Electrical characterization and sensing capabilities of self-assembly multi-scale multi-phase graphene-based composites

The addition of graphene particles to a thermoset resin strongly enhances its electrical properties, opening the use of such system for damage and strain tracking. This increase is, however, proportional to the amount and purity of the graphene particles, usually at high costs. Using a high amount of filler leads to a decrease in processability as well as a possible drop in mechanical properties. In order to reduce the quantity needed for conduction with few-layer graphene (FLG), a self-assembly network of graphene particles in the polymeric structure was created using blends of unsaturated polyester and polycaprolactone, using FLG and fibreglass reinforcement. The DC and AC electrical conductivities were strongly enhanced by the coupled effect of phase separation and fibre preform, effectively reducing the percolation threshold. An impressive shift was measured from 8.2 wt% of graphene for the neat resin to 1.4 wt% of graphene for the optimized composition. These materials also showed strong strain sensing capabilities at low applied voltage. Therefore, these systems are reliable and cost effective candidates for self-sensing and damage tracking application.

Automated summary

The addition of graphene particles significantly improves the electrical properties of thermoset resin, making it suitable for damage and strain tracking. However, the increase in electrical properties depends on the amount and purity of graphene particles, which are usually expensive. To reduce the quantity needed for conduction, a self-assembly network of graphene particles was created using unsaturated polyester and polycaprolactone blends, resulting in a significant enhancement of DC and AC electrical conductivities. The optimized composition required only 1.4 wt% of graphene compared to the initial 8.2 wt%. These materials also showed excellent strain sensing capabilities at low applied voltage, making them reliable and cost-effective for self-sensing and damage tracking applications.