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Record W7117165959 · doi:10.1109/jlt.2025.3648293

Single-Mode ZBLAN Optical Fiber Couplers: Minimizing Losses and Devitrification

2025· article· W7117165959 on OpenAlexafffund
Gebrehiwot Tesfay Zeweldi, Tina Lam, Mark P. Andrews, Martin Rochette

Bibliographic record

VenueJournal of Lightwave Technology · 2025
Typearticle
Language
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZBLANArgonDevitrificationX-ray photoelectron spectroscopyOptical fiberFiberAtmosphere (unit)Inert gasHard-clad silica optical fiber

Abstract

fetched live from OpenAlex

Single-mode ZBLAN optical fiber couplers with coupling ratios as high as 50%/50% are demonstrated. Thanks to a controlled argon atmosphere during tapering, low-loss single-mode couplers are fabricated with excess losses of$< $0.75 dB in the spectral range of 1500 nm - 2680 nm, and their broadband transmission is characterized. Tapers fabricated under an argon atmosphere are compared with those fabricated under ambient air, revealing differences in transmission loss and surface texture. Combinations of scanning electron microscopy, energy-dispersive spectroscopy, and x-ray photoelectron spectroscopy, coupled with XPS depth profiling, assign different roles to the implication of fluorine surface diffusion and reactions with adventitious oxygen, water, and carbonaceous deposits. A common denominator revealed by XPS is the interruption of the metastable ZrF$_{4}$network caused by elevated temperature processing. It is concluded that introducing a neutral blanket of inert gas to the fiber drawing oven yields optical fiber couplers with substantially reduced transmission losses.

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.258
Teacher spread0.245 · 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

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