UV Curing of Biobased Electrically Conductive Coatings with Covalent Adaptable Network Properties

The development of sustainable coatings that combine reprocessability with active functionalities remains a central challenge for the composites sector. In this work, a healable, electrically conductive coating was formulated using epoxidized castor oil (ECO) as a bio-based matrix, dibutyl phosphate (DBP) as a transesterification catalyst, and short recycled carbon fibres (RCFs, 2 mm in length) as a conductive filler at loadings of 10 and 20 phr. Formulations were UV-cured via cationic photopolymerization and characterized across the full liquid-to-solid processing chain. FT-IR and photo-DSC showed that increasing RCF content progressively reduced curing rate and conversion, an effect attributed to light scattering/absorption by the fibres and restricted chain mobility, although gel content remained above 98% in all cases. DMTA showed that RCF did significantly affect the glass transition temperature but markedly increased the rubbery storage modulus and apparent crosslink density, consistent with a physical reinforcement mechanism. Stress relaxation tests confirmed the dynamic bond exchange behaviour in all formulations, with the apparent activation energy decreasing from 112 kJ/mol for the neat resin to 33–34 kJ/mol upon RCF incorporation. This significant reduction suggests that the presence of RCF facilitates the bond-exchange process, potentially through interfacial interactions between the polymer network and the fibre surface. However, the specific molecular mechanism responsible for this effect cannot be established from the present data. Electrical conductivity peaked at 10 phr RCF (3.6 × 10−3 S/m), enabling measurable Joule heating, while the 20 phr formulation showed reduced conductivity linked to voids and lower conversion. Thermally triggered healing at 120 °C for 6 h restored mechanical integrity, which is higher than reference values, demonstrating the coating’s capacity for repeated repair through its dynamic covalent network