RESUMEN
In this study, nanosuspension of stable iodine ((127)I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was evaluated by dynamic light scattering. The response surfaces obtained from ANNs model illustrated the determining effect of input variables (solvent and antisolvent flow rate, surfactant concentration, and solvent temperature) on the output variable (nanoparticle size). Comparing the 3D graphs revealed that solvent and antisolvent flow rate had reverse relation with size of nanoparticles. Also, those graphs indicated that the solvent temperature at low values had an indirect relation with size of stable iodine ((127)I) nanoparticles, while at the high values, a direct relation was observed. In addition, it was found that the effect of surfactant concentration on particle size in the nanosuspension of stable iodine ((127)I) was depended on the solvent temperature. Nanoprecipitation process of stable iodine (127I) and optimization of particle size using ANNs modeling.