Preparation and characterization of thermo-responsive shape memory ester-based polymer blends
Source abstract
Abstract
In this project, thermo-responsive shape memory polymer blends containing copolyester thermoplastic elastomer (COPE) and semi-crystalline polycaprolactone (PCL) were prepared by solution blending method. Shape memory behavior, tensile and dynamic mechanical properties as well as creep performances of the blends were discussed by paying attention to effect of blend composition and morphology. The blends containing 10, 20, and 30 wt% PCL exhibited droplet matrix morphologies, while those having 40% (COPE60) and 50 wt% PCL (COPE50) showed co-continuous morphologies. In the blend composition, the elastic modulus values tended to increase with the increase of the PCL switch phase, and the highest value was obtained in the COPE50 blend. On the other hand, blends containing high amount of rigid PCL phase showed lower ductility and accordingly the elongation at break and toughness values decreased. Creep deformation and creep recovery values increased with the increase in the amount of elastomer component COPE in the blend. In terms of shape memory, optimum shape fixing and shape recovery ratios were obtained in the COPE60 blend which has co-continuous morphology. The same polymer blend has also been found to have repeatability in shape memory property at least in three cycles. On the other hand, the COPE60 blend exhibited definite shape recovery ratios at different temperatures and it was determined that its shape recovery was adjustable with temperature changes. Moreover, self-healing properties of COPE60 blend were examined and it was determined that the blend exhibited higher tensile strength and toughness values after repair compared to its damaged condition.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Polymer composites and self-healing · Electrospun Nanofibers in Biomedical Applications · Synthetic Organic Chemistry Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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