Soft Magneto‐Responsive Shape Memory Foam Composite Actuators
Bibliographic record
Abstract
Abstract Soft magnetic composites are exceptional because they can be controlled remotely, move quickly, conform to hard things, and interact with people safely. However, even with all these features, magnetic elastomers suffer a lack of stability due to the high softness of the elastomer. This issue affects their controllability and repeatability. This article introduces a novel conceptual design of magneto‐responsive shape memory polyurethane (SMP) foam composites with high stability and reversibility. The fabrication technique is based on the silicone resins filled with strontium ferrite magnetic particles and a thin SMP foam placed onto one side. Material properties, room‐temperature shape recovery features, and magnetization conditions necessary for the process are determined by experimental studies of composite actuators. As a result, a workable, light, stable, soft composite gripper with programmable magnetic patterns is created, which can carry out activities like grabbing, holding, and moving objects in horizontal and vertical directions when a low magnetic field is applied. The SMP foam increases the contact surface and decreases the weight by up to three times providing better stability compared to the magnetic elastomer without SMP foam. The shape‐recoverable gripper with a small contract area can lift objects eight times heavier than its weight.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".