Advances in nano research

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Machine learning modeling and DOE-assisted optimization in synthesis of nanosilica particles via Stöber method

  • Moradi, Hiresh (Department of Environmental Engineering, Kwangwoon University) ;
  • Atashi, Peyman (Research and Development Department, Ghaffari Chemical Industries Corp.) ;
  • Amelirad, Omid (Department of Mechanical Engineering, Sharif University of Technology) ;
  • Yang, Jae-Kyu (Department of Environmental Engineering, Kwangwoon University) ;
  • Chang, Yoon-Young (Department of Environmental Engineering, Kwangwoon University) ;
  • Kamranifard, Telma (Research and Development Department, Ghaffari Chemical Industries Corp.)
  • Received : 2021.11.14
  • Accepted : 2022.01.26
  • Published : 2022.04.25
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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.

Keywords

Acknowledgement

The authors are very grateful to the Korea Environment Industry & Technology Institute (research grant number: 2020002470002), Republic of Korea. The work reported in this paper was conducted during the sabbatical year of Kwangwoon University in 2021.

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