Toward Automatic Label-Free Whispering Gallery Modes Biodetection with a Quantum Dot-Coated Microsphere Population
Bibliographic record
Abstract
We explore a new calibration-free approach to biodetection based on whispering gallery modes (WGMs) without a reference measure and relative shifts. Thus, the requirement to keep track of the sensor position is removed, and a freely moving population of fluorophore-doped polystyrene microspheres can now fulfill this role of sensing resonator. Breaking free from fixed surface-based biosensing promotes adhesion between the microsphere sensors and the analytes since both can now be thoroughly mixed. The 70-nm-wide spectrum of green fluorescent microbeads allows us to monitor over 20 WGMs simultaneously without needing evanescent light coupling into the microspheres, hence enabling remote sensing. Since the exact radius of each microsphere is unknown a priori, it requires algorithmic analyses to obtain a reliable result for the refractive index of a solution. We first test our approach with different solutions of alcohol in water obtaining 3 x 10(-4) precision on the refractive index at lower concentrations. Then, the solutions of bacterial spores in water yield clear evidence of biodetection in the statistical analysis of WGMs from 50 microspheres. To extend the fluorescence spectral range of our WGM sensors, we present preliminary results on coating microspheres with CdSe/ZnS quantum dots.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".