3D Printing of Multilayer Magnetic Miniature Soft Robots with Programmable Magnetization
Bibliographic record
Abstract
Magnetically driven miniature soft robots exhibit fast and dexterous responses to an applied external magnetic field. With remote manipulation, controlled navigation of robots can be realized within hard‐to‐access spaces for potential use in the human body. Existing magnetic miniature soft robots using digital light processing are fabricated from planar sheets, and thus have limited shape transformations and locomotive behaviors. Herein, a multilayer 3D printing method is reported for patterning magnetic nanoparticles in ultraviolet (UV)‐curable polymer matrix. Various multilayer 3D structures within 10 mm in overall size are fabricated with controlled volumes at different parts, which outperform 2D folded shapes in terms of robustness and kinematic flexibility. By programming heterogeneous magnetization within discrete multilayer robot segments, magnetic torque‐induced shape changes including gripping, rolling, swimming, and walking are induced by a global actuation field. Stacked design features with minimum dimension of 200 μm and encoded magnetization with resolution of 350 μm can be realized in the printing process. Meanwhile, enhanced deformation flexibility and formation of orientation‐anchoring mechanisms are created by integrating multiple materials with distinct mechanical and magnetic properties, respectively, which enables the creation of versatile 3D multi‐material actuators.
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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".