Luminescent Capillary‐Based Whispering Gallery Mode Sensors: Crossing the Lasing Threshold
Bibliographic record
Abstract
Silicon nanocrystals (Si NCs) present many advantages for sensor applications, including relatively bright and stable luminescence, low‐to‐negligible toxicity, and high physical and chemical robustness. In this work, the efforts in creating capillary‐based sensors from smooth films of oxide‐embedded SiNCs are summarized, and their responses to dye‐doped polymer films are compared. The main method is to form sub‐micrometer luminescent coatings on microcapillary channel walls. The coating must have a high index of refraction so that it can support the luminescence whispering gallery modes (WGMs) which propagate through the film and extend into the channel medium. Using Si NCs, a general refractometric sensing and the detection of layer‐by‐layer polyelectrolyte deposition on the capillary channel are demonstrated. The Si NC sensors are exceptionally robust and can be cleaned and re‐used multiple times. The main limitation of the method currently involves the relatively slow detection (requiring typically more than 20 s per collected luminescence spectrum) due to the low light levels associated with the Si NC luminescence. Finally, our most recent work is discussed, which aims to extend the luminescent capillary sensor into the lasing regime.
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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 | 0.001 |
| 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".