Conductivity Prediction Method of Carbon Nanotube Resin Composites Considering the Quantum Tunnelling Effect

Abstract

Understanding and predicting the conductivity of carbon nanotube resin composites are essential for structural health detection and monitoring applications. Due to the complexity in the composition of carbon nanotube resin composites, it is of practical significance to develop a method for predicting the conductivity with a view to design and making of the composite. In this paper, the influence of carbon nanotube tunnelling on the conductivity was investigated thoroughly, where the tunnelling conductivity effect is considered as an independent conductive phase. Then, the effective medium model and the Hashin–Shtrikman (H–S) boundary model are used to predict the conductivity of carbon nanotube resin composites. The results presented in this paper show that the developed method can reduce the prediction range of the H–S boundary model and improve the prediction accuracy of the lower bound of the H–S boundary model. The results also show that the tunnelling has little effect on conductivity prediction based on the effective medium model. Based on the results, the effects of nanotube conductivity, the aspect ratio and the barrier height on the prediction of the effective conductivity are discussed to provide a guidance for the design and making of the composites.