MétaCan
Menu
Back to cohort
Record W4408603070 · doi:10.1117/12.3045819

Commercial laser power converters for power-over-fiber with Watts of output power capabilities over kilometers

2025· article· en· W4408603070 on OpenAlexaff
Simon Fafard, Denis Masson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsBroadcom (Canada)
Fundersnot available
KeywordsConvertersPower (physics)Electrical engineeringSwitched-mode power supplyFiber laserElectronic engineeringComputer scienceEngineeringOptical fiberTelecommunicationsPhysicsVoltage

Abstract

fetched live from OpenAlex

We demonstrate the use of laser diodes and multijunction photovoltaic power converters to efficiently deliver watts of electrical power for long-distance. Transmission through single-mode and multi-mode fibers at the wavelengths of 808nm and 1470/1550 nm are studied. An electrical output power of ~0.1W is obtained after a 5km transmission through a standard single-mode SMF28 fiber fed with 0.25W of optical power. An electrical output power of ~1W is demonstrated after a 5 km transmission with a standard OM1 multi-mode fiber fed with ~2.5W. Photovoltaic conversion efficiencies reaching Eff ~49% are obtained with an output voltage of ~5V using commercial multijunction laser power converters. Furthermore, for specialized applications, a cryogenic power-over-fiber system at ~1470nm is demonstrated with ~2W of electrical power converted over a 10m distance having a conversion efficiency of Eff > 65% at 77K.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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 routes1
Has abstractyes

Explore more

Same topicsolar cell performance optimizationFrench-language works237,207