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Record W2020686434 · doi:10.1063/1.4865932

Au-coated tilted fiber Bragg grating twist sensor based on surface plasmon resonance

2014· article· en· W2020686434 on OpenAlexafffund
Changyu Shen, Yang Zhang, Wenjun Zhou, Jacques Albert

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsSurface plasmon resonanceTwistMaterials scienceFiber Bragg gratingOpticsResonance (particle physics)WavelengthOptoelectronicsSurface plasmonGratingPlasmonNanoparticleNanotechnologyPhysics

Abstract

fetched live from OpenAlex

A fiber twist sensor based on the surface plasmon resonance (SPR) effect of an Au-coated tilted fiber Bragg grating (TFBG) is proposed. The SPR response to the twist effect on an Au-coated TFBG (immersing in distilled water) is studied theoretically and experimentally. The results show that the transmission power around the wavelength of SPR changes with the twist angle. For the twist ranging from 0° to 180° in clockwise or anti-clockwise directions, the proposed sensor shows sensitivities of 0.037 dBm/° (S-polarized) and 0.039 dBm/° (P-polarized), which are almost 7.5 times higher than that of the current similar existing twist sensor.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.194
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations68
Published2014
Admission routes2
Has abstractyes

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