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LLDPE/纳米ZnO复合材料的制备与工艺优化研究

Study on preparation and process optimization of LLDPE/nano-ZnO composite material
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摘要 讨论了利用硅烷偶联剂对纳米ZnO粒子表面改性的机理与方法,按照标准正交表L9(34)的实验方案,采用熔融共混方法制备LLDPE/纳米ZnO复合材料,并测试其拉伸强度和断裂伸长率。通过正交实验分析法得到较优的工艺参数组合,同时基于正交实验数据通过神经网络和遗传算法优化得到一组工艺参数组合,并分别在两组工艺参数下进行实验分析。结果表明,正交实验、神经网络和遗传算法三者结合优化复合材料制备工艺参数的方法更理想;在较优的工艺条件下,LLDPE基体中适当填充改性纳米ZnO粒子,拉伸强度和断裂伸长率同时提高,能够起到增强增韧的作用。 The mechanism and method were deduced on surface modification of nano-ZnO.Adopting orthogonal experiment with table of L9(3^4),LLDPE/nano-ZnO composite materials were prepared by melt blending.The tensile strength and fracture elongation rate of nano-composite material were tested.A relatively optimal group of process parameters were obtained by orthogonal experiment and another group of process parameters were also obtained by neural network of BP and genetic algorithms.With analysis,it was showed that the group of process parameters obtained by BP neural network and genetic algorithms based on the orthogonal experiment data were much better than that group obtained by orthogonal experiment.When modified nano-ZnO particles were well-distributed in LLDPE matrix,the tensile strength and fracture elongation rate of nano-composite material were improved with better process conditions,which result in the effect of reinforcing and toughening.
作者 刘佳 贾树盛
出处 《功能材料》 EI CAS CSCD 北大核心 2008年第2期349-352,共4页 Journal of Functional Materials
基金 国家重点基础研究发展计划(973计划)资助项目(2004CB619301)
关键词 纳米复合材料 正交实验 BP神经网络 遗传算法 工艺优化 nano-composite material orthogonal experiment BP neutral network genetic algorithms process optimization
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参考文献7

  • 1Wang Zhongling.Characterization of Nanophase Materials[M].New York:Wiley-VCH,2001.
  • 2Kato M,Okamoto H,Hasegawa N,et al.[J].Polymer Engineering and Science,2003,43:1312-1316.
  • 3Ruckenstein E,Yuan Y.[J].Polymer,1997,38(15):3855-3860.
  • 4Garcia M,vanzyl W E,ten Cate M G J,et al.[J].Industrial & Engineering Chemistry Research,2003,42:3750-3757.
  • 5Huang Y Q,Jiang S L,Wu L B,et al.[J].Polymer Testing,2004,23:9-15.
  • 6Hua Y Q,Zhang Y Q,Wu L B,et al.[J].Journal of Macromolecular Science Physics,2005,44(2):149-159.
  • 7Funahashi K I.[J].Neural Networks,1989,2(3):183-192.

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