Steering Micromotors via Reprogrammable Optoelectronic Paths
Bibliographic record
Abstract
Steering micromotors is important for using them in practical applications and as model systems for active matter. This functionality often requires magnetic materials in the micromotor, taxis behavior of the micromotor, or the use of specifically designed physical boundaries. Here, we develop an optoelectronic strategy that steers micromotors with programmable light patterns. In this strategy, light illumination turns hydrogenated amorphous silicon conductive, generating local electric field maxima at the edge of the light pattern that attracts micromotors via positive dielectrophoresis. As an example, metallo-dielectric Janus microspheres that self-propelled under alternating current electric fields were steered by static light patterns along customized paths and through complex microstructures. Their long-term directionality was also rectified by ratchet-shaped light patterns. Furthermore, dynamic light patterns that varied in space and time enabled more advanced motion controls such as multiple motion modes, parallel control of multiple micromotors, and the collection and transport of motor swarms. This optoelectronic steering strategy is highly versatile and compatible with a variety of micromotors, and thus it possesses the potential for their programmable control in complex environments.
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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".