Multilayer Substrate‐Mediated Tuning Resonance of Plasmon and SERS EF of Nanostructured Silver
Bibliographic record
Abstract
A thin-film of dielectric on a reflecting surface constituting a multilayer substrate modulates light intensity due to the interference effect. A nanostructure consisting of randomly oriented silver particles of different shapes, sizes, and interparticle spacings supports multiple plasmon resonances and is observed to have a broad extinction spectrum that spans the entire visible region. Combining the two systems by fabricating the nanostructure on the thin-dielectric film of the multilayer substrate yields a new composite structure which is observed to modulate both the extinction spectrum and the SERS EF (surface enhanced Raman scattering enhancement factor) of the nanostructure as the thickness of the thin-film dielectric is varied. The frequency and intensity of the visible extinction spectrum vary dramatically with the dielectric thickness and in the intermediate thickness range the spectrum has no visible band. The SERS EF determined for the composite structure as a function of the thin-film dielectric thickness varies by several orders of magnitude. Strong correlation between the magnitude of the SERS EF and the extinction intensity is observed over the entire dielectric thickness range indicating that the extinction spectrum corresponds to the excitation of the plasmon resonances of the nanostructure. A significant finding which has potential applications is that the composite structure has synergic effect to boost SERS EF of the nanostructure by an order of magnitude or more compared to the same nanostructure on an unlayered substrate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".