MétaCan
Menu
Back to cohort

Multi-Millijoule Nanosecond Fiber Amplifier at 2.8 μm

2025· article· en· W4413456191 on OpenAlexaff
Martin Bernier, Quentin Perry-Auger, Stanislav O. Leonov, Daiying Zhang, Yiğit Ozan Aydın, Darren Kraemer, Réal Vallée

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsToronto East General HospitalGenia Photonics (Canada)
Fundersnot available
KeywordsNanosecondMaterials scienceFiber laserFiber amplifierAmplifierOpticsFiberOptoelectronicsLaserPhysicsCMOSComposite material

Abstract

fetched live from OpenAlex

High-energy, short-pulse laser sources operating near water absorption peak <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(2.94 \mu \mathrm{m})$</tex> has crucial importance for many applications especially in biomedicine. This significance comes from high water content of biomedical tissues which allows such lasers to take full advantage of the strong water absorption near this wavelength. The use of high-energy pulses near this peak enhances ablation efficiency, enables penetration into dense or hard materials such as bone and calcified tissues and facilitates faster, cleaner process by minimizing residue and debris. The ideal duration of pulses for minimizing thermal effects in biomaterials needs to be shorter than the thermal relaxation time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(10 \mu \mathrm{s})$</tex> and comparable to or shorter than the thermoelastic stress propagation time, which is around 1 ns [1], [2]. The laser operation at this short pulse duration and near water absorption peak is output energy limited to sub-mJ level and one of the ideal approaches is to use fluoride fiber amplifiers to boost the limited energy of such systems to multi-millijoule levels.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
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)0.0020.001

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 teacher head, not a consensus.

Study designNot applicable
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 routes1
Has abstractyes

Explore more

Same topicPhotonic Crystal and Fiber OpticsFrench-language works237,207