Modeling of Green Synthesis of ZnO Nanoparticles for Water Treatment

Green synthesis of zinc oxide nanoparticles (ZnO NPs) represents a sustainable alternative to conventional chemical synthesis by employing biocatalytic systems under environmentally benign conditions. This study presents a systematic modeling approach based on Design of Experiments (DoEs) to optimize the green synthesis of ZnO nanoparticles intended for water treatment applications. Fractional factorial design was first applied to identify the most influential synthesis parameters, including precursor concentration, bio-reductant ratio, pH, temperature, and reaction time. Subsequently, response surface methodology using a D-optimal experimental design was employed to establish predictive mathematical models describing the relationships between process variables and nanoparticle size. The developed models enabled identification of optimal synthesis conditions and prediction of parameter combinations required to produce nanoparticles with targeted dimensions suitable for enhanced photocatalytic activity, antimicrobial performance, and colloidal stability. The proposed data-driven optimization strategy provides a robust, reproducible, and scalable protocol for green ZnO nanoparticle synthesis while minimizing reagent consumption and environmental impact. These findings contribute to the development of sustainable nanomaterials as the first step toward the future application of these ZnO nanoparticles as sustainable photocatalysts for water treatment.