Analysis of magnetic configuration and its effect on motion in magnetically actuated soft miniature robots within tubular confinement
Bibliographic record
Abstract
Soft, magnetically actuated robots offer promising potential for medical applications due to their simple fabrication, controllability, cargo loading ability and flexibility. This research focuses on the design, modeling, and behavior of soft, millimeter-scale filamentous robots composed of Gelatin Methacrylate (GelMa) hydrogels and embedded with micromagnets for magnetic actuation. These robots are designed for navigation within the human urinary tract. The study investigates two distinct configurations: screw-like and fin-like robots, each responding differently to an external rotating magnetic field. The screw-like robots propel forward through synchronized helical motion, while the fin-like robots rely on interaction with surrounding surfaces for crawling motion. Experimental frequency response tests reveal that fin-like robots exhibit three times faster motion than screw-like robots in confined environments, reaching velocities of up to 18 mm/s. Additionally, the influence of micromagnet location inside the filaments on their propulsion dynamics is explored, highlighting the potential for optimized performance in medical applications requiring navigation through narrow channels, such as the ureter. Further optimization is proposed to enhance control and performance in more complex biological environments. Supplementary Information: The online version contains supplementary material available at 10.1007/s12213-025-00188-1.
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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".