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Record W3086656774 · doi:10.1364/cleo_at.2020.af3n.4

Solar powered fiber laser for energy conversion applications

2020· article· en· W3086656774 on OpenAlexaff
Taizo Masuda, Stephan Dottermusch, Ian A. Howard, Bryce S. Richards, Jean‐François Bisson, Masamori Endo

Bibliographic record

VenueConference on Lasers and Electro-Optics · 2020
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsLasing thresholdMaterials scienceOptoelectronicsTransmittanceOpticsLaserConcentratorSolar energyEnergy conversion efficiencyFiber laserPhysicsElectrical engineeringWavelength

Abstract

fetched live from OpenAlex

We demonstrate a solar-powered laser (SPL) that does not rely on any concentrator optics or solar tracking system. We believe that the utility of such “unconcentrated” SPLs has considerably increased for energy conversion applications. The proposed SPL consists of a Nd3+-doped fiber as an active medium, a liquid sensitizer, and a housing in which the transmittance of the front window is tailored to work as a luminescent solar collector. The very simple structure of the proposed SPL can be mass-produced easily at a low cost. The lasing threshold is 0.07 W/cm2 (70% of natural sunlight) which is four orders of magnitude smaller than those of conventional SPLs, and the output power at the pumping intensity of 0.1 W/cm2 is 0.25 mW. The SPL exhibits several lasing peaks between 1095 and 1105 nm.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.200
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

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
Published2020
Admission routes1
Has abstractyes

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