Probing the Anisotropy of SERS Enhancement with Spatially Separated Plasmonic Modes in Strongly Coupled Silver Nanocubes on a Dielectric Substrate
Bibliographic record
Abstract
The utilization of substrate–particle interactions provides a route to tuning the optical properties of strongly coupled supported plasmonic nanoparticles. In this work fine control over interparticle and particle–substrate interactions is demonstrated using Langmuir–Blodgett monolayers of silver nanocubes deposited onto titanium oxide (TiO x ) thin films of varying thickness. By using two Raman reporters, a Rhodamine-B (RhB) and benzenethiol (BT), surface-enhanced Raman spectroscopy (SERS) independently examines electromagnetic (EM) enhancement at the substrate/nanocube interface (RhB) and at the surface of the cubes, where the label is predominately 20–40 nm away from the dielectric substrate (BT). For RhB the SERS enhancement factor (EF) drops as much as an order of magnitude on 20 nm TiO x with respect to glass. However, for BT, a maximum SERS EF of (2.5 ± 0.4) × 10 5 was observed on TiO x compared to (1.5 ± 0.4) × 10 5 on glass, an increase of 60%. Control over the organization of the nanocube monolayer reveals that maximum enhancement occurs in small, discrete clusters of nanocubes as opposed to large aggregates. Fine control over the optical properties and near-field EM distribution of coupled nanostructures can be accomplished through tuning of the dielectric properties of the substrate yielding a route to optimizing properties for field-enhanced plasmonic applications.
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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".