Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions
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 chlor...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/78045/1/78045.pdf |
| _version_ | 1825950958606090240 |
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| author | Ebrahimiasl, Saeideh Zakaria, Azmi |
| author_facet | Ebrahimiasl, Saeideh Zakaria, Azmi |
| author_sort | Ebrahimiasl, Saeideh |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T10:22:34Z |
| format | Article |
| id | upm.eprints-78045 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:22:34Z |
| publishDate | 2014 |
| publisher | MDPI |
| record_format | dspace |
| spelling | upm.eprints-780452020-05-03T22:31:28Z http://psasir.upm.edu.my/id/eprint/78045/ Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions Ebrahimiasl, Saeideh Zakaria, Azmi 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. MDPI 2014 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/78045/1/78045.pdf Ebrahimiasl, Saeideh and Zakaria, Azmi (2014) Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions. Sensors, 14 (2). pp. 2549-2560. ISSN 1424-8220 https://www.mdpi.com/1424-8220/14/2/2549 10.3390/s140202549 |
| spellingShingle | Ebrahimiasl, Saeideh Zakaria, Azmi Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions |
| title | Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions |
| title_full | Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions |
| title_fullStr | Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions |
| title_full_unstemmed | Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions |
| title_short | Simultaneous optimization of nanocrystalline SnO2 thin film deposition using multiple linear regressions |
| title_sort | simultaneous optimization of nanocrystalline sno2 thin film deposition using multiple linear regressions |
| url | http://psasir.upm.edu.my/id/eprint/78045/1/78045.pdf |
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