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Record W2318025824 · doi:10.1021/jp5025069

Surface Plasmon Resonances in Oriented Silver Nanowire Coatings on Optical Fibers

2014· article· en· W2318025824 on OpenAlexaff
Jean-Michel Renoirt, Marc Debliquy, Jacques Albert, Anatoli Ianoul, Christophe Caucheteur

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCarleton University
FundersWaalse Gewest
KeywordsMaterials scienceNanowireCladding (metalworking)Surface plasmon resonanceRefractive indexOptical fiberLocalized surface plasmonOpticsSurface plasmonOptoelectronicsCoatingPlasmonNanoparticleNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Silver nanowires 1–3 μm in length and diameters of 0.04–0.05 μm were synthesized by a polyol process and deposited on a single mode optical fiber with the Langmuir–Blodgett technique. For nanowire surface coverage of ∼40% and partial orientation of their long axis obtained by controlling the deposition parameters, the optical properties of the nanowire coating become identical to those of a uniform metal coating obtained by sputtering or evaporation. Excitation of the nanowires by the polarized evanescent field of fiber cladding modes at near-infrared wavelengths near 1.5 μm results in surface plasmon-like resonances in the transmission spectrum of the optical fiber. The polarization-dependent loss (PDL) spectrum of the tilted fiber Bragg grating used to excite the cladding modes shows a pronounced characteristic dip indicative of a plasmon resonance for radially polarized light waves and complete shielding of light for azimuthally polarized light. The PDL dip shifts at a rate of 650 nm/(refractive index unit) when the surrounding refractive index is changed, a 10-fold increase compared to uncoated fiber gratings and similar to that of uniform metal coated gratings. The advantage of the nanowire approach is to provide a much increased contact surface area for biomolecular recognition-based immunosensing.

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 score0.075
Threshold uncertainty score0.405

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 integrity0.0000.001
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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations49
Published2014
Admission routes1
Has abstractyes

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