Size Control in the Nanoprecipitation Process of Stable Iodine (¹²⁷I) Using Microchannel Reactor-Optimization by...

Area of Science:

• Materials Science
• Chemical Engineering
• Nanotechnology

Background:

• Stable iodine ((127)I) is crucial in various applications, necessitating controlled synthesis methods.
• Nanosuspensions offer enhanced properties but require precise particle size control.
• Microfluidic devices provide a platform for controlled nanoprecipitation.

Purpose of the Study:

• To prepare stable iodine ((127)I) nanosuspensions using nanoprecipitation in microfluidic devices.
• To optimize nanoparticle size through artificial neural networks (ANNs) modeling.
• To investigate the influence of process parameters on nanoparticle size.

Main Methods:

• Nanosuspension preparation via nanoprecipitation in microfluidic devices.
• Particle size characterization using dynamic light scattering.
• Optimization of particle size using artificial neural networks (ANNs) modeling.

Main Results:

• ANNs modeling identified key input variables (solvent/antisolvent flow rate, surfactant concentration, solvent temperature) affecting nanoparticle size.
• Solvent and antisolvent flow rates showed an inverse relationship with nanoparticle size.
• Solvent temperature exhibited a complex relationship with particle size, varying with temperature values.

Conclusions:

• The study successfully demonstrated the preparation and size optimization of stable iodine ((127)I) nanosuspensions.
• Artificial neural networks (ANNs) proved effective in modeling and predicting nanoparticle size.
• Understanding the interplay between process parameters is vital for controlled nanosuspension synthesis.