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Record W4205404622 · doi:10.1109/tnb.2022.3144313

Hollow-Core Microstructured Optical Fiber Based Refractometer: Numerical Simulation and Experimental Studies

2022· article· en· W4205404622 on OpenAlexaff
N. Ayyanar, G. Thavasi Raja, Julia S. Skibina, Yashar E. Monfared, Anastasiya A. Zanishevskaya, Andrey A. Shuvalov, Gryaznov A. Yu

Bibliographic record

VenueIEEE Transactions on NanoBioscience · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiosensorAnalyteMicrostructured optical fiberMaterials sciencePhotonic-crystal fiberRefractive indexBiomoleculeOptical fiberRefractometerCore (optical fiber)OptoelectronicsFiberPhotonicsSensitivity (control systems)Fiber optic sensorOpticsGraded-index fiberNanotechnologyChemistryElectronic engineeringChromatography

Abstract

fetched live from OpenAlex

In this paper, we numerically and experimentally propose a novel hollow-core microstructured optical fiber (HC-MOF) biosensor for refractive index determination. The sensing mechanism of the proposed sensor is based on photonic bandgap effect and the location of transmission maxima of the fiber, which is strongly depend on the liquid analyte RI filled in the fiber core. The proposed HC-MOF biosensor demonstrates the spectral sensitivity of 5636.3 nm/RIU with a RI detection range of 1.333 to 1.3385 for different ratios of plasma in blood serum in our experimental studies. The HC-MOF proposed here can detect similar liquid analytes with RI close to 1.33. The proposed sensor with a high sensitivity, ease of operation and the possibility of real-time sensing has a strong potential for detection of liquid analytes and biomolecules with possible applications in medicine, chemistry, and biology.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.767

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)0.0000.000

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 head0.029
GPT teacher head0.299
Teacher spread0.270 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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