Qiu Xiaowen, Lin Zewen, Zhao Yanan, Zhang Jinmeng, Hu Xiaolan, Bai Hua
College of Materials, Xiamen University, Xiamen, 361005, P. R. China.
Small. 2023 Aug;19(33):e2300931. doi: 10.1002/smll.202300931. Epub 2023 Apr 24.
Conductive composites based on thermosetting resins have broad applications in various fields. In this paper, a new self-compositing strategy is developed for improving the conductivity of graphene nanoplatelet/thermosetting resin composites by optimizing the transport channels. To implement this strategy, conventional graphene nanoplatelet/thermosetting resin is crushed into micron-sized composite powders, which are mixed with graphene nanoplatelets to form novel complex fillers to prepare the self-composited materials with thermosetting resins. A highly conductive compact graphene layer is formed on the surface of the crushed composite powders, while the addition of the micron-sized powder induces the orientation of graphene nanoplatelets in the resin matrix. Therefore, a highly conductive network is constructed inside the self-composited material, significantly enhancing the electrical conductivity. The composite materials based on epoxy resin, cyanate resin, and unsaturated polyester are prepared with this method, reflecting that the method is universal for preparing composites based on thermosetting resins. The highest electrical conductivity of the self-composited material based on unsaturated polyester is as high as 25.9 S m . This self-compositing strategy is simple, efficient, and compatible with large-scale industrial production, thus it is a promising and general way to enhance the conductivity of thermosetting resin matrix composites.
基于热固性树脂的导电复合材料在各个领域有着广泛的应用。本文提出了一种新的自复合策略，通过优化传输通道来提高石墨烯纳米片/热固性树脂复合材料的导电性。为实施该策略，将传统的石墨烯纳米片/热固性树脂粉碎成微米级复合粉末，将其与石墨烯纳米片混合形成新型复合填料，再与热固性树脂制备自复合材料。在粉碎后的复合粉末表面形成了高导电致密石墨烯层，而微米级粉末的加入促使石墨烯纳米片在树脂基体中取向。因此，在自复合材料内部构建了高导电网络，显著提高了电导率。用该方法制备了基于环氧树脂、氰酸酯树脂和不饱和聚酯的复合材料，表明该方法对于制备基于热固性树脂的复合材料具有通用性。基于不饱和聚酯的自复合材料的最高电导率高达25.9 S/m。这种自复合策略简单、高效且与大规模工业生产兼容，因此是提高热固性树脂基复合材料导电性的一种有前景的通用方法。
