Prediction of size, precursor ratio and monodispersity of silica nanospheres though adaptive neuro- fuzzy inference system - Anish Nair; Shanmugapriya V; M.S. Revathy; Naidu Dhanpal Jayram (Chemical Physics Impact, 2022)
Highly monodisperse nanostructures are becoming the centre of focus in the field of material science towards the application of sensors, photocatalysis, gas sensing, antibacterial activity, drug delivery and energy applications. Monodisperse resembles uniform nanostructures towards better device performances and applications. In order to minimize the reaction time and characterization costs, an attempt has been made for development of a prediction model for the synthesis of monodisperse silica nanospheres using Adaptive Neuro-Fuzzy Inference System (ANFIS) software. Experimental parameters of the Stober method such as precursor's ratio (ethanol, water and ammonia), Tetraethyl Orthosilicate (TEOS) and sphere size were predicted in the model. The results from the prediction model were used for carrying out experimentation on thin films using vertical deposition technique. The prepared substrates were characterized by FE-SEM & XRD analysis. Obtained Experimental results shows that nanosphere with a size range of 200- 250 nm will form monodisperse layer. XRD analysis confirms the amorphous nature of SiO2 film. ANFIS has predicted best suitable size of silica nanospheres and optimized precursor's ratios for attaining highly monodisperse structure. The optimized parameters predicted from the ANFIS network matches well with the experimental results.