Light‐Activated Micromotors in Air Propelled by Thermal Convection
Bibliographic record
Abstract
Micromotors are an attractive cutting-edge technology that exhibit controllable motion in response to chemical reactions or external stimuli. These nature-inspired materials are widely explored for use in environmental remediation, and drug delivery, other emerging applications. Until now, the micromotors field is restricted to applications in aqueous environments, as achieving controllable motion in air while overcoming gravity remains a significant challenge. Herein, for the first time, to our knowledge, we introduce a system capable of overcoming gravity to achieve light-induced thermal convective motion in air, driven by near-infrared light excitation. The micromotors are composed of spiky, pollen-like ZnO microparticles coated with gold nanoparticles, which interact photothermally with the NIR light, generating a thermal gradient that induces propulsion of the micromotor system. Lanthanide-doped upconverting nanoparticles are deposited onto the micromotor surface to enable nanothermometric monitoring of surface temperature, providing critical information needed to describe the system's thermal behavior in air. This micromotor platform provides a versatile approach to overcome gravity and induce a controllable movement in a gaseous matrix, opening new opportunities to develop proof-of-concepts and applications using this aerodynamic micromotor approach.
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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".