Colloidal plasmonic nanostar antennas with wide range resonance tunability
Bibliographic record
Abstract
Gold nanostars display exceptional field enhancement properties and tunable resonant modes that can be leveraged to create effective imaging tags, phototherapeutic agents, and hot electron-based photocatalytic platforms. Despite having emerged as the cornerstone among plasmonic nanoparticles with respect to resonant strength and tunability, some well-known limitations have hampered their technological implementation. Herein we tackle these recognized intrinsic weaknesses, which stem from the complex, and thus computationally untreatable morphology and the limited sample monodispersity, by proposing a novel 6-spike nanostar, which we have computationally studied and synthetically realized, as the epitome of 3D plasmonic nanoantenna with wide range plasmonic tunability. Our concerted computational and experimental effort shows that these nanostars combine the unique advantages of nanostructures fabricated from the top-down and those synthesized from the bottom-up, showcasing a unique plasmonic response that remains largely unaltered on going from the single particle to the ensemble. Furthermore, they display multiple, well-separated, narrow resonances, the most intense of which extends in space much farther than that observed before for any plasmonic mode localized around a colloidal nanostructure. Importantly, the unique close correlation between morphology and plasmonic response leads the resonant modes of these particles to be tunable between 600 and 2000 nm, a unique feature that could find relevance in cutting edge technological 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 | 0.001 | 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".