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Record W2293981370 · doi:10.1063/1.4943623

Plasmonic fiber-optic vector magnetometer

2016· article· en· W2293981370 on OpenAlexafffund
Zhaochuan Zhang, Tuan Guo, Xuejun Zhang, Jian Xu, Wenping Xie, Ming Nie, Qiang Wu, Bai‐Ou Guan, Jacques Albert

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGuangdong Science and Technology DepartmentBeijing University of Posts and TelecommunicationsNatural Science Foundation of Guangdong ProvinceNational Research Council CanadaNational Natural Science Foundation of China
KeywordsMaterials scienceOpticsPlasmonCladding modeOptoelectronicsOptical fiberSurface plasmon resonanceWavelengthCladding (metalworking)Surface plasmonMagnetometerFiber optic sensorMagnetic fieldNanoparticlePhysicsPolarization-maintaining optical fiberNanotechnology

Abstract

fetched live from OpenAlex

A compact fiber-optic vector magnetometer based on directional scattering between polarized plasmon waves and ferro-magnetic nanoparticles is demonstrated. The sensor configuration reported in this work uses a short section of tilted fiber Bragg grating (TFBG) coated with a nanometer scale gold film and packaged with a magnetic fluid (Fe3O4) inside a capillary. The transmission spectrum of the sensor provides a fine comb of narrowband resonances that overlap with a broader absorption of the surface plasmon resonance (SPR). The wavelength of the SPR attenuation in transmission shows high sensitivity to slight perturbations by magnetic fields, due to the strong directional scattering between the SPR attenuated cladding modes and the magnetic fluid near the fiber surface. Both the orientation (2 nm/deg) and the intensity (1.8 nm/mT) of magnetic fields can be determined unambiguously from the TFBG spectrum. Temperature cross sensitivity can be referenced out by monitoring the wavelength of the core mode resonance simultaneously.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.191
Teacher spread0.183 · 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

Citations93
Published2016
Admission routes2
Has abstractyes

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