Optical properties of gold particles with near micron size: localized and propagating surface plasmons
Bibliographic record
Abstract
This proceeding summarizes the optical properties of plasmonic structures from nanoscale to macroscale. Of particular interest, Au triangles and hole arrays of near micron size exhibit concomitantly surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) optical properties in the Vis-NIR region, resulting in excellent optical properties for biosensing. In transmission spectroscopy, 15 nm nanoparticles absorbs at λ = 525 nm, nanotriangles of > 200 nm edge length absorbs at λ > 600 nm while nanohole arrays exhibit a more complex spectrum including absorption and enhanced optical transmission (EOT) features. Nanohole arrays are also sensitive to refractive index (RI) change and it can be optimized by tailoring the hole diameter and the periodicity. Au triangles ranging from nano (200 nm) to micron size (1.5 μm) are active in LSPR with an absorption peak that redshifts with the increasing aspect ratio of the structure. In total internal reflection (TIR) experiment, Au triangles with an edge length of 500 nm or greater present an absorption peak at λ = 800 nm. Also, triangles of 700, 950 and 1800 nm have a maximum transmission around λ= 650 nm that is highly sensitive to refraction index (RI) variations. This absorption peak is attributed to propagating SPR, similarly to the optical phenomenon occurring on a smooth Au film as used in the Kretschmann configuration of SPR. Lastly, nanohole and microhole arrays spectra, measured in TIR, are a composite of both triangles (LSPR) and thin film spectra (SPR).
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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 | 0.000 |
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".