Self-Assembly of Large-Scale and Ultrathin Silver Nanoplate Films with Tunable Plasmon Resonance Properties
Bibliographic record
Abstract
We describe a rapid, simple, room-temperature technique for the production of large-scale metallic thin films with tunable plasmonic properties assembled from size-selected silver nanoplates (SNPs). We outline the properties of a series of ultrathin monolayer metallic films (8-20 nm) self-assembled on glass substrates in which the localized surface plasmon resonance can be tuned over a range from 500 to 800 nm. It is found that the resonance peaks of the films are strongly dependent on the size of the nanoplates and the refractive index of the surrounding dielectric. It is also shown that the bandwidth and the resonance peak of the plasmon resonance spectrum of the metallic films can be engineered by simply controlling aggregation of the SNP. A three-dimensional finite element method was used to investigate the plasmon resonance properties for individual SNPs in different dielectrics and plasmon coupling in SNP aggregates. A 5-17 times enhancement of scattering from these SNP films has been observed experimentally. Our experimental results, together with numerical simulations, indicate that this self-assembly method shows great promise in the production of nanoscale metallic films with enormous electric-field enhancements at visible and near-infrared wavelengths. These may be utilized in biochemical sensing, solar photovoltaic, and optical processing 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) | 0.000 | 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".