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Record W2965796510 · doi:10.3390/s19153357

Monitoring of Carbon Dioxide Using Hollow-Core Photonic Crystal Fiber Mach–Zehnder Interferometer

2019· article· en· W2965796510 on OpenAlexafffund
Farid Ahmed, Vahid Ahsani, Kaveh Nazeri, Ehsan Marzband, Colin Bradley, Ehsan Toyserkani, Martin Byung‐Guk Jun

Bibliographic record

VenueSensors · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of VictoriaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMach–Zehnder interferometerInterferometryPhotonic-crystal fiberCore (optical fiber)Materials scienceOptical fiberOpticsPhotonicsOptoelectronicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Monitoring of greenhouse gases is essential to understand the present state and predict the future behavior of greenhouse gas emissions. Carbon dioxide (CO2) is the greenhouse gas of most immediate concern, because of its high atmospheric concentration and long lifetime. A fiber-optic Mach–Zehnder interferometer (MZI) is proposed and demonstrated for the laboratory-scale monitoring of carbon dioxide concentration. The interferometric sensor was constructed using a small stub of hollow-core photonic crystal fiber between a lead-in and lead-out standard single mode fiber, with air-gaps at both interfaces. At room temperature and atmospheric pressure, the sensor shows the sensitivity of 4.3 pm/% CO2. The device was packaged to demonstrate the laboratory-scale leakage detection and measurement of CO2 concentration in both subsurface and aqueous environments. The experimental study of this work reveals the great potential of the fiber-optic approach for environmental monitoring of CO2.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
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 head0.020
GPT teacher head0.242
Teacher spread0.223 · 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

Citations33
Published2019
Admission routes2
Has abstractyes

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