Polymer filament–based in situ microrobot fabrication using magnetic guidance
Bibliographic record
Abstract
In this article, we present a new three-dimensional printing inspired method for in situ fabrication of mobile magnetic microrobots with complex topology by bending a polymer filament on demand directly inside an enclosed operational environment. Compared with current microrobot fabrication methods that typically involve multiple microfabrication steps and complex equipment, the proposed method is simply and fast. The target shape is formed as the filament is fed through a hot needle inserted into the workspace, and the filament bending moment is induced by attaching a tip magnet at the end of the filament and projecting magnetic fields wirelessly from external electromagnetic coils. The filament bending mechanics and the behavior of the bending zone are analyzed and verified through bending experiment. A shape planner is developed for automatically controlling the fabrication process of any desired planar shapes, and the shape creation potential of this method is also studied. Magnetically active millimeter-scale robotic devices of different planar shapes are fabricated using polylactic acid filament with diameter as small as 100 μm. As demonstrations of the in situ formation of functional microrobotic devices, a micro-gripper is fabricated and controlled to assemble a cell cage. A micro-spring is created as a manipulating tool with force sensing capability. We, thus, show the utility of the fabrication method for creating complex microrobot shapes remotely in enclosed environments for advanced microrobotic applications, with the potential for scaled down applications in health care and microfluidics.
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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 | 0.001 | 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".