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Record W2127489443 · doi:10.1002/andp.201200149

Plasmonics and single‐molecule detection in evaporated silver‐island films

2012· article· en· W2127489443 on OpenAlexafffund
Golam Moula, Rogelio Rodríguez–Oliveros, Pablo Albella, José A. Sánchez‐Gil, Ricardo F. Aroca

Bibliographic record

VenueAnnalen der Physik · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlasmonSurface plasmon resonanceMaterials scienceRaman scatteringLocalized surface plasmonSubstrate (aquarium)Raman spectroscopySurface plasmonResonance (particle physics)Layer (electronics)MoleculeMolecular physicsNanotechnologyOptoelectronicsOpticsNanoparticleChemistryAtomic physicsPhysics

Abstract

fetched live from OpenAlex

Abstract The plasmonic origin of surface‐enhanced Raman scattering (SERS) leads to the concept of hotspots and plasmon coupling that can be realized in the interstitial regions, or on specially engineered, silver and gold nanostructures. It is also possible to achieve spatial locations of high local field or hotspots on silver‐island films (SIF) allowing single‐molecule detection (SMD). When a single monomolecular layer coating the SIFs contains dye molecules dispersed in it, single‐molecule impurities, (with an average of one hundred dye molecules in 1 µm 2 , which is the field of view of the micro‐Raman system), SMD is observed as a rare statistical event. Here, the SMD results for silver‐island films are presented, with the same nominal mass thickness, but differing in the localized surface plasmon resonance that is a function of the temperature of substrate during deposition. A blue‐shifted plasmon can be seen as a decrease in plasmon coupling for deposition at higher temperature. A simple two‐particle model for localized plasmon resonance coupling calculations, including the shape and substrate effects seems to explain the trend of observations.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2012
Admission routes2
Has abstractyes

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