journal article·2019-08

A “trampoline” nanocomposite: Tuning the interlayer spacing in graphene oxide/polyurethane to achieve coalesced mechanical and memory properties

Yuanchi Zhang, Jinlian Hu, Shanshan Zhu, Tingwu Qin, Fenglong Ji

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Abstract

Abstract Overall high-performance shape memory polymer composites (SMCs) are desired in diverse fields while a stubborn conflict between mechanical and memory properties impedes their development in applications such as bone repair for high load-bearing locations' defects and minimally invasive surgery. For achieving a SMC with coalesced high performances in both shape memory and mechanical properties simultaneously, we here report a novel “trampoline” polyurethane nanocomposite based on the modification sequence of graphene oxide in the composite fabrication process, which can tune interlayer spacing. In this work, an intercalating structure with spring-like support, trampoline model, is proposed and was carefully examined. It is found that with larger interlayer spacing, namely taller spring support, with a longer molecular chain between two-dimensional sheets, the “trampoline” structure can remarkably improve memory and mechanical properties jointly. As desired, both high mechanical and good memory properties, e.g. ∼456.72 MPa of Young's modulus and ∼100% of shape recovery ratio, were attained respectively for the SMC. A minimally invasive bone-repairing example then demonstrated an ideal support performance for bone regeneration through finite element analysis, which showed the highest stress distributed around the implant particularly at the interface, which is essential for effective bone tissue regeneration. This “trampoline” nanocomposite paves a bright way to design and prepare SMCs with advanced performances for broad applications.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Polymer composites and self-healing · Bone Tissue Engineering Materials · Supramolecular Self-Assembly in Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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