Plasmonic Properties of Supported Silver Nanocrystals: Tuning and Anisotropy
Bibliographic record
Abstract
Silver nanoparticles support strong localized surface plasmon resonances (LSPRs) in the visible region of the spectrum affording them great potential for refractive index sensing, molecular sensing, Raman and fluorescent signal enhancement, and fabrication of optical materials.Characterization of the effects on the plasmonic properties of the particles in terms of size, shape, and anisotropy in their local environment, and how these factors are related to intrinsic properties such as absorption, scattering, and transmission of incident radiation is required for the rational design of materials.This work focuses on the optical properties of ensembles of silver nanocubes (AgNCs) supported by dielectric materials.AgNCs support multiple plasmon resonances in the ultraviolet-visible region of the spectrum which are strongly influenced by anisotropy in their local environment.Nanocrystals of various sizes studied in this work were synthesized via the polyol method and dispersed as Langmuir films for transfer to solid substrates.Ultraviolet-visible spectroscopy was employed to monitor the peak positions and optical properties associated with the LSPR modes present in these systems.The peak positions, range, refractive index sensitivities (RIS), and figures of merit (FOM) were determined for observable modes in colloidal and contrasted with those of solid supported AgNCs.Modes present in colloidal AgNCs cannot be tuned independently while some modes present in solid supported AgNC allow for independent tuning by varying the anisotropy of the surrounding environment selectively.Similarly the effects of angle of incidence and polarization on transmission, scattering, and absorption of incident radiation is quantified for solid supported AgNCs as functions of size and refractive index of the solid support.
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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".