journal article·2025-09Open access

Multifunctional natural fibre composites with integrated process monitoring, damage sensing and energy-efficient heating capabilities via upcycled carbon fibre waste

Yansong Li, Thomas D.S. Thorn, Yushen Wang, Aaron Soul, Yi Liu, Dimitrios Papageorgiou, Ton Peijs, Han Zhang

Abstract

Natural fibre composites are increasingly recognised as sustainable alternatives to synthetic counterparts in a variety of structural and semi-structural applications, driven by their eco-friendly characteristics and potential to reduce environmental impacts. However, the absence of affordable and practical inspection techniques for monitoring and assessing the lifespan of natural fibre composites is a key barrier to their broader adoption. This paper presents a new approach to use recycled carbon fibre (rCF) veil as a multifunctional layer for manufacturing monitoring, out-of-oven (OoO) curing and damage detection in natural fibre composites. Manufacturing monitoring and damage detection functions are achieved through electrical impedance tomography (EIT) technology with the advantages of real-time imaging and low-cost devices. The rCF veil enables real-time detection of the resin flow front during the infusion stage and subsequently facilitates locating the damage of the composites during the mechanical tests. In addition, it functions as a Joule-heating medium for energy-efficient OoO curing, which reduces energy consumption by more than 95 % compared to conventional oven curing without compromising mechanical properties. The introduction of rCF veil not only provides insights into natural fibre composites from the manufacturing to the in-service stage, but also demonstrates upcycling routes for rCF as a multifunctional layer in future sustainable composites.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Smart Materials for Construction · Electrical and Bioimpedance Tomography · Natural Fiber Reinforced Composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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