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Cited by 267

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Magnetic programming of 4D printed shape memory composite structures

Zhang, Fenghua, Wang, Linlin, Zheng, Zhichao, Liu, Yanju, Leng, Jinsong·2019·Composites Part A: Applied Science and Manufacturing

Abstract

Abstract 4D printed shape memory polymers and their composites are currently a highly topical research area. The potential applications for 4D printed smart materials are wide-reaching, with particular promise for personalized medicine. In this work, we 4D printed various structures made of biocompatible and biodegradable polylactic acid (PLA) and PLA/Fe3O4 composite filaments. The shape memory behaviors of the 4D printed structures triggered by magnetic field were investigated. The printed structures can return to their original shapes with a high speed in just a few seconds. Moreover, the structures like bone tissues printed by PLA/Fe3O4 composites filaments with 15% Fe3O4 were actuated by magnetic field at 27.5 kHz. During the shape recovery process, surface temperature of the printed structures is uniform and around 40 °C. This physiologically relevant operating temperature range is a highly attractive feature for potential healthcare and biomedical applications.

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Metadata

Authors
Zhang, F., Wang, L., Zheng, Z., Liu, Y., & Leng, J.
Venue
Composites Part A: Applied Science and Manufacturing
Publisher
Elsevier BV
Published
2019-10
Volume
125
Pages
105571
ISSN
1359-835X (ISSN-L)
OpenAlex
W2965738952
Cited by
267
References
37
FWCI
15.91
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Magnetic programming of 4D printed shape memory composite structures. https://app.cochat.ai/papers/10.1016/j.compositesa.2019.105571
The paper
Zhang, F., Wang, L., Zheng, Z., Liu, Y., & Leng, J. (2019). Magnetic programming of 4D printed shape memory composite structures. Composites Part A: Applied Science and Manufacturing. https://doi.org/10.1016/j.compositesa.2019.105571

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.