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Surface enhanced Raman scattering of silver sensitized cobalt nanoparticles in metal–dielectric nanocomposites

2008· article· en· W2161649556 on OpenAlexafffund
Jérémie Margueritat, J. Gonzalo, C. N. Afonso, U. Hörmann, G. Van Tendeloo, Adnen Mlayah, D. B. Murray, Lucien Saviot, Yong Jin Zhou, Min‐Ho Hong, B S Luk’yanchuk

Bibliographic record

VenueNanotechnology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersEuropean Social FundNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSilver nanoparticleNanocompositeCobaltRaman scatteringNanoparticleDielectricRaman spectroscopyExcited statePlasmonNanotechnologyMetalScatteringSurface plasmonChemical engineeringOptoelectronicsOpticsMetallurgyAtomic physics

Abstract

fetched live from OpenAlex

We report the preparation of a new type of nanocomposite containing cobalt and silver nanoparticles organized in parallel layers with a well controlled separation. This arrangement allows the observation of an enhanced low-frequency Raman signal at the vibration frequency of cobalt nanoparticles excited through the surface plasmons of silver nanoparticles. Numerical simulations of the electric field confirm the emergence of hot spots when the separation between silver and cobalt nanoparticles is small enough.

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 score0.002
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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

Citations14
Published2008
Admission routes2
Has abstractyes

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