Bibliographic record
Abstract
We investigate the functioning of a monolithically integrated surface plasmon resonance (SPR) device comprising a metal coated dielectric layer deposited atop a luminescence emitting quantum well (QW) wafer. The device takes advantage of the uncollimated and incoherent emission of QWs. The light modulations in the far field, where the surface plasmons are extracted by a grating, have been calculated for a continuum of energies and wavevectors injected by the substrate. We discuss the results of our calculations based on a tensorial rigorous coupled-wave analysis aimed at the full description of SPR coupling in QW semiconductor-based architectures, designed for biosensing applications. The surface roughness induced by various nanofabrication methods is also studied, given that it is one of the main limiting factors in diffraction-based SPR sensing. This aspect is studied for thin film microstructures operating in the visible and near-infrared spectral regions. The surface roughness and dielectric values for various deposition rates of very thin Au films are examined. We finally introduce a novel experimental method for direct mapping of the electromagnetic (EM) wave dispersion that enabled us monitoring of a massive amount of light-scattering related information. We present the results of far field measurements of the complete 3D dispersion relation of a SPR effect induced by this nanodevice. The quasi-real time method is applied for tracking SPR directly in the E(k) space. Those additional dimensions, measured with scalable tracking precision, reveal anisotropic surficial interactions and provide spectroscopic response for SPR.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".