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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 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

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.

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 score0.002
Threshold uncertainty score0.309

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 head0.004
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