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Record W2128447328 · doi:10.1039/b602626e

Fabrication of stable bimetallic nanostructures on Nafion membranes for optical applications

2006· article· en· W2128447328 on OpenAlexafffund
Ramón A. Álvarez‐Puebla, G.A. Nazri, Ricardo F. Aroca

Bibliographic record

VenueJournal of Materials Chemistry · 2006
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of Canada
KeywordsBimetallic stripMaterials scienceNanostructureNafionRaman scatteringChemical engineeringRaman spectroscopyMonolayerAbsorption (acoustics)ElectrochemistryAnalytical Chemistry (journal)NanotechnologyElectrodeOpticsOrganic chemistryComposite materialChemistryPhysical chemistryMetalMetallurgy

Abstract

fetched live from OpenAlex

Novel stable crystalline bimetallic silver–gold nanostructures homogeneously dispersed on Nafion were prepared by galvanic substitution of a vacuum evaporated silver island film with gold. The method allows control of the composition of the bimetallic nanostructures and tuning of optical properties. Formation of the nanoalloy was monitored by UV-Vis absorption, SEM-EDX, XRD, AFM, ATR-FTIR, and Raman scattering. Bimetallic nanostructure growth was monitored by UV-Vis absorption and surface-enhanced Raman scattering (SERS), by casting an aliquot from a dilute solution of 2-naphthalenethiol (2-NAT) on the composite surface. SERS intensity increases with galvanic substitution, reaching a maximum, and providing a material that delivers SERS enhancement several times higher than those obtained with regular silver and gold island films. Optical enhancement is also fairly homogeneous throughout the treated Nafion surface; this is demonstrated by mapping the average SERS intensity of a mixed Langmuir–Blodgett monolayer bis(benzimidazo)perylene and stearic acid excited with 514 and 633 nm laser lines.

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 score0.012
Threshold uncertainty score0.429

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations22
Published2006
Admission routes2
Has abstractyes

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