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Sensors 2014, 14(2), 2549-2560; doi:10.3390/s140202549
Article

Simultaneous Optimization of Nanocrystalline SnO2 Thin Film Deposition Using Multiple Linear Regressions

1,*  and 2,*
1 Department of Nanotechnology, Ahar Branch, Islamic Azad University, Ahar 54515, Iran 2 Department of Physics, Universiti Putra Malaysia, UPM Serdang 43400, Malaysia
* Authors to whom correspondence should be addressed.
Received: 7 December 2013 / Revised: 29 December 2013 / Accepted: 6 January 2014 / Published: 6 February 2014
(This article belongs to the Section Physical Sensors)
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Abstract

A nanocrystalline SnO2 thin film was synthesized by a chemical bath method. The parameters affecting the energy band gap and surface morphology of the deposited SnO2 thin film were optimized using a semi-empirical method. Four parameters, including deposition time, pH, bath temperature and tin chloride (SnCl2·2H2O) concentration were optimized by a factorial method. The factorial used a Taguchi OA (TOA) design method to estimate certain interactions and obtain the actual responses. Statistical evidences in analysis of variance including high F-value (4,112.2 and 20.27), very low P-value (<0.012 and 0.0478), non-significant lack of fit, the determination coefficient (R2 equal to 0.978 and 0.977) and the adequate precision (170.96 and 12.57) validated the suggested model. The optima of the suggested model were verified in the laboratory and results were quite close to the predicted values, indicating that the model successfully simulated the optimum conditions of SnO2 thin film synthesis.
Keywords: nanocrystalline SnO2; thin film; modeling; ANOVA; energy band gap nanocrystalline SnO2; thin film; modeling; ANOVA; energy band gap
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ebrahimiasl, S.; Zakaria, A. Simultaneous Optimization of Nanocrystalline SnO2 Thin Film Deposition Using Multiple Linear Regressions. Sensors 2014, 14, 2549-2560.

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