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Record W4414917067 · doi:10.3390/photonics12100988

Mid-Infrared Fiber Amplification of a DFB Interband Cascade Laser

2025· article· en· W4414917067 on OpenAlexafffund
Louis-Charles Michaud, Tommy Boilard, Réal Vallée, Martin Bernier

Bibliographic record

VenuePhotonics · 2025
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundCanada Foundation for Innovation
KeywordsLaser linewidthCascadeFiber laserLaserPower (physics)FiberDistributed feedback laserLaser power scaling

Abstract

fetched live from OpenAlex

The limited availability of powerful, tunable, and reliable mid-infrared sources has historically prevented their widespread adoption in spectroscopy applications, even if most greenhouse gases’ fundamental absorption lines are found in this region of the electromagnetic spectrum. However, both mid-infrared fiber lasers and ICLs have enjoyed substantial growth in available output powers in recent years. Since the two technologies have complementary benefits, combining them could prove to be an interesting avenue to explore toward the development of a powerful, easily tunable, and narrow linewidth mid-infrared source. We report what we believe to be the first demonstration of fiber amplification of a distributed feedback interband cascade laser (DFB-ICL) operating in the mid-infrared. The system, based on an in-band pumped dysprosium-doped fluoride fiber amplifier, yields 10 dB of gain and up to 30 mW of output power at 3240 nm. We believe this is an important milestone toward power scaling of single-mode, single-frequency, and rapidly tunable mid-infrared laser sources suitable for advanced gas spectroscopy.

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 categoriesInsufficient payload (model declined to judge)
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 score0.270
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 head0.009
GPT teacher head0.274
Teacher spread0.264 · 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.

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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