Optimization of process parameters on grain size of Fe3O4 nanoparticles by support vector regression

Support vector machine (SVM), which is a novel technology for solving classification and regression issues, has been successfully used in many fields. In this study, according to an experimental dataset on the average grain size of Fe<inf>3</inf>O<inf>4</inf> nanoparticles, a predicting and optimizing model using support vector regression (SVR) was developed. In this model, the estimated result of SVR agreed with the experimental data well. In addition, the particle swarm optimization (PSO) algorithm is employed for optimizing the parameters of SVR models and obtaining the optimal process parameters for preparing Fe<inf>3</inf>O<inf>4</inf> nanoparticles. The minimum grain size of Fe<inf>3</inf>O<inf>4</inf> nanoparticles is forecasted to be 10nm while the Fe<inf>3</inf>O<inf>4</inf> nanoparticles are synthesized by using the optimal process parameters. Meanwhile, multifactor analysis is conducted for investigating the influence of process parameters on the average grain size of Fe<inf>3</inf>O<inf>4</inf> nanoparticles. The results suggest that SVR is capable of providing important theoretical and practical guide in research and development of Fe<inf>3</inf>O<inf>4</inf> nanoparticles possessing ideal grain size.

[1]  Deyan He,et al.  Designed synthesis of wide range microwave absorption Fe3O4–carbon sphere composite , 2010 .

[2]  Y. Z. Chen,et al.  Protein function classification via support vector machine approach. , 2003, Mathematical biosciences.

[3]  Y. Wang,et al.  Tailoring size and structural distortion of Fe3O4 nanoparticles for the purification of contaminated water. , 2009, Bioresource technology.

[4]  Biao Yan,et al.  Preparation and magnetic properties of magnetite nanoparticles by sol-gel method , 2010 .

[5]  Cui Sheng.  Optimization of processing conditions for the preparation condition of Fe_3O_4 nanoparticle , 2005 .

[6]  R. F. Jardim,et al.  Magnetic properties of Fe3O4 nanoparticles coated with oleic and dodecanoic acids , 2010, 1004.2231.

[7]  Peng Xu,et al.  Magnetic properties and microwave absorption properties of carbon fibers coated by Fe3O4 nanoparticles , 2010 .

[8]  Toshinobu Yogo,et al.  Magnetic and rheological properties of monodisperse Fe3O4 nanoparticle/organic hybrid , 2009 .

[9]  N. Li,et al.  Preparation of Fe3O4 particles from copper/iron ore cinder and their microwave absorption properties , 2010 .

[10]  Yu Zong Chen,et al.  Support Vector Machine Classification Of Physical And Biological Datasets , 2003 .

[11]  Yanjun Cui,et al.  Facile synthesis of amino-silane modified superparamagnetic Fe3O4 nanoparticles and application for lipase immobilization. , 2010, Journal of biotechnology.

[12]  X. Chen,et al.  SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence , 2003, Nucleic Acids Res..

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  C. Cai,et al.  Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression , 2009 .

[15]  Ying Zhang,et al.  Synthesis of PVP-coated ultra-small Fe3O4 nanoparticles as a MRI contrast agent , 2010, Journal of materials science. Materials in medicine.

[16]  C. Cai,et al.  Superconducting Transition Temperature Tc Estimation for Superconductors of the Doped MgB2 System Using Topological Index via Support Vector Regression , 2010 .

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[19]  Deyan He,et al.  Size controlled and morphology tuned fabrication of Fe3O4 nanocrystals and their magnetic properties , 2010 .