Design of experiments (DOE) approach for optimizing ZnS nanoparticle morphology via displacement synthesis

The controlled tailoring of zinc sulfide (ZnS) nanoparticle morphology is essential for achieving predictable optical, electronic, and catalytic performance in semiconductor applications. Displacement synthesis offers a simple and scalable route to ZnS formation; however, morphological outcomes are highly sensitive to the interacting reaction parameters, including precursor concentration, temperature, pH, reaction time, and stirring conditions. This study proposes a structured Design of Experiments (DOE) framework to systematically optimize the morphology of ZnS nanoparticles produced via displacement reactions. A multivariate experimental matrix based on factorial and response surface methodologies was employed to quantify the individual and interaction effects of critical process variables. Morphological characteristics, including particle size, aspect ratio, and agglomeration tendency, were correlated with statistical models to identify optimal synthesis windows. Analysis of variance (ANOVA) confirmed the statistical significance of key factors and their coupled effects, while predictive regression models demonstrated strong agreement with experimental observations. The optimized conditions enabled reproducible synthesis of narrowly distributed ZnS nanoparticles with controlled structural features. The proposed DOE-guided strategy reduces experimental redundancy, enhances process reproducibility, and provides a scalable pathway for morphology-driven functional tuning of ZnS nanomaterials. This methodology can be extended to other metal chalcogenide systems where precise nanostructure control is required.