Photo-induced shape memory blend composites with remote selective self-healing performance enabled by polypyrrole nanoparticles
Source abstract
Abstract
Abstract Shape memory polymers (SMPs) are a type of smart materials that can recover to the original shape from a set temporary shape under external stimulus, but SMPs are prone to be damaged during repeated deformation and recovery. Therefore, it is of great significance to improve the self-healing ability of SMPs. In this work, ethylene-vinyl acetate copolymer (EVA)/polycaprolactone (PCL)/polypyrrole (PPy) blend composites were prepared via simple melt blending. In this system, PPy nanoparticles, as photothermal fillers, selectively distributed in the PCL component, enable the EVA/PCL blend with a co-continuous structure to exhibit excellent photo-induced shape memory performance with a shape recovery ratio of 100%. Thanks to the excellent photothermal effect of PPy nanoparticles and the melting and recrystallization behaviors of linear EVA and PCL chains, the blend composites also exhibit outstanding photo-induced self-healing performance for different forms of damage, and the considerable healing efficiencies for the bent, scratched and cut samples reach 85.68%, 94.63% and 86.49%, respectively. Compared with the traditional thermal-induced self-healing shape memory composites, near infrared (NIR) irradiation can not only remotely manipulate the shape memory behavior of these composites, but also can accurately and selectively heal the damaged areas without distinct impact on the performance of intact areas. The above dual functions, along with the comprehensive performances and simple preparation process of these materials, enable the EVA/PCL/PPy blend composites to be the ideal candidates for many potential applications, including soft robots, smart packaging, biomedical devices, etc.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Polymer composites and self-healing · Conducting polymers and applications · Photochromic and Fluorescence Chemistry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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