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research
Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks
Authors
S. Abbasi
M. Aghajani
+5 more
M.H. Aghajani
M. Assadi
M.-J. Hajibagheri-Fard
S.H. Mostafavi
A.M. Pashazadeh
Publication date
1 January 2015
Publisher
Springer New York LLC
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PubMed
Abstract
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. © 2015, American Association of Pharmaceutical Scientists
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Bushehr University of Medical Sciences Repository
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oai:eprints.bpums.ac.ir:6361
Last time updated on 08/07/2018
Golestan University of Medical Sciences Repository
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oai:eprints.goums.ac.ir:4698
Last time updated on 11/11/2016