利用BP神经网络算法优化纳米Fe3O4的合成工艺

Optimization of the preparation condition of Fe3O4 nanoparticle by BP nerve network

  • 摘要: 采用液相共沉淀法制备出了纳米级Fe3O4颗粒,并利用正交设计法对实验进行设计,据此找出最佳实验条件为Fe3O4的晶化温度为90℃,Fe3+/Fe2+的比例为1:2和制备Fe3O4的晶化时间为45 min。此时制得的纳米粉体平均粒径可达10~20nm,并利用BP神经网络分析了Fe3+/Fe2+的比例、晶化温度和晶化时间等对粒子粒径的影响规律。

     

    Abstract: The preparation of Fe3O4 nanoparticle by liquid-phase coprecipitation was introduced. And the orthogonal experiment was designed. The optimized condition that the proportion of Fe3+/Fe2+ was 1:2, crystallization temperature of Fe3O4 nanoparticle was 90℃ and crystallization time of Fe3O4 nanoparticle was 45min was achieved. Under the condition, the average size of nanoparticle is 10nm to 20nm. And BP nerve network is used to analyze the influence factors of the size of Fe3O4 nanoparticle such as proportion of Fe3+/Fe2+, crystallization temperature of Fe3O4 nanoparticle and crystallization time of Fe3O4 nanoparticle.

     

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