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Efficiently Controlling the 3D Thermal Conductivity of a Polymer Nanocomposite via a Hyperelastic Double-Continuous Network of Graphene and Sponge

机译:通过石墨烯和海绵的超弹性双连续网络有效控制聚合物纳米复合材料的3D热导率

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摘要

Graphene-reinforced polymer composites with high thermal conductivity show attractive prospects as thermal transfer materials in many applications such as intelligent robotic skin. However, for the most reported composites, precise control of the thermal conductivity is not easily achieved, and the improvement efficiency is usually low. To effectively control the 3D thermal conductivity of graphene-reinforced polymer nanocomposites, a hyperelastic double-continuous network of graphene and sponge is developed. The structure (orientation, density) and thermal conductivity (in-plane, cross-plane) of the resulting composites can be effectively controlled by adjusting the preparation and deformation parameters (unidirectional, multidirectional) of the network. Based on the experimental and theoretical simulation results, the thermal conduction mechanism is summarized as a two-stage transmission of phonons. The in-plane thermal conductivity increases from 0.175 to 1.68 W m(-1) K-1 when the directional compression ratio increases from 0% to 95%, and the corresponding enhancement efficiency exceeds 300. The 3D thermal conductivity reaches a maximum of 2.19 W m(-1) K-1 when the compression ratio is 70% in three directions, and the graphene content is 4.82 wt%. Moreover, the thermal conduction network can be largely prepared by power-driven roller equipment, making the composite an ideal candidate for sensitive robotic skin for temperature detection.
机译:具有高导热率的石墨烯增强的聚合物复合材料在许多应用(例如智能机器人皮肤)中作为热传递材料显示出诱人的前景。然而,对于大多数报道的复合材料,不容易实现对导热率的精确控制,并且改善效率通常较低。为了有效地控制石墨烯增强的聚合物纳米复合材料的3D热导率,开发了石墨烯和海绵的超弹性双连续网络。通过调节网络的制备和变形参数(单向,多向),可以有效地控制所得复合材料的结构(取向,密度)和热导率(面内,横截面)。根据实验和理论模拟结果,将热传导机理概括为声子的两阶段传输。当方向压缩率从0%增加到95%时,面内导热率从0.175 W m(-1)K-1增加,并且相应的增强效率超过300。3D导热率最大达到2.19当在三个方向上的压缩比为70%且石墨烯含量为4.82重量%时,W m(-1)K-1为W-1。而且,热传导网络可以通过动力驱动的滚轮设备进行大量准备,从而使复合材料成为敏感的机器人皮肤进行温度检测的理想选择。

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