Development of 3D metallic nano-structures for sensing applications
Bibliographic record
Abstract
The interaction of a light with an array of sub-wavelength holes in a thin metal film has given rise to a unique optical property, the so-called extraordinary optical transmission (EOT). Not only does EOT of a sub-wavelength hole array exceed the incident light on the holes, but also could surpass the diffraction limit of the light and provide an intensified electric field at vicinity of the holes. These phenomena have introduced many new possibilities in the field of photonic applications. Surface Plasmon Resonance (SPR) sensing is one of the most common applications of a metallic sub-wavelength hole array structure and it results from EOT spectral shifts due to changes in the refractive index of materials on the top or bottom of the structure. In this thesis, novel sub-wavelength hole array structures with a surface plasmon energy matching property between the top and bottom of the structure have been fabricated and tested in a bulk-SPR sensing application. The numerical and experimental results demonstrated improved SPR sensitivities and higher electric field intensity at the edges of the holes at the EOT wavelength for an energy-matched structure compared to a conventional sub-wavelength hole array structure. However, in order to fabricate the novel structure, two systematic studies were performed to elucidate the effects of various geometrical parameters and different composition and thickness of the adhesion layers on the EOT properties of sub-wavelength hole array structures. Many other applications of a sub-wavelength hole array could potentially benefit from these novel structures due to their enhanced EOT properties over conventional structures.
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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) | 0.001 | 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".