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Record W2468495053 · doi:10.1364/oe.24.012466

Lasing of whispering gallery modes in optofluidic microcapillaries

2016· article· en· W2468495053 on OpenAlexafffund
F. Alexandre, Nicolas Riesen, Kirsty Gardner, Tanya M. Monro, A. Meldrum

Bibliographic record

VenueOptics Express · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsLasing thresholdRefractive indexWhispering-gallery waveMaterials scienceOpticsCoatingLaserOptoelectronicsPolymerHigh-refractive-index polymerResonatorNanotechnologyPhysics

Abstract

fetched live from OpenAlex

This paper demonstrates lasing of the whispering gallery modes in polymer coated optofluidic capillaries and their application to refractive index sensing. The laser gain medium used here is fluorescent Nile Red dye, which is embedded inside the high refractive index polymer coating. We investigate the refractometric sensing properties of these devices for different coating thicknesses, revealing that the high Q factors required to achieve low lasing thresholds can only be realized for relatively thick polymer coatings (in this case ≥ 800 nm). Lasing capillaries therefore tend to have a lower refractive index sensitivity, compared to non-lasing capillaries which can have a thinner polymer coating, due to the stronger WGM confinement within the polymer layer. However we find that the large improvement in signal-to-noise ratio realized for lasing capillaries more than compensates for the decreased sensitivity and results in an order-of-magnitude improvement in the detection limit for refractive index sensing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.344

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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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 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

Citations34
Published2016
Admission routes2
Has abstractyes

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