Towards Mass-Scale Micro-Assembly Systems Using Magnetotactic Bacteria
Bibliographic record
Abstract
Magnetotactic bacteria (MTB) can be used in a coordinated fashion to assemble micro-objects in an orderly manner. To perform micro-assembly tasks, magnetotaxis-based control is used where a directional magnetic field is generated to induce a torque on an embedded chain of membrane-based magnetic nanoparticles (MNP) named magnetosomes. Such chain acts like a nano-compass or a nano-steering system embedded in each bacterium. Such magnetotaxis-based control is then used to orient the MTB in such a way that the laminar flow created by their flagella bundles provides a displacement force on the micro-objects being assembled. Since the force is generated by the bacteria, relatively large micro-objects can be moved with no requirement for electrical energy except for a relatively small value required for inducing a directional torque on the chain of magnetosomes in the cells. Because the energy required to generate the directional torque is independent on the population of MTB being involved but the displacement force can be scaled up with the use of a larger swarm while the total workspace would typically be at microscale dimensions, the energy required for the coils configuration around such workspace and responsible for generating the directional torque can be reduced further to a very low level and hence, makes the implementation of mass-scale bacterial micro-assembly systems, a viable approach. Based on these findings, we propose a corresponding mass-scale system based on many workspaces, each relying on a swarm of MTB to perform micro-assembly tasks in parallel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".