Exploiting the responses of magnetotactic bacteria robotic agents to enhance displacement control and swarm formation for drug delivery platforms
Bibliographic record
Abstract
Magnetotactic bacteria MC-1 (MTB) synthesize a chain of magnetic nanoparticles called magnetosomes to navigate in deep-sea environments by orienting themselves in the direction of the Earth’s magnetic field. MTB’s inherent mobility and ability to be controlled by exposition to an external magnetic field has become of increasing interest for micromanipulation and drug transport applications. In the traditional control schemes, MTB were oriented by exposure to an external magnetic field causing them to align with the magnetic field lines. Directional changes were applied below a critical frequency and, as such, MTB were still able to swim along the generated magnetic field lines. The approach presented here proposes to apply to the MTB an oscillating magnetic field with a frequency beyond a critical limit to in order to exploit the time averaging magnetic field motion behavior of the bacteria cells. Results indicate that a time-multiplexed magnetic field made of various directional cycling fields can control the MTB more efficiently with less power, which is an advantage for future human-scale medical 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 | 0.000 |
| 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".