Discrimination of Bulk and Surface Refractive Index Change in Plasmonic Sensors with Narrow Bandwidth Resonance Combs
Bibliographic record
Abstract
A method to enable surface plasmon resonance (SPR) sensors to discriminate between bulk and surface-localized refractive index changes is demonstrated with modified gold-coated tilted fiber Bragg grating SPR sensors (TFBG-SPR). Without this capability, all high-resolution SPR sensors should be using reference channels and strict temperature control to prevent the contamination of the desired detection of surface-localized chemical or binding events by drift of the refractive index of the medium, in which the experiment is carried out. The very fine comb of high-quality-factor resonances of a TFBG-SPR device coupled to the large differential sensitivity of some of the resonances to various perturbations is used to measure unambiguously the refractive index changes within a surface layer thinner than 25 nm from those of the bulk surrounding. The enabling modification of the conventional TFBG-SPR is a reduction of the gold coating from its optimum value near 50-30 nm: at this lower thickness, a surface plasmon wave can still be excited by a limited number of cladding mode resonances, but at the same time, the metal is thin enough to allow modes away from the SPR to tunnel across the metal and probe the bulk RI value. Measurements and simulations of the deposition of a self-assembled monolayer of 1-dodecanethiol in ethanol show that the bulk refractive index changes as small as 0.0004 can be distinguished from the formation of a 1 nm thick coating on the surface of the fiber.
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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 | 0.000 | 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".