A facile approach to fabricate shape memory polyvinyl alcohol/short carbon fibre composites with self-folding properties
Source abstract
Abstract
In this article, electroactive shape memory polyvinyl alcohol (PVA)/short carbon fibres (SCFs) composites were prepared by simple solution casting method. The self-folding properties of PVA/SCF composites were achieved by shape programming procedure and multilayer assembly technology. The design concept was a three-layer self-folding structure, which utilized three easily obtainable materials; namely, PVA/SCF composite films (shape change layer), polyimide tape (structural layer) and copper wire. The reliability of spontaneous shape recovery of self-folding samples was studied by electrical and shape memory performance tests. The results showed that with the appropriate increase of the applied voltage, the deformation time of the samples decreased and shape memory performance improved. A sample completed the entire shape recovery process within 48 s under a low triggered voltage of 35 V, and shape recovery ratio ( $$R_{\text{r}}$$ ) reached 95%. In addition, shape memory cycle tests showed that the samples had good cycle stability. After 15 consecutive times of unfolding/self-folding cycles, the $$R_{\text{r}}$$ and the shape fixity ratio ( $$R_{\text{f}}$$ ) of the samples were all greater than 93%. A 2D self-folding component was fabricated by properly assigned PVA/SCF composites in a plane, which could complete shape changing at 35 V. This showed the feasibility of self-folding samples in actuating the conversion of 2D framework to 3D structure. The study provides a unique strategy to expand the application of shape memory PVA/SCF composites in smart 3D structures with self-folding properties.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Polymer composites and self-healing · Advanced Materials and Mechanics · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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