Gold Nano-Island Platforms for Localized Surface Plasmon Resonance Sensing: A Short Review
Bibliographic record
Abstract
Nano-islands are entities (droplets or other shapes) that are formed by spontaneous dewetting (agglomeration, in the early literature) of thin and very thin metallic (especially gold) films on a substrate, done by post-deposition heating or by using other sources of energy. In addition to thermally generated nano-islands, more recently, nanoparticle films have also been dewetted, in order to form nano-islands. The localized surface plasmon resonance (LSPR) band of gold nano-islands was found to be sensitive to changes in the surrounding environment, making it a suitable platform for sensing and biosensing applications. In this review, we revisit the development of the concept of nano-island(s), the thermodynamics of dewetting of thin metal films, and the effect of the substrate on the morphology and optical properties of nano-islands. A special emphasis is made on nanoparticle films and their applications to biosensing, with ample examples from the authors' work.
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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 | 0.002 |
| 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) | 0.004 | 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".