Plasmonic properties of suspended nanodisc structures for enhancement of the electric field distributions
Bibliographic record
Abstract
Metallic nanostructures possess many advantages for utilization in various applications including sensing applications. However, achieving an easy to fabricate platform with high sensitivity performance is considered the main challenge in designing such nanostructures. Two factors should be considered when designing a wavelength based nanostructured sensor; the field distribution around the nanostructures, and the full width at half maximum (FWHM) of the sensor spectral response. In this paper, we study suspended nanodisc structures as a candidate for enhancing the electric field distribution in-plane and out of plane axes of the nanodiscs, and hence enhancing the probe depth of the nanosensor. Another advantage of the suspended nanodisc structure is that it offers a 100% surface coverage. The Finite Difference Time Domain (FDTD) method is used for the study of optical properties of the structure. The resonance location depends on the dimensions of the nanodiscs as well as the polymer base. Higher order modes can also be supported by nanodiscs with larger dimensions. The local electric field is enhanced as it is distributed in both perpendicular and horizontal planes with respect to the plane of gold nanodiscs without altering the FWHM relative to the regular nanodisc structure. This is considered as an advantage in sensing applications. Another advantage of this structure is that it can be readily fabricated by nanoimprint lithography and gold deposition.
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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 | 0.000 |
| 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".