Fine tuning of plasmonic properties of monolayers of weakly interacting silver nanocubes on thin silicon films
Bibliographic record
Abstract
Plasmonic properties, such as refractive index sensitivity (RIS), surface enhancement of the Raman signal (SERS), fluorescence quenching, and photocatalytic activity, of monolayers of weakly interacting monodisperse silver nanocubes were qualitatively modified in a very well controlled manner by supporting them on thin silicon films with varying thickness. Such fine tunability is made possible by the strong dependence of the nanocube dipolar (D) and quadrupolar (Q) plasmon mode hybridization on the refractive index of the supporting substrate. By increasing the Si film thickness from zero to ~25 nm we were able to "shift" the D resonance mode by up to 200 nm for ~80 nm cubes without significantly affecting the Q mode. The silicon supported nanocubes showed a significant improvement in RIS via the Q mode with a figure of merit greater than 6.5 and about an order of magnitude enhancement of the SERS signal due to the stronger electric field created by the D mode. Such substrates also showed a ~10 times decrease in rhodamine 6G fluorescence as well as the rates of amorphous carbon formation. The study proposes a new way to design and engineer plasmonic nanostructures.
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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".