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Record W4409964861 · doi:10.3390/photonics12050431

Cooling Fiber Laser Power Converter Systems by Immersion in Oil

2025· article· en· W4409964861 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

VenuePhotonics · 2025
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsBroadcom (Canada)
Fundersnot available
KeywordsMaterials scienceImmersion (mathematics)Fiber laserOptoelectronicsLaserOptical fiberFiberOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

We demonstrate the use of Laser Power Converters (LPCs) driven by fiber laser light while immersed in transformer oil for heat management purposes. Reliability tests performed via extended continuous operation using 6–7 W of input power from 808 nm and 976 nm light propagating through oil show no degradation of components nor transmission losses from the oil for up to 1000 h. The operation of a bare die designed for use with 1040–1080 nm light and in direct contact with oil is also shown to be feasible. We discuss how the use of transformer oil can be beneficial to transfer excess heat away from LPCs in special applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.427

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 head0.003
GPT teacher head0.190
Teacher spread0.187 · 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