Comprehensive modeling of corkscrew motion in micro-/nano-robots with general helical structures
Bibliographic record
Abstract
Micro-/nano-robots (MNRs) have impressive potential in minimally invasive targeted therapeutics through blood vessels, which has disruptive impact to improving human health. However, the clinical use of MNRs has yet to happen due to intrinsic limitations, such as overcoming blood flow. These bottlenecks have not been empirically solved. To tackle them, a full understanding of MNR behaviors is necessary as the first step. The common movement principle of MNRs is corkscrew motion with a helical structure. The existing dynamic model is only applicable to standard helical MNRs. In this paper, we propose a dynamic model for general MNRs without structure limitations. Comprehensive simulations and experiments were conducted, which shows the validity and accuracy of our model. Such a model can serve as a reliable basis for the design, optimization, and control of MNRs and as a powerful tool for gaining fluid dynamic insights, thus accelerating the development of the field. Micro-/nano-robots (MNRs) hold potential for minimally invasive targeted therapies through blood vessels, but clinical use is hindered by challenges such as overcoming blood flow. This study proposes a dynamic model for general MNRs without structural limitations, validated through simulations and experiments, to facilitate their design, optimization, and control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".