Prediction of thermal stability, crystallinity and thermomechanical properties of poly(ethylene oxide)/clay nanocomposites with artificial neural networks
Prediction of thermal stability, crystallinity and thermomechanical properties of poly(ethylene oxide)/clay nanocomposites with artificial neural networks
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DOI：
10.1016/j.tca.2013.10.032
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发表时间：
2014-01-10
影响因子：
3.5
通讯作者：
Ozcan, Hakan
中科院分区：
文献类型：
--
作者：
Burgaz, Engin;Yazici, Mehmet;Ozcan, Hakan
采用人工神经网络技术和前馈传播算法研究了粘土组成和温度对聚氧乙烷/粘土纳米复合材料的热稳定性、结晶度和热机械性能的影响。在动态力学分析(DMA)、差示扫描量热法(DSC)和热重分析(TGA)实验的基础上，用训练好的人工神经网络成功地计算了分解温度、残炭率、熔化热、储能系数(E‘)和tan增量。模拟数据与实验数据吻合较好。ANN结果表明，PEO纳米复合材料的热稳定性随着熔融热焓和相对结晶度的降低而增加，且弹性系数(刚度)与热稳定性成正比。人工神经网络技术在聚合物/粘土纳米复合材料的热分析中是一种有用的数学工具。(C)2013爱思唯尔B.V.保留所有权利。
The artificial neural network (ANN) technique with a feed-forward back propagation algorithm was used to examine the effect of clay composition and temperature on thermal stability, crystallinity and thermomechanical properties of poly(ethylene oxide)/clay nanocomposites. Based on dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) experiments, values of decomposition temperature, char yield, enthalpy of melting, storage modulus (E') and tan delta were successfully calculated by well-trained ANNs. The simulated data is in very good agreement with the experimental data. ANN results confirm that thermal stability of PEO nanocomposites increases with the decrease of enthalpy of melting and relative crystallinity, and there is a directly proportional relationship between the modulus (stiffness) and thermal stability. The ANN technique is confirmed to be a useful mathematical tool in the thermal analysis of polymer/clay nanocomposites. (C) 2013 Elsevier B.V. All rights reserved.