi optimization was performed to improve thickness uniformity and defect density of CVD grown graphene. 1.56% decrease in the mean 2D/G intensity ratio
87.96% improvement in the mean D/G intensity ratio
56.07% improvement in the standard deviation D/G intensity ratio
25.21% improvement in the standard deviation 2D/G intensity ratio
and 69.32% improvement in the surface roughness were achieved with TOPSIS based Taguchi optimization. The statistical differences between the copper and silicon substrates have been found significantly in terms of their impacts on the graphene's properties with the 0.000 p-value for the mean D/G intensity ratio and with the 0.009 p-value for the mean 2D/G intensity ratio
respectively. Graphene having 11% lower mean D/G intensity ratio (low defective graphene products) compared to the values given in the literature using single-response optimization was obtained using multi-response optimization.
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