Double-network-interlocked morphology produces 3D-printable thermoplastic dynamic vulcanizate with high durability in shape-memory performance
Source abstract
Abstract
Due to the unique stress relaxation and unrecoverable chain disentanglement as well as slip of polymers, thermoplastic shape-memory polymers (SMP) often exhibit low durability and robustness in dynamic load-bearing applications. 3D printing of thermoplastic dynamic vulcanizates (TPVs) which are normally composed of dispersed rubberic phase and continuous plastic phase are rarely reported due to their poor thermoviscous flowability. Here, we propose a strategy to fabricate 3D-printable TPVs with improved durability in shape-recovery performance based on phase-inversion blending. Evolution of the obtained mechanical properties and shape memory performance was discussed based on morphology regulation. The durable SMP blends were achieved via a facile melt-compounding process under multi-stage temperatures by involving two immiscible phases: one is the partially crosslinked eucommia ulmoides gum (EUG) skeletons and the other is the infilled/interlocked low-viscosity polycaprolactone (PCL), which construct a kind of double-network-interlocked morphology and thereby provide the desired elastic resilience while enabling the feasibility of melt-extrusion 3D printing. To address the poor interfacial compatibility, furthermore, diisocyanate (MDI) was incorporated to enhance PCL/EUG adhesion by extending molecular chains, particularly for PCL. This resulted in a mechanically robust double-network-interlocked morphology at the microscopic scale, comprising crosslinked EUG skeletons and an infilled thermoplastic PCL phase. Through morphology optimization, a 40 wt% EUG/60 wt% PCL blend demonstrated exceptional durable shape recovery (Rr ~ 91% after 10 cycles) while retaining excellent 3D printability.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Polymer composites and self-healing · Advanced Materials and Mechanics · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
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