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Record W2010359322 · doi:10.1039/c4an02257b

Metal-enhanced fluorescence and FRET on nanohole arrays excited at angled incidence

2015· article· en· W2010359322 on OpenAlexafffund
Hugo-Pierre Poirier-Richard, Maxime Couture, Thibault Brulé, Jean‐François Masson

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de MontréalFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for Innovation
KeywordsFörster resonance energy transferFluorescenceExcited stateMetalChemistryMaterials sciencePhotochemistryOpticsAtomic physicsPhysics

Abstract

fetched live from OpenAlex

The influence of experimental parameters on the performance of plasmonic sensors is of great importance in analytical sciences. The plasmon coupling conditions (angle of incidence, metal composition, laser frequency and excitation/emission properties of fluorophores) were thus investigated for surface plasmon-enhanced fluorescence on metallic nanohole arrays. Optimal fluorescence enhancements were achieved when the plasmon resonance, the excitation laser and the fluorophore's excitation wavelengths were matched. The enhancement of the acceptor emission of a rhodamine 6G(Rh6G)-Quasar670™ FRET pair was achieved on the nanohole arrays by tuning the plasmon wavelength with the maximal overlap of the donor's emission and acceptor excitation. Silver nanohole arrays achieved larger fluorescence enhancement than gold nanohole arrays at 532 nm, while gold nanohole arrays led to larger fluorescence enhancement at 635 nm. These results demonstrate the importance of tuning the plasmon coupling conditions for surface plasmon-enhanced fluorescence sensing.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.403

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations20
Published2015
Admission routes2
Has abstractyes

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