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Record W2328930937 · doi:10.1109/jlt.2016.2543138

Bulk Sensing Using a Long-Range Surface-Plasmon Dual-Output Mach–Zehnder Interferometer

2016· article· en· W2328930937 on OpenAlexaff
Hui Fan, Pierre Berini

Bibliographic record

VenueJournal of Lightwave Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInterferometryCladding (metalworking)Mach–Zehnder interferometerAstronomical interferometerRefractive indexOpticsMaterials scienceOptoelectronicsSurface plasmonSurface plasmon polaritonPlasmonBiosensorDynamic rangePhysicsNanotechnology

Abstract

fetched live from OpenAlex

Optical bulk (refractometric) sensing of sample solutions is demonstrated using dual-output Mach-Zehnder interferometers built from long-range surface-plasmon polariton waveguides operating at a free-space wavelength of 1375 nm. The device of interest was constructed by embedding Au stripes in Cytop claddings and etching a fluidic channel through the top cladding of one arm of the interferometer to expose the Au stripe. Bulk sensing was carried out by flowing sequentially a series of solutions of different refractive index through the microfluidic channel. The optical powers of the two outputs responded sinusoidally and were complimentary, as expected in theory. Three detection schemes aiming at improving the detection limit are analyzed showing that the device benefits from a 2× larger dynamic range, and has the ability to suppress common perturbations, relative to a single output. A detection limit of ~4 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> RIU is demonstrated, which can be further improved by lengthening the sensing channel. The device is promising for application as a biosensor.

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)0.001
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 score0.090
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.252
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.

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

Citations23
Published2016
Admission routes1
Has abstractyes

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