Optimizing Refractive Index Sensitivity of Supported Silver Nanocube Monolayers
Bibliographic record
Abstract
The refractive index (RI) sensitivity of extinction spectra was compared experimentally for silver nanocubes in solution and in supported monolayers prepared by the Langmuir technique. The size of the nanocubes, RI of supporting dielectric substrate, and monolayer surface pressure were used as variables in refractive index sensing optimization. The dipolar plasmon modes of the colloidal nanocubes were found to have the highest RIS values of 176, 361, and 480 nm/RI units for 40, 80, and 130 nm cubes, respectively. The largest figure of merit (FOM) of 4.55 was measured for a quadrupolar mode of 130 nm nanocubes. When compared to suspensions, the refractive index sensitivities (RIS) of supported nanocubes were reduced by ∼50% and decreased with increasing monolayer surface pressure. The RIS of 40 nm cube monolayers appeared to be sensitive to the substrate RI due to the RI-dependent plasmon mode hybridization, resulting in dipolar and quadrupolar modes. The intensity of this quadrupolar peak was found to increase with the angle of incident light. This work shows that the use of high refractive index dielectric substrates, a passive molecular spacer, and large angles of incidence can improve the detection of plasmonic response by supported nanocube monolayers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".