Design and simulation of a refractive index sensor based on SPR and LSPR using gold nanostructures
Bibliographic record
Abstract
A refractive index sensor to detect chemicals based on surface plasmon resonance is designed and analytically investigated by a finite element method via COMSOL multiphysics. A tunable sensitivity is achieved by patterning the continuous metallic thin films with cavities or protrusions. The simulation results exhibit that the improved sensitivity of the devices is attributed to the co-excitation of SPR and LSPR modes. This result is obtained by studying the variation of the electric field intensity along several cut lines through the metallic layer. The penetration depth of the plasmon field is characterized, and accordingly, SPR and LSPR modes of the sensors are determined. The proposed sensor is calibrated for eight substances with refractive indices ranging from 1.333 to 1.38. The linearity of the calibration curve indicates the applicability of the sensor to identify the refractive indices of unknown mediums as a function of resonance wavelength. This study is proposing a new way to show the duality nature of patterned thin films to support both propagating and localized surface plasmon modes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".