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Synthesis and characterization of waste polyethylene reinforced modified castor oil-based polyester biocomposite

机译:废旧聚乙烯增强改性蓖麻油基聚酯生物复合材料的合成与表征

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摘要

In this research, modified castor oil (MCO)-based biocomposite has been synthesized and its structure is strengthened with waste polyethylene (PE) reinforcement. Both the petrochemical raw material used is reduced by 12 wt and a new environmentally friendly biocomposite is produced using waste PE. Considering some thermophysical properties of the obtained biocomposite, experimental working conditions, and composition ratios have been optimized with response surface methodology (RSM). The chemical bond structure of the biocomposite has been investigated by Fourier transform infrared spectrophotometer, thermal decomposition behavior by thermogravimetric analysis, and surface morphology by scanning electron microscopy. According to the results obtained, the density and hardness of the biocomposite synthesized by the addition of MCO to unsaturated polyester (UP) decreases, and its thermal conductivity and thermal stability increase. The thermal decomposition kinetics of the biocomposite is also modeled with the newly improved hyperbolic function equation. The relationship between conversion rate and the temperature has been determined by the new model with a high correlation coefficient (R2 = 0.9985) and low-error functions (SST = 0.0096, RMSE = 0.0285, chi 2 = 0.0037). Effective and efficient use of MCO, UP, methyl ethyl ketone peroxide, and cobalt octoate in the production process has provided an economical and steady the biocomposite. Evaluation of experimental data with both RSM and artificial neural networks raises the reliability of the model results.
机译:本研究合成了改性蓖麻油(MCO)基生物复合材料,并用废聚乙烯(PE)增强材料加固了其结构。所使用的石化原料减少了 12 wt%,并使用废 PE 生产了一种新的环保生物复合材料。考虑到所制备生物复合材料的一些热物理性质,采用响应面法(RSM)对实验工况和组成比例进行了优化。采用傅里叶变换红外分光光度计研究了生物复合材料的化学键结构,利用热重分析研究了生物复合材料的热分解行为,利用扫描电子显微镜研究了生物复合材料的表面形貌。结果表明,在不饱和聚酯(UP)中加入MCO合成的生物复合材料的密度和硬度降低,其导热性和热稳定性增加。生物复合材料的热分解动力学也使用新改进的双曲函数方程进行建模。转换率与温度之间的关系由具有高相关系数(R2 = 0.9985)和低误差函数(SST = 0.0096,RMSE = 0.0285,chi 2 = 0.0037)的新模型确定。MCO、UP、过氧化甲乙酮和辛酸钴在生产过程中的有效和高效使用提供了一种经济、稳定的生物复合材料。使用RSM和人工神经网络对实验数据进行评估,提高了模型结果的可靠性。

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