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Record W2366541818

Research of high-power multi-mode Er~(3+)/Yb~(3+) co-doped double-cladding optical fiber lasers

2006· article· en· W2366541818 on OpenAlexaboutno aff
Shufu Dong, Guofu Chen

Bibliographic record

VenueLaser Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceFiber laserOpticsCladding (metalworking)Fiber Bragg gratingDouble-clad fiberDispersion-shifted fiberYtterbiumPlastic-clad silica fiberPlastic optical fiberPolarization-maintaining optical fiberLaserOptoelectronicsFiberDopingFiber optic sensorPhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

In order to achieve much higher output power to satisfy the application requirements,the performances of erbium/ytterbium co-doped double-cladding fiber lasers(EY-DCFLs) are studied experimentally and theoretically.By using the EY805 model erbium/ytterbium co-doped multi-mode double-cladding fiber manufactured by INO,Canada as the gain medium,it is described that the output power is a function of the input pump power and fiber length.The maximum output power is about 3.5W by using a 1.8m fiber,with an optical to optical conversion efficiency of 31.8%.Numerical analysis of this EY-DCFL is also performed based on the rate and propagation equations.The calculated output powers of about 4.4W and conversion efficiency of 40% for the same fiber are a little larger than the experimental results.Then further optimization measures of the EY-DCFL are discussed,such as preparing of the fiber-end well,adding dichroic mirror at the output end and making fiber Bragg gratings directly in the fiber core.The above results are very important for the utilization and performance improvement of EY-DCFL.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.297
Teacher spread0.274 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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