“Rigid-stretchable” unity of shape memory composites with fluorescence via crystallinity tailoring for anti-counterfeiting application
Source abstract
Abstract
Abstract Strength and stiffness improvement without flexibility loss in shape-memory polymers is particularly significant in creating desirable functionality and broadening practical applications. Based on crystallinity tailoring strategy, a “rigidity-stretchability”-united approach is presented to construct shape-memory polyurethane composites (SMPUs) with specialized assembly for the integrated mechanical robustness and reliable deformability. Cellulose with different geometries were prepared as tailoring agent of crystallinity and structural modifier to assemble dual-networked structures of hybridized ionic- and hydrogen-bonding for the first time. Surface modification and morphology control of these celluloses were developed to manipulate the crystallization behaviors of soft segments in PU. Specifically, functionalized nanocrystal cellulose (f-NCC) with high crystallinity and needle-like geometry promoted a strong heterogeneous nucleation on the PU crystallization (the highest crystallinity, 31.3%) and contributed to 58% and 47% increase in tensile strength (48.9 MPa) and Young's modulus (237.2 MPa) without deteriorating the existent outstanding flexibility (emax = 756.14%), thereby featuring favorable shape memory and fluorescence. Furthermore, the f-NCC based films showed the highest shape memory properties (Rr = 99.7%, Rf = 99.4%) than functionalized cellulose from cotton pulps and functionalized microcrystal cellulose based ones. The “rigid-stretchable” unity of SMPUs would lay a significant foundation of applications in the fields of larger strain sensors and show great potential in information hiding and storage fields.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Polymer composites and self-healing · Advanced Sensor and Energy Harvesting Materials · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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