Millimeter-scale flexible robots with programmable three-dimensional magnetization and motions
Bibliographic record
Abstract
Flexible magnetic small-scale robots use patterned magnetization to achieve fast transformation into complex three-dimensional (3D) shapes and thereby achieve locomotion capabilities and functions. These capabilities address current challenges for microrobots in drug delivery, object manipulation, and minimally invasive procedures. However, possible microrobot designs are limited by the existing methods for patterning magnetic particles in flexible materials. Here, we report a method for patterning hard magnetic microparticles in an elastomer matrix. This method, based on ultraviolet (UV) lithography, uses controlled reorientation of magnetic particles and selective exposure to UV light to encode magnetic particles in planar materials with arbitrary 3D orientation with a geometrical feature size as small as 100 micrometers. Multiple planar microrobots with various sizes, different geometries, and arbitrary magnetization profiles can be fabricated from a single precursor in one process. Moreover, a 3D magnetization profile allows higher-order and multi-axis bending, large-angle bending, and combined bending and torsion in one sheet of polymer, creating previously unachievable shape changes and microrobotic locomotion mechanisms such as multi-arm power grasping and multi-legged paddle crawling. A physics-based model is also presented as a design tool to predict the shape changes under magnetic actuation.
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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".