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Record W2979313353 · doi:10.1364/oe.27.031013

Direct Inscription of on-surface waveguides in polymers using a mid-ir fiber laser

2019· article· en· W2979313353 on OpenAlexafffund
Jean-Philippe Bérubé, Clément Frayssinous, Jérôme Lapointe, Simon Duval, Vincent Fortin, Réal Vallée

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceOpticsLaserWaveguideRefractive indexPolymerOptical fiberAbsorption (acoustics)PlanarPolycarbonateSubstrate (aquarium)Optoelectronics

Abstract

fetched live from OpenAlex

A detailed study of photo-inscribed optical waveguides in PMMA and polycarbonate using a mid-IR laser is presented. The wavelength of the laser is tuned near the absorption peaks of stretching C-H molecular bonds and the focused beam is scanned onto the surface of planar polymer samples. For the first time, we report the formation of optical waveguides in both polymers through resonant absorption of the laser beam. The optical properties of the waveguides were thoroughly assessed. An elliptic Gaussian mode is guided at the surface of both polymers. Insertion losses of 3.1 dB for a 30 mm long on-surface waveguide inscribed in PMMA were recorded. Such waveguides can interact with the external medium through evanescent coupling. As a proof of concept, the surface waveguides are used as highly sensitive refractometric sensors. An attenuation dynamical range of 35 dB was obtained for a liquid that matches the index of the PMMA substrate. Our results pave the way for large scale manufacturing of low cost biocompatible photonic devices.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.473

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 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

Citations34
Published2019
Admission routes2
Has abstractyes

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