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首页> 外文期刊>Thermochimica Acta: An International Journal Concerned with the Broader Aspects of Thermochemistry and Its Applications to Chemical Problems >Effect of graphite and Ag-plated graphite nanoplatelets on the thermal properties of polypropylene nanocomposites: Experimental studies and modeling
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Effect of graphite and Ag-plated graphite nanoplatelets on the thermal properties of polypropylene nanocomposites: Experimental studies and modeling

机译:石墨和镀银的石墨纳米片对聚丙烯纳米复合材料热性能的影响:实验研究和建模

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摘要

The effects of using graphite nanoplatelets (GNPs) and Ag-plated GNPs (Ag-GNPs), as fillers on the thermal properties of nanocomposites based on polypropylene (PP) matrix are investigated in this study. GNPs were exfoliated from expanded graphite after oxide-intercalation and ultrasonic methods. The Ag-GNP was fabricated by electroless plating. The morphologies of the fillers were characterized, and the thermal properties of the obtained nanocomposites were evaluated. Results indicated that the incorporation of the two fillers improved the thermal stability, and the 5% weight loss temperature had increased to 67.82 degrees C. Thermomechanical analysis showed that the softening temperature increased by 5-8 degrees C relative to that of PP. The thermal conductivity of the nanocomposites that contained 20 wt% GNP (11 vol%) significantly improved from 0.18 W/(m K) to 1.05 W/(m K). Comparison of the experimental data of the nanocomposites with four theoretical models showed that the data of GNP/PP nanocomposites and Ag-GNP/PP nanocomposites matched well with the Nielsen-Lewis model and the Russell model, respectively, except for certain deviations. (C) 2016 Elsevier B.V. All rights reserved.
机译:本研究研究了使用石墨纳米片(GNPs)和镀银的GNPs(Ag-GNPs)作为填料对基于聚丙烯(PP)基质的纳米复合材料热学性能的影响。在氧化物嵌入和超声方法之后,从膨胀石墨中剥落GNP。 Ag-GNP是通过化学镀制造的。表征了填料的形态,并评估了所得纳米复合材料的热性能。结果表明,两种填料的掺混提高了热稳定性,并且5%失重温度提高到67.82摄氏度。热力学分析表明,软化温度相对于PP升高了5-8摄氏度。包含20 wt%GNP(11 vol%)的纳米复合材料的热导率从0.18 W /(m K)显着提高到1.05 W /(m K)。将该纳米复合材料的实验数据与四个理论模型进行比较,结果表明,GNP / PP纳米复合材料和Ag-GNP / PP纳米复合材料的数据与Nielsen-Lewis模型和Russell模型分别具有良好的匹配性。 (C)2016 Elsevier B.V.保留所有权利。

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