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Radiation-Balanced Silica Fiber Amplifier

2021· article· en· W3133741698 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Review Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical properties and cooling technologies in crystalline materials
Canadian institutionsUniversité Laval
FundersAir Force Office of Scientific Research
KeywordsMaterials scienceFiber Bragg gratingOpticsAmplifierCore (optical fiber)FiberOptical fiberOptoelectronicsSilica fiberMetrologyFiber laserPhysicsCMOS

Abstract

fetched live from OpenAlex

We report what we believe to be the first radiation-balanced fiber amplifier-a device that provides optical gain while experiencing no temperature rise. The gain medium is a silica fiber with a 21-μm-diameter core highly doped with Yb^{3+} (2.52 wt. %) and codoped with 2.00 wt. % Al to reduce concentration quenching. The amplifier is core pumped with 1040-nm light to create anti-Stokes fluorescence cooling and gain in the core at 1064 nm. Using a custom slow-light fiber Bragg grating sensor with mK resolution, temperature measurements are performed at multiple locations along the amplifier fiber. A 4.35-m fiber pumped with 2.62 W produced 17 dB of gain, while the average fiber temperature remained slightly below room temperature. This advancement is a fundamental step toward the creation of ultrastable lasers necessary to many applications, especially low-noise sensing and high-precision metrology.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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