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Record W2902686475 · doi:10.1109/pvsc.2018.8547912

Opportunities for Increased Efficiency in Monochromatic Photovoltaic Light Conversion

2018· article· en· W2902686475 on OpenAlexaff
Daixi Xia, Matthew M. Wilkins, Sanmeet Chahal, Christopher E. Valdivia, Karin Hinzer, Jacob J. Krich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMonochromatic colorThermalisationEnergy conversion efficiencyPhotovoltaic systemMaterials scienceOptoelectronicsWavelengthOpticsReflection (computer programming)Intensity (physics)Solar cell efficiencyQuantum efficiencyPhysicsAtomic physicsSolar cellComputer scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

We study efficiency loss mechanisms and the potential for improvement of a record-setting monochromatic photovoltaic device, which has demonstrated efficiency of 70% at 830 nm. We combine experimental and modeling-based estimates of losses due to reflection, transmission, thermalization, and series resistance into an extended detailed-balance model that further includes losses due to nonradiative recombination, quantified by an external radiative efficiency (ERE). From the device's efficiency, we estimate ERE ≥ 10%. The device efficiency could be improved by increased material quality, operation at a longer wavelength closer to the GaAs band gap, and increased light intensity. Each order of magnitude increase in incident light intensity or material ERE increases the efficiency by 4% absolute. Redesigning the device to operate at 850 nm reduces thermalization losses by 2% absolute. Overall device efficiency over 80% could be attained by combining these opportunities.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.214
Teacher spread0.192 · 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
Published2018
Admission routes1
Has abstractyes

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