Bibliographic record
Abstract
Metal nanocrystals support unique light-matter interactions through a phenomenon known as the localized surface plasmon resonance (LSPR).These resonances show a great deal of sensitivity to their local environment, and by altering this environment it is possible to produce new, "hybridized" plasmon modes.The far-field spectral qualities, and the near-field electromagnetic properties can by heavily manipulated through the generation of hybridized plasmon modes.This work explores the properties and functions of hybridized plasmon modes in a variety of systems.Finite-difference time-domain modelling use used to correlate the experimental spectral response is with the calculated near-field spatial distribution of hybridized modes in a number of systems involving silver nanocubes (AgNC).These systems demonstrate it is possible to carefully monitor the environment of a nanocrystal, induce an unusually sharp spectral extinction in dielectric-coated AgNCs, and manipulate the spatial distribution of plasmon modes in colloidal composite Ag@Cu2O core-shell nanocrystals.These properties are applied to functional materials, including photothermal and colour patterning of a plasmonic system driven by the embedment of an AgNC into a polymer matrix.The spectral signature of hybridized plasmon modes is used to both characterize and manipulate the degree of photothermal heating in this thermoplasmonic patterning system.Hybridized gap-plasmons are used to generate wide range of colours, with controllable palettes using AgNC-over-Au and AgNC-over-Ag nanoparticle-over-mirror (NPoM) films.Lastly the potential for alternative plasmonic materials for ultraviolet (UV) plasmonics was explored.The potential for In and Al nanocrystals for UV surface-enhanced Raman spectroscopy was investigated.All these systems demonstrate the potential and advantage of using hybridized plasmon modes to manipulate the far-field and near-field optical response of functional plasmonic materials.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".