MétaCan
Menu
Back to cohort
Record W2014603333 · doi:10.1364/oe.17.007630

Insights into tunnelling rays: outperforming guided rays in fiber-optic sensing device

2009· article· en· W2014603333 on OpenAlexafffund
Jianjun Ma, Wojtek J. Bock, Andrea Cusano

Bibliographic record

VenueOptics Express · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversité du Québec en Outaouais
FundersCanada Research Chairs
KeywordsOpticsTotal internal reflectionQuantum tunnellingCladding (metalworking)Optical fiberPhysicsCore (optical fiber)Reflection (computer programming)Fiber optic sensorPolarization-maintaining optical fiberOptoelectronicsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

We report that, for an evanescent-wave (EW) based fiber-optic sensor, the less skewed tunnelling rays and a small number of very steep guided rays experience total internal reflection (TIR) at one end-face of the air-clad fiber segment. As a result, they demonstrate nine times greater efficiency in collecting EW-power than rays that do not exhibit end-face- TIR. This tunneling ray group originates from a launching ring that is seven times wider than that of end-face-TIR capable guided rays, enabling it to dominate the overall detected EW-power level. Strongly supporting this conclusion is the counter-intuitive experimental observation that the detected power at one end of the fiber abruptly drops 91% when the back reflection at the opposite end is eliminated, while an isotropic light source is placed at the core-cladding interface between these two ends. The mechanism of non-propagating ray excitation accounts for this high efficiency. We also indicate that many highly skewed tunnelling rays are naturally isolated from the external world because, outside the fiber core, they are accessible only via their EW fields even at the fiber end-faces. The findings reported here have significant implications for the design of future high-performance EW-based fiber sensing devices for the analysis of surface-event.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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 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

Citations13
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueOptics ExpressSame topicAdvanced Fiber Optic SensorsFrench-language works237,207