Reinforcement of shape-memory poly(ethylene-co-vinyl acetate) by carbon fibre to access robust recovery capability under resistant condition
Source abstract
Abstract
Abstract Poly (ethylene-co-vinyl acetate) (EVA), as one of commercial polymers, has been used early to fabricate shape-memory polymers (SMPs). However, its low recovery stress restricts the potential applications in some tough conditions due to its high flexible feature. Herein, we developed a series of EVA-based composites (cEVA/CF) with remarkably enhanced recovery stress both in free-state and resistant condition by incorporating different amounts of a stiff filler carbon fibre (CF) into the commercial EVA matrix. The observation of morphology showed that CF was well dispersed in EVA matrix, and the thermal properties of cEVA/CF composites were just slightly affected as demonstrated by the DSC analysis. More importantly, the modulus of composites was significantly improved, especially above the melting temperature (Tm). This feature is valuable to improve the recovery stress of cEVA/CF composites as both the shape deformation and recovery occur at T > Tm. To investigate the recovery behaviors of the cEVA/CF composites in free-state or with varying external loads, the strategy that combing appropriate clamp operation and well-designed procedures under two DMA modes (DMA Controlled Force and DMA Iso-Strain) was put forward. Compared with pristine cEVA, cEVA/CF composites with 5–30 wt% CF possessed 100–650% increase of recovery stress. Consequently, cEVA/CF composites exhibited a robust shape recovery performance under resistant condition, while the recovery capability of pristine cEVA was totally depressed. Aiming at practical applications, a model of deployable device made of cEVA/CF30 was well-established.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Polymer composites and self-healing · Silicone and Siloxane Chemistry · Marine Sponges and Natural Products
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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