Self-noise-filtering phase-sensitive surface plasmon resonance biosensing
Bibliographic record
Abstract
Emerged as an upgrade of currently available Surface Plasmon Resonance (SPR) biosensing in terms of sensitivity, phase-sensitive SPR technology still requires the minimization of instrumental noises to profit from its projected ultra-low detection limit (10(-8) refractive index units and lower). We present a polarimetry-based methodology for the efficient reduction of main instrumental noises in phase-sensitive measurements. The proposed approach employs a sinusoidal phase modulation of pumping light and is based on selection of proper modulation amplitude and initial phase relation for the first two modulation harmonics (F1 and F2), which enables to subtract amplitude drifts in the difference (F1 - F2) signal while doubling the phase response. The resulting effect can be called self-noise-filtering, since it implies an inherent noise subtraction in every phase sensing measurement. This methodology allows one to tackle drifts related to instabilities of light sources and optical elements and thus drastically lower the detection limit of phase-sensitive SPR sensing even in relatively simple and noisy experimental implementations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".