1 Department of Chemistry and Biochemistry and the Centre for NanoScience Research, Concordia University, Montreal, Quebec H3G 1M8, Canada. rafik.naccache@concordia.ca.
2 Quebec Centre for Advance Materials, Concordia University, Montreal, Quebec H3G 1M8, Canada.

CuInSe₂ nanoparticles are promising materials for solar energy conversion owing to their broad and tunable optical absorption and the absence of heavy metals such as cadmium or lead. However, current synthesis methods for ternary chalcogenides are largely constrained by inefficient one-variable-at-a-time strategies, limiting the understanding of how reaction parameters and their interactions influence nanoparticle properties. Here, we apply a statistical approach based on a Box-Behnken response surface model to evaluate the influence of temperature (200-240 °C), reaction time (5-10 min), and ligand composition defined by different oleylamine/oleic acid volume combinations (1/3, 2/2, and 3/1 mL) on nanoparticle average size which varied from 7.3 to 15.4 nm depending on synthesis conditions. Statistical analysis showed that all three variables were significant (p < 0.05), with temperature having the strongest influence, achieving a predicted R² of 0.85. Oleylamine-rich conditions favored smaller nanoparticles (~12.5 nm), whereas oleic-acid-rich mixtures produced larger ones (~15 nm) at 240 °C. Interactions among temperature, time, and ligand composition also revealed variations in growth rate resulting from the combined effects of these variables. Representative conditions from the model were then selected to study how ZnS passivation affects photoluminescence. Among these, samples synthesized at 200 °C showed the most intense emission, particularly under oleic-acid-rich conditions, suggesting that low temperature and higher oleic acid content promote more effective surface passivation and radiative recombination. This statistical approach can be extended to other chalcogenide systems that share similar synthesis conditions, allowing the systematic study of multiple variables at once. By showing how synthesis factors and their interactions influence average size and surface characteristics, it provides a predictive basis for more controlled nanoparticle design.