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Record W2940180139 · doi:10.1364/oe.27.00a543

Higher efficiency tandem solar cells through composite-cell current matching

2019· article· en· W2940180139 on OpenAlexafffund
Ronan Garrison, R. N. Kleiman

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTandemSolar cellMaterials scienceBand gapMatching (statistics)SiliconSolar cell efficiencyComposite numberOptoelectronicsCurrent (fluid)Energy conversion efficiencyOpticsComputer scienceElectrical engineeringPhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

We expand in detail on a new approach to current matching in double junction solar cells that increases the theoretical maximum efficiencies attainable for many bandgap pairs. In this approach, either or both cell types are repeated one or more times, which provides for improved current matching and 2-terminal operation for a wide variety of bandgap pairs, opening up the opportunity to utilize materials not previously considered. While a multijunction design in which the bandgap of every cell is fully optimized will have higher efficiency, this approach achieves simplicity and potential cost savings by using only two cell types. Of particular interest are tandem cells with silicon as the base cell, where significant improvements in efficiency can be achieved with composite-cell current matching. This is illustrated for a 2.19 eV/Si(3) device with a theoretical maximum efficiency of 42.9%, well in excess of the 27.7% achievable for a 2.19 eV/Si device. The benefits of utilizing composite-cell stacks in Si-based triple-junction devices are also discussed.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.215
Teacher spread0.205 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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