Mechanically-tuned optofluidic lenses for in-plane focusing of light
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In-plane lenses are desired for light manipulation within on-chip platforms. Such an in-plane lens can be achieved through optofluidic lens technologies that provide tunability of optical parameters through alterations to the shape or size of the lens. However, passive optofluidic lenses are often more desirable than active optofluidic lenses. In this work, we design a passive mechanically-tuned optofluidic lens. Tunability is brought about by placing a microdroplet between two substrate plates and varying the plate separation. We carry out analyses with an experimental optical setup and theoretical ray tracing. The experimental optical setup makes use of a fluorescent dye filler fluid to assist in the visualization and measurement of the back focal length. Ultimately, the sensitivity of the back focal length to a change in plate separation is shown, with strong agreement between experimental and theoretical analyses. It is envisioned that such a mechanically-tuned optofluidic lens will be used in a myriad of in-plane optical applications.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it