Janus Micromotors for Photophoretic Motion and Photon Upconversion Applications Using a Single Near-Infrared Wavelength
Bibliographic record
Abstract
External stimuli can trigger changes in temperature, concentration, and momentum between micromotors and the medium, causing their propulsion and enabling them to perform different tasks with improved kinetic efficiencies. Light-activated micromotors are attractive systems that achieve improved motion and have the potential for high spatiotemporal control. Photophoretic swarming motion represents an attractive means to induce micromotor movement through the generation of temperature gradients in the medium, enabling the micromotors to move from cold to hot regions. The micromotors studied herein are assembled with Fe 3 O 4 nanoparticles, and NaGdF 4:Yb 3+,Er 3+ /NaGdF 4:Yb 3+ and LiYF 4:Yb 3+,Tm 3+ upconverting nanoparticles. The Fe 3 O 4 nanoparticles were localized to one hemisphere to produce a Janus architecture that facilitates improved upconversion luminescence with the upconverting nanoparticles distributed throughout. Under 976 nm excitation, Fe 3 O 4 nanoparticles generate the temperature gradient, while the upconverting nanoparticles produce visible light that is used for micromotor motion tracking and triggering of reactive oxygen species generation. As such, the motion and application of the micromotors are achieved using a single excitation wavelength. To demonstrate the practicality of this system, curcumin was adsorbed to the micromotor surface and degradation of Rhodamine B was achieved with kinetic rates that were over twice as fast as the static micromotors. The upconversion luminescence was also used to track the motion of the micromotors from a single image frame, providing a convenient means to understand the trajectory of these systems. Together, this system provides a versatile approach to achieving light-driven motion while taking advantage of the potential applications of upconversion luminescence such as tracking and detection, sensing, nanothermometry, particle velocimetry, photodynamic therapy, and pollutant degradation.
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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".