Ultrafast Miniature Robotic Swimmers with Upstream Motility
Bibliographic record
Abstract
With the development of materials science and micro-nano fabrication techniques, miniature soft robots at millimeter or submillimeter size can be manufactured and actuated remotely. The small-scaled robots have the unique capability to access hard-to-reach regions in the human body in a noninvasive manner. To date, it is still challenging for miniature robots to accurately move in the diverse and dynamic environments in the human body (e.g., in blood flow). To effectively locomote in the vascular system, miniature swimmers with upstream swimming capability are required. Herein, we design and fabricate a miniature robotic swimmer capable of performing ultrafast swimming in a fluidic environment. The maximum velocity of the swimmer in water is 30 cm/s, which is 60 body lengths. Moreover, in a tubular environment, the swimmer can still obtain a swimming velocity of 17 cm/s. The swimmer can also perform upstream swimming in a tubular environment with a velocity of 5 cm/s when the flow speed is 10 cm/s. The ultrasound-guided navigation of the swimmer in a phantom mimicking a blood vessel is also realized. This work gives insight into the design of agile undulatory milliswimmers for future biomedical applications.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".