Preparation and performance optimization in high‐voltage cable semiconductive shielding layer for polyolefin composite based on multifractal analysis

材料科学 复合材料 复合数 聚烯烃 电磁屏蔽 微观结构 炭黑 碳纳米管 极限抗拉强度 图层(电子) 天然橡胶
作者
Yonghai Zhang,Leigang Zhang,Yong Liu,Tan Wu,Yuhui Chen,Bao-Feng Bai,Qi Luo
出处
期刊:Polymer Composites [Wiley]
卷期号:46 (2): 1206-1219 被引量:3
标识
DOI:10.1002/pc.28019
摘要

Abstract In this study, the semiconductive shielding composite was prepared successfully using the melt mixing method. The effects of combining multiwall carbon nanotubes (MWCNT) and carbon black (CB) into polyolefin plasticizers/ethylene vinyl acetate/linear low density polyethylene matrix on the mechanical performances, electrical conductivity, and processing rheological properties of the samples were investigated. The research results indicate that adding MWCNT to the prepared semiconductive shielding composite can form a complete conductive network, and its synergistic effect with CB jointly improves the conductivity of the composite material. Compared with the absence of MWCNT, its bulk resistivity decreased from 1.61 W • cm to 0.89 W • cm, a decrease of 44.7%. At the same time, the prepared semiconductive shielding composite has excellent mechanical and processing performances. The elongation at break, maximum tensile strength, and elastic modulus of the composites were increased by 30.9%, 17.9%, and 4.9%, respectively. Based on multifractal analysis, by adjusting the content of MWCNT in composite and optimized processing paths, the distribution and microstructure of MWCNT in composite can be optimized to obtain adjustable mechanical and processing rheological properties. This study presents a guiding approach for designing and developing functional composites requiring adjustable mechanical performances. Highlights The semiconductive shielding composite was prepared successfully. The prepared composite has excellent mechanical and processing performances. Multiwall carbon nanotubes with carbon black can form conductive network. The microstructure can be optimized by quantitative characterization of the morphology.
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