Self-referencing and Sensitivity Optimization in Photonic Crystal Slabs for Biosensing Applications
Bibliographic record
Abstract
Photonic crystal slabs (PCS) are explored in the context of optofluidic refractive index (RI) sensing for portable, label-free, biosensing applications. The accuracy of RI sensors is limited by noise signals that cause a change in RI that cannot be differentiated from the signal of interest. For this reason self-referencing schemes that provide rejection of common mode signals, and an inherent temperature stabilization approach, are explored. A novel referencing method that allows for frequency shifts to be read out in the transmission power spectrum is proposed and characterized. In terms of improving sensing metrics the relevant characteristics of various PCS architectures are explored numerically. In addition, a novel suspended \\emph{air-substrate} device that offers greatly improved sensitivity is proposed and characterized. An experimental measurement near the theoretical detection limit for a PCS is demonstrated. In understanding measurement errors the crossed-polarization effect and its practical limitations are explored numerically.
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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) | 0.000 | 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".