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Record W1973365633 · doi:10.1109/jphotov.2014.2322279

Carrier Dynamics in Quantum-Dot Multijunction Solar Cells Under Concentration

2014· article· en· W1973365633 on OpenAlexafffund
Alexandre W. Walker, Olivier Thériault, Karin Hinzer

Bibliographic record

VenueIEEE Journal of Photovoltaics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Ottawa
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC MicrosystemsUniversity of Ottawa
KeywordsSuns in alchemyWetting layerQuantum dotMaterials scienceOptoelectronicsGallium arsenideCarrier lifetimeQuantum efficiencyIndium arsenideWettingSilicon

Abstract

fetched live from OpenAlex

The key performance metrics of quantum-dot (QD)- lattice-matched multijunction solar cells (MJSCs) composed of InGaP/(In)GaAs/Ge with InAs/GaAs QDs are explored under high-concentration illumination with a focus on the carrier dynamics in the QD layers of the middle subcell. An effective medium approach is used to describe generation and recombination in the QD system, including carrier escape and capture from the weakly confining quantum well and the QD states. At a concentration of 1000 suns, simulations indicate that the specific QD MJSC studied outperforms a standard MJSC by 1.1% in relative efficiency operating at 25 °C. However, this gain in efficiency is highly dependent on the confinement potentials of the wetting layer, as well as the resulting current mismatch between the top and middle subcells when carrier escape rates from within the wetting layer confinements are reduced.

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 score0.010

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 communication0.0010.001
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 head0.011
GPT teacher head0.242
Teacher spread0.232 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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