Evolution of zinc oxide nanostructures grown on graphene by ultrasonic spray pyrolysis and its statistical growth modelling
The evolution of zinc oxide nanostructures grown on graphene by alcohol-assisted ultrasonic spray pyrolysis was investigated. The evolution of structures is strongly depended on pyrolysis parameters, i.e., precursor molarity, precursor flow rate, precursor injection/deposition time, and substrate te...
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Format: | Article |
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Springer
2015
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Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/55131/ http://dx.doi.org/10.1186/s11671-015-1163-1 |
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Summary: | The evolution of zinc oxide nanostructures grown on graphene by alcohol-assisted ultrasonic spray pyrolysis was investigated. The evolution of structures is strongly depended on pyrolysis parameters, i.e., precursor molarity, precursor flow rate, precursor injection/deposition time, and substrate temperature. Field-effect scanning electron microscope analysis, energy dispersive X-ray spectroscopy, X-ray diffraction, and transmission electron microscopy were used to investigate the properties of the synthesized nanostructures and to provide evidence for the structural changes according to the changes in the pyrolysis parameters. The optimum parameters to achieve maximum density and well-defined hexagonally shaped nanorods were a precursor molarity of 0.2 M, an injection flow rate of 6 ml/min, an injection time of 10 min, and a substrate temperature of 250–355 °C. Based on the experimental results, the response surface methodology (RSM) was used to model and optimize the independent pyrolysis parameters using the Box-Behnken design. Here, the responses, i.e., the nanostructure density, size, and shape factor, are evaluated. All of the computations were performed using the Design-Expert software package. Analysis of variance (ANOVA) was used to evaluate the results of the model and to determine the significant values for the independent pyrolysis parameters. The evolution of zinc oxide (ZnO) structures are well explained by the developed modelling which confirms that RSM is a reliable tool for the modelling and optimization of the pyrolysis parameters and prediction of nanostructure sizes and shapes. |
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