Study on the Process Optimization of Synthesizing Co3O4 Nanoparticles by Homogeneous Precipitation Based on Support Vector Regression
The Co3O4 is the major raw material for fabricating lithium cobalt oxide electrode of lithium ion battery. According to the experimental dataset on grain diameter of Co3O4 nanoparticles synthesized by homogeneous precipitation under four main process parameters including the concentration of Co(NO3)2?H2O solution, mole ratio of reactants, reaction temperature and reaction time, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, is introduced to establish a model for estimating grain diameter of Co3O4 nanoparticles. The comparison of prediction results strongly support the prediction and generalization abilities of SVR are superior to those of multivariable gradual regression (MGR). Meanwhile, the index of grain diameter of Co3O4 nanoparticles under an independent combination of process parameters predicted by SVR model is more accurate than that by MGR model. The multi-factors analysis results based on SVR model are consistent with that of the literatures. This study suggests that SVR is a theoretical significance and potential practical value in development of smaller grain diameter of Co3O4 nanoparticles via guiding experiment.
cobaltosic oxide (Co3O4) nanoparticles electrode material grain diameter support vector machines modeling
C.Z. Cai X.J. Zhu J.F. Pei G.L. Wang
Department of Applied Physics, Chongqing University, Chongqing 400044, China
国际会议
11th IUMRS International Conference in Asia(第十一届国际材联亚洲材料大会 IUMRS-ICA 2010)
青岛
英文
211-219
2010-09-25（万方平台首次上网日期，不代表论文的发表时间）