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Advances in Nano Research Volume 12, Number 4, April 2022 , pages 387-403 DOI: https://doi.org/10.12989/anr.2022.12.4.387 |
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Machine learning modeling and DOE-assisted optimization in synthesis of nanosilica particles via Stöber method |
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Hiresh Moradi, Peyman Atashi, Omid Amelirad, Jae-Kyu Yang,
Yoon-Young Chang and Telma Kamranifard
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| Abstract | ||
| Silica nanoparticles, which have a broad range of sizes and specific surface features, have been used in many industrial applications. This study was conducted to synthesize monodispersed silica nanoparticles directly from tetraethyl orthosilicate (TEOS) with an alkaline catalyst (NH3) based on the sol–gel process and the Stöber method. A central composite design (CCD) is used to build a second-order (quadratic) model for the response variables without requiring a complete three-level factorial experiment. The process was then optimized to achieve the minimum particle size with the lowest concentration of TEOS. Dynamic light scattering and scanning electron microscopy were used to analyze the size, dispersity, and morphology of the synthesized nanoparticles. After optimization, a confirmation test was carried out to evaluate the confidence level of the software prediction. The results revealed that the predicted optimization is consistent with experimental procedures, and the model is significant at the 95% confidence level. | ||
| Key Words | ||
| design of experiments (DOE); machine learning; nanoparticles; silica; Stöber method | ||
| Address | ||
| Hiresh Moradi Jae-Kyu Yang and Yoon-Young Chang: Department of Environmental Engineering, Kwangwoon University, Seoul, Korea Peyman Atashi and Telma Kamranifard: Research and Development Department, Ghaffari Chemical Industries Corp., Tehran, Iran Omid Amelirad: Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran | ||