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Record W3093281536 · doi:10.3390/molecules25204661

Gold Nano-Island Platforms for Localized Surface Plasmon Resonance Sensing: A Short Review

2020· review· en· W3093281536 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecules · 2020
Typereview
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsConcordia University
Fundersnot available
KeywordsDewettingSurface plasmon resonanceNanotechnologyMaterials sciencePlasmonSubstrate (aquarium)Nano-Colloidal goldNanoparticleThin filmDeposition (geology)BiosensorLocalized surface plasmonEconomies of agglomerationOptoelectronicsChemical engineeringComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.265
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it