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Graphene nanoplatelet, multiwall carbon nanotube, and hybrid multiwall carbon nanotube–graphene nanoplatelet epoxy nanocomposites as strain sensing coatings
Journal of Reinforced Plastics and Composites ( IF 3.1 ) Pub Date : 2021-03-12 , DOI: 10.1177/0731684421994324
Mario Bragaglia 1 , Lorenzo Paleari 1 , Francesca R Lamastra 1 , Debora Puglia 2 , Francesco Fabbrocino 3 , Francesca Nanni 1
Affiliation  

Strain monitoring is of great interest in order to check components structural life, to prevent catastrophic failures, and, possibly, to predict residual life in case of unexpected events. In this study, strain sensing epoxy-based coatings containing carbon nanotubes (MWCNTs), graphene nanoplatelets (GNPs), and a mix of the two (MWCNT+GNP) have been produced, with the same initial electrical resistivity, and applied on glass fiber reinforced composites. Morphological, mechanical, and electrical tests have been then performed evaluating the resistance variation and the strain sensing performance of the sensors. A theoretical model to relate the resulting gauge factors to the different types of nanofillers has been applied. The results showed that all systems present a strain sensing performance with different gauge factors (and hence sensitivity) at low strain: GNP samples showed the highest gauge factor (10.3), MWCNT samples the lowest (1.5), and the mixed system lies in the middle (4.3). From analytical analysis, the value of initial distance among conductive particles was found to be 0.3 nm in the case of MWCNT and 1.2 nm for GNP, explaining why the gauge factors of the produced sensors are different.



中文翻译:

石墨烯纳米片,多壁碳纳米管和杂化多壁碳纳米管-石墨烯纳米片环氧纳米复合材料作为应变传感涂层

为了检查组件的结构寿命,防止灾难性故障,并可能在发生意外事件时预测剩余寿命,应变监控非常重要。在这项研究中,以相同的初始电阻率生产了含有碳纳米管(MWCNT),石墨烯纳米片(GNP)以及两者的混合物(MWCNT + GNP)的应变感测环氧基涂料,并将其应用于玻璃纤维增强复合材料。然后进行了形态,机械和电气测试,以评估传感器的电阻变化和应变感测性能。已应用理论模型将所得的规格因子与不同类型的纳米填料相关联。结果表明,所有系统在低应变下均具有不同的应变系数(因此灵敏度)不同的应变传感性能:GNP样品的应变系数最高(10.3),MWCNT样品的应变系数最低(1.5),并且混合系统位于中(4.3)。通过分析分析,发现导电颗粒之间的初始距离值在MWCNT情况下为0.3 nm,对于GNP为1.2 nm,这解释了为什么所生产的传感器的规格因子不同。

更新日期:2021-03-12
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