Mechanical modulation method for ultrasensitive phase measurements in photonics biosensing
Bibliographic record
Abstract
A novel polarimetry methodology for phase-sensitive measurements in single reflection geometry is proposed for applications in optical transduction-based biological sensing. The methodology uses altering step-like chopper-based mechanical phase modulation for orthogonal s- and p- polarizations of light reflected from the sensing interface and the extraction of phase information at different harmonics of the modulation. We show that even under a relatively simple experimental arrangement, the methodology provides the resolution of phase measurements as low as 0.007 deg. We also examine the proposed approach using Total Internal Reflection (TIR) and Surface Plasmon Resonance (SPR) geometries. For TIR geometry, the response appears to be strongly dependent on the prism material with the best values for high refractive index Si. The detection limit for Si-based TIR is estimated as 10(-5) in terms Refractive Index Units (RIU) change. SPR geometry offers much stronger phase response due to a much sharper phase characteristics. With the detection limit of 3.2*10(-7) RIU, the proposed methodology provides one of best sensitivities for phase-sensitive SPR devices. Advantages of the proposed method include high sensitivity, simplicity of experimental setup and noise immunity as a result of a high stability modulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".