journal article·2025-06Open access

High-performance 4D printing of shape memory polymers with interpenetrating networks from hybrid biobased benzoxazine with acrylic resin blends

Nuttinan Boonnao, Panuwat Luengrojanakul, Reza Gholami, Cheol-Hee Ahn, Panagiotis Karagiannidis, Sarawut Rimdusit

Abstract

Developing 4D printing using shape memory polymers (SMPs) with high mechanical and thermal stability is crucial for specific applications, such as deployable structures. In this work, we utilize sustainable chemicals to produce high-performance 4D printing of SMPs by blending biobased eugenol-furfurylamine benzoxazine (E-fa) (0–20 wt%) with a photoreactive acrylic (AC) resin via dual-curing of UV (LCD 3D printer) and thermal methods. Interpenetrating polymer networks (IPNs) were efficiently created in the as-prepared hybrids with enhancements in material properties. DMA analysis revealed the proportional increase in stiffness and crosslinked density with the increase of E-fa resin content. Blending E-fa with AC gradually improved the 4D material properties at an optimum of 15 wt% E-fa, with a 45 % enhancement in flexural strength (101.20 to 146.84 MPa), a 36 % rise in tensile strength (60.33 to 81.80 MPa), and a 16 °C increase in 5 % weight loss temperature, in comparison to pure AC resin. The optimum blending of E-fa with AC also exhibited excellent shape memory properties, with a 99.6 % shape fixity ratio, a 97.8 % shape recovery ratio, and stability for up to 15 shape memory cycles. More importantly, 4D spring shapes were produced to illustrate the practical application and efficiency of AC and AC/E-fa blend 4D materials. Notably, the 4D spring shape from the AC/E-fa blend has a higher recovery force than the 4D spring shape from pure AC. Therefore, the AC/E-fa blend 4D materials show great potential for meeting demanding engineering requirements.
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Research context

OpenAlex topics: Photopolymerization techniques and applications · Polymer composites and self-healing · Advanced Polymer Synthesis and Characterization

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

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