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Record W1967076188 · doi:10.1021/la061894p

Spatially Inhomogeneous Enhancement of Fluorescence by a Monolayer of Silver Nanoparticles

2006· article· en· W1967076188 on OpenAlexaff
Anatoli Ianoul, A. Bergeron

Bibliographic record

VenueLangmuir · 2006
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsCarleton University
FundersNational Institutes of Health
KeywordsRhodamine 6GFluorescenceMonolayerMaterials scienceNanoparticleNear-field scanning optical microscopeSilver nanoparticleAnalytical Chemistry (journal)SIGNAL (programming language)Quenching (fluorescence)RhodamineSurface plasmon resonanceOpticsChemistryOptical microscopeNanotechnologyScanning electron microscope

Abstract

fetched live from OpenAlex

Near-field scanning optical microscopy (NSOM) was applied to study the effect of a two-dimensional array of silver nanoparticles on the spatial distribution and magnitude of fluorescence signal enhancement for a monolayer of Rhodamine 6G (Rh6G). Twenty polyelectrolyte monolayers were deposited between the nanoparticles and the dye by a layer-by-layer deposition technique resulting in a 15-20 nm separation cushion, necessary to minimize the fluorescence signal quenching. The fluorescence signal in NSOM images was found to be distributed inhomogeneously as small (100-200 nm in diameter) fluorescent clusters with typically 5-30 times higher fluorescence intensities than a sample without nanoparticles. The position and relative intensity of the clusters was found to be dependent on the excitation wavelength, suggesting that the enhancement originates from the nanoparticle surface plasmon resonance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2006
Admission routes1
Has abstractyes

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