Nanoislands as plasmonic materials
Bibliographic record
Abstract
Subwavelength metal nanoislands thermally dewetted from a thin film emerge as a powerful and cost-effective photonic material, due to the formation of substantially strong nano-gap-based plasmonic hot spots and their simple large-area nanofabrication. Unlike conventional nanostructures, nanoislands dewetted from thin metal films can be formed on a large scale at the wafer level and show substrate-dependent plasmonic phenomena across a broad spectral range from ultraviolet to infrared. Substrate-selective dewetting methods for metal nanoislands enable diverse nanophotonic and optoelectronic technologies, underlining mechanical, structural, and material properties of a substrate. Emerging bioplasmonic technology using metal nanoislands also serves as a high-throughput and surface-sensitive analytical technique with wide-ranging application in rapid, real-time, and point-of-care medical diagnostics. This review introduces an assortment of dewetting fabrication methods for metal nanoislands on distinct substrates from glass to cellulose fibers and provides novel findings for metal nanoislands on a substrate by three-dimensional numerical modeling. Furthermore, the plasmonic properties of metal nanoislands and recent examples for their photonic applications, in particular, biological sensing, are technically summarized and discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".