4D Printing of Magnetic Shape Memory Polymer—La<sub>0.7</sub>Sr<sub>0.3</sub>MnO<sub>3</sub> Nanocomposite

Four‐dimensional printing has revolutionized traditional three‐dimensional printing (3D printing) by using smart materials such as shape memory polymers (SMPs). As nonmagnetic materials, SMPs require direct exposure to heat for the shape memory effect to be triggered. This limitation can be addressed by incorporating magnetic nanoparticles, allowing the SMP/nanocomposite to be remotely heated through induction heating. This work focuses on the (LSMO) manganite with an optimized stoichiometry of x = 0.3. Polycrystalline LSMO is sintered from pure powders and later added to SMP through the solution mixing route, and finally extruded into filaments to be used for 3D printing. Significant improvements in the elastic modulus, yield, and ultimate strengths are observed following LSMO addition. All the composites exhibit magnetic moments near 3.5 μ B Mn −1 at the ground state indicating desirable magnetic properties. Shape memory properties are evaluated following the application of programming and recovery cycles on the as‐printed single‐cell auxetic structure. The evaluation of the bulk strain on the auxetic structure for 0 and 20 wt% LSMO/SMP nanocomposite reveals no change in shape fixity, a slight decrease in shape recovery ratio from 88 to 81%, and a significant improvement in the shape recovery rate from 124.3 to 179.5% min −1 .

Authors

Institutions

Publication Details

Journal
Advanced Engineering Materials
Published
2025-01-16
DOI
https://doi.org/10.1002/adem.202402423
Citations
4
Primary Topic
Polymer composites and self-healing
Type
article
Field-Weighted Citation Impact
2.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

4D Printing of Magnetic Shape Memory Polymer—La0.7Sr0.3MnO3 Nanocomposite

Mohammad H. Yousuf, M. Egilmez, Wael Z. Abuzaid
4 citations
Advanced Engineering Materials
Polymer composites and self-healing
2.43
article

4D Printing of Magnetic Shape Memory Polymer—La0.7Sr0.3MnO3 Nanocomposite

Mohammad H. Yousuf, M. Egilmez, Wael Z. Abuzaid
article en
4 citations

Abstract

Four‐dimensional printing has revolutionized traditional three‐dimensional printing (3D printing) by using smart materials such as shape memory polymers (SMPs). As nonmagnetic materials, SMPs require direct exposure to heat for the shape memory effect to be triggered. This limitation can be addressed by incorporating magnetic nanoparticles, allowing the SMP/nanocomposite to be remotely heated through induction heating. This work focuses on the (LSMO) manganite with an optimized stoichiometry of x = 0.3. Polycrystalline LSMO is sintered from pure powders and later added to SMP through the solution mixing route, and finally extruded into filaments to be used for 3D printing. Significant improvements in the elastic modulus, yield, and ultimate strengths are observed following LSMO addition. All the composites exhibit magnetic moments near 3.5 μ B Mn −1 at the ground state indicating desirable magnetic properties. Shape memory properties are evaluated following the application of programming and recovery cycles on the as‐printed single‐cell auxetic structure. The evaluation of the bulk strain on the auxetic structure for 0 and 20 wt% LSMO/SMP nanocomposite reveals no change in shape fixity, a slight decrease in shape recovery ratio from 88 to 81%, and a significant improvement in the shape recovery rate from 124.3 to 179.5% min −1 .

Advanced Engineering MaterialsVol. 27(5)
American University of Sharjah (AE), University of Wollongong in Dubai (AE)
American University of Sharjah
Openalex Percentile: Top 14%
Polymer composites and self-healing
2.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.