Effect of synthesis parameters on dimensional characteristics of Fe3O4 nanoparticles: neural-network research
A.V. Blinov, A.A. Gvozdenko, M.A. Yasnaya, A.B. Golik, А.A. Blinova, I.M. Shevchenko, V.N. Kramarenko
North-Caucasian Federal University, Stavropol, Russia
Abstract: Our research shows the possibility of using the neural-network processing of experimental data to study the influence of various factors on the process of synthesis of nanoscale Iron (II, III) oxide. A mathematical model was obtained which adequately describes the effect of temperature, stabilizer mass and precipitant quantity on the size of nanoparticles of Iron (II, III) oxide. The optimal synthesis conditions were determined, which provide a high content of Fe3O4 particles with an average hydrodynamic radius less than 100 nm.
Keywords: double iron oxide nanoparticles, neural network modeling, multilayer perceptron, response surface, dynamic light scattering method.
Blinov, A.V. Effect of synthesis parameters on dimensional characteristics of Fe3O4 nanoparticles: neural-network research / A.V. Blinov, A.A. Gvozdenko, M.A. Yasnaya et al. // Physical and chemical aspects of the study of clusters, nanostructures and nanomaterials: Interuniversity collection of proceedings / Ed. by V.M. Samsonov, N.Yu. Sdobnyakov. – Tver: TSU, 2019. – I. 11. – P. 298-306.
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