Designing a magnetic micro-robot for transporting stiff filamentous microcargo
Bibliographic record
Abstract
In recent years, the medical industry has witnessed a growing interest in developing minimally invasive procedures, with magnetic micro-robots emerging as a promising approach. These micro-robots possess the ability to navigate through various media, including viscoelastic and non-Newtonian fluids, enabling targeted drug delivery and medical interventions. Many designs that have been proposed to date employ a contact-based method for transporting a payload. Undesired adhesion between the cargo and the carrier can make release at the target site problematic. The primary objective of our current work is to modify the design of magnetically actuated helical micro-robots to enable transportation of cargo in Newtonian fluids without requiring contact between the robot and the cargo. We conduct a comprehensive study on the shape and geometrical parameters of the helical micro-robot, specifically focusing on its capability to transport passive filaments that are rigid to thermal noise. Based on our analysis, we propose a novel design consisting of three helical sections with alternating handedness, including two pulling and one pushing microhelices. We first focus on naturally straight filaments but also show that the micro-robot can capture filaments with intrinsic curvature and those with a spherical payload attached at one end. Our findings offer valuable insights into the physics of helical micro-robots and their potential for medical procedures and drug delivery. Furthermore, the proposed non-contact method for delivering filamentous cargo could lead to the development of more effective micro-robots for medical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".