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Record W2291160733 · doi:10.1116/1.4939754

Design optimizations of InGaAsN(Sb) subcells for concentrator photovoltaic systems

2016· article· en· W2291160733 on OpenAlexafffund
Ross Cheriton, Matthew M. Wilkins, Pratibha Sharma, Christopher E. Valdivia, Anna H. Trojnar, Henry Schriemer, Karin Hinzer, James A. Gupta, Boussairi Bouzazi, Gitanjali Kolhatkar, Abderraouf Boucherif, Abdelatif Jaouad, S. Fafard, Vincent Aimez, Richard Arès

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2016
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversité de SherbrookeNational Research Council CanadaInstitut interdisciplinaire d'innovation technologiqueUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of ExcellenceNational Science CouncilCMC Microsystems
KeywordsMaterials scienceOptoelectronicsPhotovoltaic systemGermaniumEnergy conversion efficiencySolar cellConcentratorLattice (music)SiliconOpticsElectrical engineering

Abstract

fetched live from OpenAlex

The InGaAsN(Sb) material system is an attractive candidate for use in lattice-matched four-junction (4J) solar cells based on germanium substrates. Design optimizations for an InGaAsN(Sb) subcell are proposed for optimal power conversion efficiency within a 4J solar cell under a highly concentrated AM1.5D solar spectrum. The performance of the subcell is modeled using drift-diffusion simulations using Crosslight Apsys. An InGaAsN(Sb) test subcell was fabricated to obtain realistic materials parameters for the optimization of subcell performance. A thin InGaAsN(Sb) subcell is suggested for operation at 1000 Sun illumination intensities at low carrier lifetimes and mobilities.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0010.000
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 head0.013
GPT teacher head0.203
Teacher spread0.189 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2016
Admission routes2
Has abstractyes

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Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicsolar cell performance optimizationFrench-language works237,207