Magnetoactive Metamaterials: A State‐of‐the‐Art Review
Bibliographic record
Abstract
Magnetoactive metamaterials (MMs) represent a cutting‐edge class of smart materials that integrate magnetoactive material with architected mechanical metastructures, enabling dynamic control over their mechanical, acoustic, and elastic properties through the application of external magnetic fields. This review presents an in‐depth summary of recent progress in MMs, emphasizing their design strategies, manufacturing methods, and wide‐ranging applications in areas like biomedical devices, soft robotics, and adaptive structures. The study particularly explores the integration of magnetoactive soft composite materials with mechanical metamaterials, highlighting their ability to achieve tunable physical and mechanical property changes, shape morphing, and wave manipulation. Key fabrication methods, including 3D/4D printing and conventional molding techniques, are discussed, emphasizing their role in creating complex, functional architectures. Additionally, the influence of embedded hard and soft magnetic particles on the performance of MMs made of soft elastomeric matrix is examined, emphasizing their role in achieving contactless actuation, rapid response, and multifunctionality. The review concludes with future research directions, advocating for the integration of machine learning techniques for optimized metamaterial design. The review may serve as a valuable resource for researchers and engineers aiming to harness the potential of these advanced adaptive materials for next‐generation technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".