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Record W2329672541 · doi:10.1103/physrevb.88.115427

Experimental evidence of nanometer-scale confinement of plasmonic eigenmodes responsible for hot spots in random metallic films

2013· article· en· W2329672541 on OpenAlexafffund
Arthur Losquin, S. Camelio, David Rossouw, Mondher Besbes, F. Pailloux, David Babonneau, Gianluigi A. Botton, Jean‐Jacques Greffet, Odile Stéphan, Mathieu Kociak

Bibliographic record

VenuePhysical Review B · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsMcMaster University
FundersDirection Générale de l’ArmementNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsPlasmonNanometreMaterials scienceSurface plasmonCondensed matter physicsElectron energy loss spectroscopySilicon nitrideCharacterization (materials science)NanotechnologySiliconMolecular physicsOptoelectronicsPhysicsTransmission electron microscopyComposite material

Abstract

fetched live from OpenAlex

We report on the identification and nanometer scale characterization over a large energy range of random, disorder-driven, surface plasmons in silver semicontinuous films embedded in silicon nitride. By performing spatially resolved electron energy loss spectroscopy experiments, we experimentally demonstrate that these plasmons eigenmodes arise when the films become fractal, leading to the emergence of strong electrical fields (``hot spots'') localized over few nanometers. We show that disorder-driven surface plasmons strongly depart from those usually found in nanoparticles, being strongly confined and randomly and densely distributed in space and energy. Beyond that, we show that they have no obvious relation with the local morphology of the films, in stark contrast with surface plasmon eigenmodes of nanoparticles.

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 categoriesInsufficient payload (model declined to judge)
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.035
GPT teacher head0.326
Teacher spread0.291 · 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.

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

Citations57
Published2013
Admission routes2
Has abstractyes

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