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Record W2933649475 · doi:10.3390/s19071652

Tapered Fiber-Optic Mach-Zehnder Interferometer for Ultra-High Sensitivity Measurement of Refractive Index

2019· article· en· W2933649475 on OpenAlex
Vahid Ahsani, Martin Byung‐Guk Jun, Colin Bradley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSensors · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of WaterlooUniversity of Victoria
FundersKorea Carbon Capture and Sequestration R and D CenterMinistry of Trade, Industry and Energy
KeywordsTaperingInterferometryCladding (metalworking)Mach–Zehnder interferometerMaterials scienceRefractive indexOpticsOptical fiberMicrofiberSensitivity (control systems)Fiber optic sensorFusion splicingSingle-mode optical fiberPhotonic-crystal fiberOptoelectronicsPhysicsElectronic engineeringComposite material

Abstract

fetched live from OpenAlex

A Mach-Zehnder interferometer (MZI) based fiberoptic refractive index (RI) sensor is constructed by uniformly tapering standard single mode fiber (SMF) for RI measurement. A custom flame-based tapering machine is used to fabricate microfiber MZI sensors directly from SMFs. The fabricated MZI device does not require any splicing of fibers and shows excellent RI sensitivity. The sensor with a cladding diameter of 35.5 µm and length of 20 mm exhibits RI sensitivity of 415 nm/RIU for RI range of 1.332 to 1.384, 1103 nm/RIU for RI range of 1.384 to 1.4204 and 4234 nm/RIU for RI range of 1.4204 to 1.4408, respectively. The sensor reveals a temperature sensitivity of 0.0097 nm/°C, which is relatively low in comparison to its ultra-high RI sensitivity. The proposed inexpensive and highly sensitive optical fiber RI sensors have numerous applications in chemical and biochemical sensing fields.

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.

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 score0.276
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 head0.015
GPT teacher head0.223
Teacher spread0.209 · 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