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Record W4282914601 · doi:10.1038/s41598-022-13873-z

Rapid and sensitive magnetic field sensor based on photonic crystal fiber with magnetic fluid infiltrated nanoholes

2022· article· en· W4282914601 on OpenAlexaff
Saeed Azad, Satyendra K. Mishra, G. Rezaei, Ricardo Izquierdo, Bora Ung

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsSNC-Lavalin (Canada)Université LavalÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceMagnetic fieldOptical fiberPhotonic-crystal fiberFiber optic sensorSensitivity (control systems)PhotonicsMagnetic nanoparticlesOpticsOptoelectronicsPhysicsNanotechnologyNanoparticleElectronic engineering

Abstract

fetched live from OpenAlex

A fast response time (0.1 s) magnetic field sensor has been demonstrated utilizing a photonic crystal fiber with nano-size air holes infiltrated with polyethylene glycol based magnetic fluid. The effect of magnetic nanoparticles concentration in the fluid on the magneto-optical sensor performance and its dependence under varying magnetic-field loads was investigated in detail. In particular, the sensor response was analytically modelled with a Langevin function with a good fit (R[Formula: see text]0.996). A threshold sensing point as low as 20 gauss was recorded and a detection range of 0-350 gauss was demonstrated by means of optical transmission measurements. The experimental results were validated by theory using a waveguide light transmission model fed by finite-element method simulations of the principal guided modes in the infiltrated fiber sensor. The simple interrogation scheme, high sensitivity and quick response time makes the proposed hybrid fiber-optic magneto-fluidic probe a promising platform for novel biochemical sensing applications.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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