MétaCan
Menu
Back to cohort
Record W7117105935 · doi:10.1109/jlt.2025.3648142

High Sensitivity Evanescent Field-Based Micro-Resonator Using Porous Cladding for Sensing Applications in the Visible Range

2025· article· W7117105935 on OpenAlexafffund
Pauline Girault, Laurent Oyhenart, Théo Rouanet, S. Joly, Guillaume Beaudin, Jean‐François Bryche, Michael Canva, Paul G. Charette, L. Bechou

Bibliographic record

VenueJournal of Lightwave Technology · 2025
Typearticle
Language
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaInstitut National des Sciences Appliquées de LyonUniversité Grenoble AlpesAgence Nationale de la RechercheUniversité de Sherbrooke
KeywordsCladding (metalworking)Refractive indexEvanescent waveResonatorVisible spectrumAqueous solutionPorosityWaveguideLayer (electronics)

Abstract

fetched live from OpenAlex

In this work, we fabricate and study a sensitive polymer-based optical sensor for visible range detection in water environment. The studied transducer is a micro-resonator relying on the interaction between the evanescent wave and the aqueous medium to measure the medium refractive index change. The micro-resonator consists of a waveguide with PMMA polymer as the core layer and porous silica as the lower cladding layer to significantly enhance the evanescent field ratio and sensitivity. The first results show a very good resonator quality factor of around 2.10$^{4}$and a sensitivity of 255nm/RIU at 760nm for aqueous solutions with glucose at different concentrations. These state-of-the-art results obtained in the visible range demonstrate that a polymer-based sensor with a porous cladding appears as a promising platform for evanescent field-based sensing in the visible range without any surface chemical functionalization in an aqueous medium.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Lightwave TechnologySame topicPhotonic and Optical DevicesFrench-language works237,207