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Record W2015989285 · doi:10.1364/ome.1.000128

Anomalous permittivity and plasmon resonances of copper nanoparticle conformal coatings on optical fibers

2011· article· en· W2015989285 on OpenAlexafffund
Liyang Shao, Jason P. Coyle, Seán T. Barry, Jacques Albert

Bibliographic record

VenueOptical Materials Express · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research ChairsUniversity of Ottawa
KeywordsMaterials sciencePlasmonPermittivityCopperNanoparticleConformal mapOptical fiberSurface plasmon resonanceOpticsOptoelectronicsSurface plasmonDielectricNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

The conformal coating of a 50 nm-thick layer of copper nanoparticles deposited with pulse chemical vapor deposition of a copper (I) guanidinate precursor on the cladding of a single mode optical fiber was monitored by using a tilted fiber Bragg grating (TFBG) photo-inscribed in the fiber core. The pulse-per-pulse growth of the copper nanoparticles is readily obtained from the position and amplitudes of resonances in the reflection spectrum of the grating. In particular, we confirm that the real part of the effective complex permittivity of the deposited nano-structured copper layer is an order of magnitude larger than that of a bulk copper film at an optical wavelength of 1550 nm. We further observe a transition in the growth behavior from granular to continuous film (as determined from the complex material permittivity) after approximately 20 pulses (corresponding to an effective thickness of 25 nm). Finally, despite the remaining granularity of the film, the final copper-coated optical fiber is shown to support plasmon waves suitable for sensing, even after the growth of a thin oxide layer on the copper surface.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations50
Published2011
Admission routes2
Has abstractyes

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