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Record W2944239701 · doi:10.1109/tns.2019.2916027

III–V Laser Power Converters With Vertically Stacked Subcells Demonstrating Superior Radiation Resilience

2019· article· en· W2944239701 on OpenAlexafffund
M. C. A. York, Francine Proulx, O. Gilard, Laurent Béchou, Richard Arès, Vincent Aimez, Denis Masson, Simon Fafard

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2019
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueBroadcom (Canada)Université de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersRadiationLaserOptoelectronicsPower (physics)Materials sciencePhotovoltaic systemHeterojunctionReduction (mathematics)PhysicsResilience (materials science)Radiative transferDegradation (telecommunications)PhotonOpticsElectrical engineeringComputer scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Photovoltaic cells are detrimentally impacted by exposure to ionizing radiation, a consideration of particular significance for applications in outer space. In this paper, we demonstrate that vertical epitaxial heterostructure architecture (VEHSA) laser power converters are particularly resilient toward radiation-induced degradation; in particular, we observed reductions in efficiency of 1.9% and 6.4% for 5- and 20-junction monolithic devices, respectively, at near 3 W of input power (Vocof 5.78 and 23.32 V and peak response near 850 nm). This contrasts markedly with the 16.9% and 25.4% reductions in efficiency for the same 5- and 20-junction devices when using a detuned source at 808 nm, which we attribute to a reduction in nonradiative recombination lifetimes leading to a suppression of radiative recombination driven photon recycling.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.173
Teacher spread0.170 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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