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Record W4407017885 · doi:10.1364/oe.551978

Reconfigurable NIR-emission gain via thermal control in multimode tri-doped tellurite fiber

2025· article· en· W4407017885 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Express · 2025
Typearticle
Languageen
FieldEngineering
TopicSolid State Laser Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsOpticsMaterials scienceMulti-mode optical fiberDopingOptical fiberErbium doped fiber amplifierOptoelectronicsFiberFiber laserAmplified spontaneous emissionOptical amplifierLaserPhysics

Abstract

fetched live from OpenAlex

In fiber-based telecommunication systems, information is transmitted over wide optical spectrum bands. However, the volume of data, service demands, and traffic speeds keep rising, overloading the whole fiber links. Thus, enhancing efficiency and broadening transparency are essential in optical systems. Moreover, preserving gain across the broad spectrum remains a significant bottleneck to be overcome. Here we enhance intensity and broaden emission in the C-, L- and U- telecommunication bands via thermal population control in 30 cm of a tri-doped (Er 3+ -Tm 3+ -Yb 3+ ) multimode tellurite fiber (3Dp-MM-Te-fiber). The results indicate that increasing the temperature, a blue shift occurs around 1530 nm, and due to energy transfer among the tri rare-earths dopants a flat gain in the near-infrared region up to 1550 nm to 1650 nm is observed with a bandwidth of more than 100 nm. Furthermore, pumping the fiber at 980 nm with a 0.1 mW signal at 1550 nm, an experimental relative gain of approximately 2.5 dB was also demonstrated at a 120 o C heating temperature.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.862

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)0.0000.000

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 head0.009
GPT teacher head0.227
Teacher spread0.219 · 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