Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
Bibliographic record
Abstract
This paper discusses fluorescent core microcavity-based sensors that can operate in a microfluidic analysis setup. These structures are based on the formation of a fluorescent quantum-dot (QD) coating on the channel surface of a conventional microcapillary. Silicon QDs are especially attractive for this application, owing in part to their negligible toxicity compared to the II-VI and II-VI compound QDs, which are legislatively controlled substances in many countries. While the ensemble emission spectrum is broad and featureless, an Si-QD film on the channel wall of a capillary features a set of sharp, narrow peaks in the fluorescence spectrum, corresponding to the electromagnetic resonances for light trapped within the film. The peak wavelength of these resonances is sensitive to the external medium, thus permitting the device to function as a refractometric sensor in which the QDs never come into physical contact with the analyte. The experimental methods associated with the fabrication of the fluorescent-core microcapillaries are discussed in detail, as well as the analysis methods. Finally, a comparison is made between these structures and the more widely investigated liquid-core optical ring resonators, in terms of microfluidic sensing capabilities.
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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 | 0.001 |
| 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 | 0.001 | 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 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".