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Record W2941962078 · doi:10.1364/ol.44.002338

Demonstration of anti-Stokes cooling in Yb-doped ZBLAN fibers at atmospheric pressure

2019· article· en· W2941962078 on OpenAlexaff
Jennifer M. Knall, Arushi Arora, Martin Bernier, Solenn Cozic, Michel J. F. Digonnet

Bibliographic record

VenueOptics Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical properties and cooling technologies in crystalline materials
Canadian institutionsUniversité Laval
FundersAir Force Office of Scientific Research
KeywordsZBLANMaterials scienceOpticsMulti-mode optical fiberFiber Bragg gratingOptical fiberFiberFiber laserAtmospheric pressureOptoelectronicsMeteorologyPhysicsComposite material

Abstract

fetched live from OpenAlex

For the first time, to the best of our knowledge, optical cooling is demonstrated in a fiber at atmospheric pressure. Using a specialized slow-light fiber Bragg grating temperature sensor, −5.2 mK and −0.65 K were measured in a single-mode (1% YbF3) and multimode (3% YbF3) ZBLAN fiber with respective cooling efficiencies of 2.2% and 0.90%. Fitting a recently reported quantitative model of optical cooling in fibers to the measured temperature change dependence on the pump power per unit length validates the model and allows us to infer the fibers’ absorptive loss and quenching lifetime, key parameters that are scarce in literature. These values are necessary for accurate cooling predictions and will aid in the development of fibers for application in optical coolers and radiation-balanced lasers.

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 score0.003
Threshold uncertainty score0.006

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)0.0020.000

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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