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Record W2070174378 · doi:10.1063/1.3544486

Numerical model of current-voltage characteristics and efficiency of GaAs nanowire solar cells

2011· article· en· W2070174378 on OpenAlexafffund
Ray LaPierre

Bibliographic record

VenueJournal of Applied Physics · 2011
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
FundersOntario Centres of Excellence
KeywordsNanowirePassivationMaterials scienceOptoelectronicsSolar cellDiodeCurrent densitySemiconductorDopingTheory of solar cellsCarrier lifetimeEnergy conversion efficiencyFermi levelNanotechnologySolar cell efficiencySiliconLayer (electronics)ElectronPhysics

Abstract

fetched live from OpenAlex

Numerical simulation of current-voltage (J-V) characteristics of III-V nanowire core-shell p-n junction diodes under illuminated conditions is presented with an emphasis on optimizing the nanowire design for photoconversion efficiency. Surface recombination and depletion effects are found to play a dominant role in the J-V characteristics. The impact of surface charge density, surface recombination velocity, doping concentration, and nanowire geometry are investigated. Investigation of contacting methodology indicated that solar cell efficiency is degraded with electrical contacts on the sidewalls of the nanowire due to Fermi level pinning at the metal/semiconductor interface. On the other hand, contacts on the top of nanowires with sidewall passivation provide solar cell performance close to the detailed balance efficiency limit of ∼30%. Elimination of the thin film between nanowires produces a smaller dark current and improved cell performance.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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
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

Citations107
Published2011
Admission routes2
Has abstractyes

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