Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks
- 247 Downloads
In this study, nanosuspension of stable iodine (127I) 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 (127I) 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 (127I) was depended on the solvent temperature.
KEY WORDSANNs microfluidic nanoprecipitation particle size stable iodine
This project was supported by the vice-chancellor of research at Bushehr University of Medical Sciences and Health Services grant no 20-18-3-46333. The author wishes also to thank Dr. Afshin Ostovar for his support in this research.
Conflict of Interest
The authors express that they have no conflicts of interest declaration to display.
- 1.Lakshmi P, Kumar GA. Nanosuspension technology: a review. Int J Pharm Sci. 2010;2(4):35–40.Google Scholar
- 4.Chingunpituk J. Nanosuspension technology for drug delivery. Walailak J Sci Tech. 2007;4(2):139–53.Google Scholar
- 12.Crowe CT, Elger DF, Roberson JA. Engineering fluid mechanics. Hoboken: Wiley; 2005.Google Scholar
- 22.Amani A, Mohammadyani D. Artificial neural networks: applications in nanotechnology. Artificial neural networks—application Rijeka. INTECH; 2011.Google Scholar
- 32.Fokin VM, Yuritsyn NS, Zanotto ED. Nucleation and crystallization kinetics in silicate glasses: theory and experiment. Nucleation Theory Appl. 2005:74–125.Google Scholar