High Sensitivity of Plasmonic Microstructures near the Transition from Short-Range to Propagating Surface Plasmon
Bibliographic record
Abstract
The optical properties of Au triangles with 700 and 950 nm and 1.8 μm edge length and of microhole arrays with a 3 μm periodicity show a strong absorption peak highly sensitive to refractive index with excitation in the Kretschmann configuration of surface plasmon resonance (SPR). This absorption peak is due to a SPR phenomenon only present with microstructures of micrometer length, as demonstrated with SPR active materials, Au and Ag; but absent in SPR inactive materials, TiO x N y and Ag triangles covered with a thin film of silver oxide. The Au triangle arrays were exhibiting: 1) two transmission minima at λ = 525 and 800 nm with low refractive index sensitivity and 2) a transmission maximum near λ = 600 nm exhibiting a high sensitivity to bulk refractive index for the triangles with an edge length of 950 nm (1993 nm/RI) and 1.8 μm (1038 nm/RI). This is a significant improvement of the sensitivity compared to nanoparticles and is near the sensitivity of SPR on a smooth film. The transmission minima at λ = 800 nm was also present in microhole arrays in a Au film, but of reduced intensity as the size of the hole decreases. Moreover, the microhole arrays with smaller holes also presented a strong transmission minimum around λ = 600 nm, with a similar wavelength, bandwidth and sensitivity as SPR on a smooth gold film. Lastly, the response measured for the formation of a monolayer on the triangle arrays results in a large shift of the SPR response of 13 ± 3 nm, which correlates to a short penetration depth of 24 nm. Triangle arrays investigated in the present configuration could significantly improve SPR biosensing by increasing the measured response from a binding event and could decrease the temperature drift due to a lower bulk refractive index sensitivity.
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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) | 0.001 | 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".