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Record W4413381052 · doi:10.1364/ome.569884

High-bismuth-content phospho-silicate fiber for optical amplification

2025· article· en· W4413381052 on OpenAlexfundno aff
Thomas Meyneng, J. Lefebvre, Saber Jalilpiran, Théo Guérineau, Philippe Labranche, Sophie LaRochelle, Younès Messaddeq

Bibliographic record

VenueOptical Materials Express · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsBismuthMaterials scienceSilicateOptical fiberFiberOpticsComposite materialChemistry

Abstract

fetched live from OpenAlex

We report a fiber fabrication process combining modified chemical vapor deposition and solution doping, which maximizes material retention to produce bismuth-doped fibers with high doping content. With a detectable amount of Bi at the 0.1 wt.% level, fabricated fibers exhibit low levels of background and unsaturable loss. Moreover, a flat and broad absorption profile is obtained and is attributed to a balanced level of phosphorus and silica-related bismuth active centers (BACs). Our photoluminescence results suggest excitation-dependent emission spectra, including broad luminescence under 808 and 980 nm pump wavelengths. The BPFs present a flat gain spectrum, free from OH-related absorption, reaching 34 dB peak gain with 100 nm bandwidth (>20 dB) and a noise figure (NF) in the range of 3.7 - 5.7 dB over the E- and S-band.

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 score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.230
Teacher spread0.211 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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