Electromagnetic shielding property and electrical conductivity of multi‐walled carbon nanotubes filled poly(ε‐caprolactone) composites with dense conductive networks: A simulation and experimental study

Abstract Electromagnetic shielding properties of conductive polymer composites (CPCs) are dependent on the content of conductive fillers. In this study, electromagnetic shielding effectiveness (EMW SE) of poly(ε‐caprolactone) (PCL) filled with different content of multi‐walled carbon nanotubes (MWCNTs) were prepared to systematically discuss content‐dependent electromagnetic shielding mechanism via a simulation and experimental study. At low MWCNT loadings, the composites have high absorption coefficients because the MWCNT particles are separated by PCL chains. The shielding performance mainly comes from the polarization loss and conduction loss. However, the composites with high content of MWCNT can form conductive networks which leads to high reflection values, suggesting a reflection‐dominant shielding mechanism. In addition, a conductive mesh model is first introduced to simulate the effect of filler loadings on electromagnetic shielding performance. For electrical conductivity, there is no obvious platform being found in the samples at a high content of MWCNTs. The formation of a denser conductive network within the composites reduces inter‐filler gaps and promotes closer contact. However, according to the experimental and simulation results, upon exceeding the percolation threshold of MWCNT loadings, the EM SE value slightly increases leading to a plateau region. The results are ascribed to the formation of stable shielding networks at high content of MWCNTs. Highlights The sample has a high absorption coefficient at a low content of MWCNTs. All the samples exhibit a reflection‐dominant shielding mechanism. No obvious platform of conductivity is found at high content of MWCNTs. Obvious platform of EMW SE values is found in the high content of MWCNTs. High EMW SE values for CPC only require relatively dense conductive networks.