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Record W2386243393 · doi:10.1134/s1063782616040205

Simulation of the real efficiencies of high-efficiency silicon solar cells

2016· article· en· W2386243393 on OpenAlexaff
A. I. Skrebtii, R.М. Korkishko, V. P. Kostylyov, М. P. Kulish, I.O. Sokolovskyi

Bibliographic record

VenueSemiconductors · 2016
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPhotocurrentSiliconSolar cell efficiencyTheory of solar cellsMaterials scienceSolar cellRadiative transferOpen-circuit voltageEnergy conversion efficiencyTemperature coefficientOptoelectronicsVoltageOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

The temperature dependences of the efficiency η of high-efficiency solar cells based on silicon are calculated. It is shown that the temperature coefficient of decreasing η with increasing temperature decreases as the surface recombination rate decreases. The photoconversion efficiency of high-efficiency silicon-based solar cells operating under natural (field) conditions is simulated. Their operating temperature is determined self-consistently by simultaneously solving the photocurrent, photovoltage, and energy-balance equations. Radiative and convective cooling mechanisms are taken into account. It is shown that the operating temperature of solar cells is higher than the ambient temperature even at very high convection coefficients (~300 W/m 2 K). Accordingly, the photoconversion efficiency in this case is lower than when the temperature of the solar cells is equal to the ambient temperature. The calculated dependences for the open-circuit voltage and the photoconversion efficiency of high-quality silicon solar cells under concentrated illumination are discussed taking into account the actual temperature of the solar cells.

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 score0.006
Threshold uncertainty score0.011

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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