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

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 Stober 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.

Automated summary

This study synthesized monodispersed silica nanoparticles directly from TEOS using the sol-gel process and the Stober method. By optimizing the process with a central composite design, the smallest particle size and lowest TEOS concentration were achieved. The results showed that the predicted optimization was consistent with experimental procedures and the model was significant at a 95% confidence level.