High Q Nanoplasmonic biosensor based on surface lattice resonances in the visible spectrum
Bibliographic record
Abstract
Plasmonic nano-antennas are widely accepted as suitable platforms for biosensing tasks because Surface Plasmon Resonance (SPR) is very sensitive to changes in its environment. However, recent studies suggest that SPRs may have limited Quality (Q) factors, especially in comparison with their dielectric counterparts. Therefore, this paper attempts to innovate the design of plasmonic nano-antennas to achieve high Q factors through Surface Lattice Resonance (SLR) in the visible frequency band. This resonance is linked with plasmonic nanostructures organized in arrays. The structure consists of a metal-dielectric-metal configuration at the base with metallic nanopillars protruding upward. The nanophotonic device has been investigated for refractometric sensing applications. The maximum Q factor achieved as a result of this work is 245, which has been compared with contemporary plasmonic metasurface Q factors. The simulation framework has been implemented in COMSOL Multiphysics, which employs the Finite Element Method (FEM). Regression analysis has been used to formulate the calibration curve for the sensor. High Q factors provide better selectivity for biosensing applications.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".