IRONSperm swimming by Rigid-Body Rotation Versus Transverse Bending Waves
Bibliographic record
Abstract
Abstract Cell membrane potential affects the electrostatic self-assembly of magnetizable nanoparticles around the flagellum of sperm cells, leading to the formation of biohybrid microrobots (i.e. IRONSperm) with various bending stiffness. Here we explain the influence of bull sperm cell membrane potential on the formation of two types of IRONSperm samples that are produced by electrostatic self-assembly. The first is a proximal-coated soft body with nanoparticles concentrated on the head to maintain high flexibility of the flagellum and create a passively propagating transverse bending wave under the influence of an external rotating magnetic field. The second is a rigid-body with nanoparticles approximately uniformly distributed along the length to provide arbitrary geometry that maintains a constant chiral shape and propel by rotation about its long axis. We present a magneto-elastohydrodynamic model to predict the swimming speed at low Reynolds number for rigid IRONSperm with arbitrary shapes, and show that decreasing the bending stiffness allows the model to capture the behavior of its soft counterpart. While the response of a rigid chiral IRONSperm is distinguished by a greater swimming speed with a smooth decay with frequency, the benefit of a soft flagellum in certain scenarios would present a much smaller range of frequencies for wireless actuation.
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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".