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Record W2088367346 · doi:10.1366/000370203322005337

Surface-Enhanced Resonance Raman Scattering: Single-Molecule Detection in a Langmuir—Blodgett Monolayer

2003· article· en· W2088367346 on OpenAlexaff
Carlos José Leopoldo Constantino, Tibebe Lemma, Patrícia Alexandra Antunes, Paul J. G. Goulet, Ricardo F. Aroca

Bibliographic record

VenueApplied Spectroscopy · 2003
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMonolayerLangmuir–Blodgett filmRaman scatteringArachidic acidRaman spectroscopyChemistryMoleculeAnalytical Chemistry (journal)FluorescenceResonance (particle physics)LangmuirPhysical chemistryAdsorptionOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Surface-enhanced resonance Raman scattering (SERRS) is used for single-molecule detection from spatially resolved 1-microm2 sections of a Langmuir-Blodgett (LB) monolayer deposited onto a Ag film. The target molecule, bis (benzimidazo) thioperylene (BZP), is dispersed in an arachidic acid monomolecular layer containing one BZP molecule per microm2, which is also the probing area of the Raman microscope. For concentrated samples (attomole quantities in the field of view), average SERRS, surface-enhanced fluorescence (SEF), and Raman imaging, including line mapping and global images at different temperatures, were recorded. Single-molecule SERRS spectra, obtained using an LB monolayer, present changes in bandwidth and relative intensities, highlighting the properties of single-molecule SERRS that are lost in average SERRS measurements of mixed LB monolayers obtained at the same temperatures. Also, the dilute system phenomenon of blinking is discussed with regard to results obtained from LB monolayers. The dilution process used in the single-molecule LB SERRS work is independently supported by fluorescence results obtained from very dilute solutions with monomer concentrations down to 10(-12) M.

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 score0.011
Threshold uncertainty score0.710

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations18
Published2003
Admission routes1
Has abstractyes

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