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Optimal Band Gaps for Non-Ideal Intermediate Band Solar Cells

2020· article· en· W3120733905 on OpenAlexafffund
Matthew M. Wilkins, Jacob J. Krich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversity of Ottawa
FundersCompute Canada
KeywordsBand gapRadiative transferPhysicsLimit (mathematics)OptoelectronicsTopology (electrical circuits)OpticsMathematicsCombinatoricsMathematical analysis

Abstract

fetched live from OpenAlex

A detailed balance model for intermediate-band solar cells (IBSCs) has been adapted to include non-radiative recombination. We search for the band structure which would allow for a >30% efficient IBSC with minimum required material quality. With 1-sun AM1.5G illumination, a band gap Eg=2.4 eV with an intermediate band located at 0.6 Egyields 30% efficiency with material quality well below the radiative limit. This optimum band gap is 0.1 eV larger than the 2.3 eV found from optimization in the radiative limit. We find that materials with Egin the range of 2.2 to 3.0 eV offer the best opportunity to demonstrate efficient IBSCs. For lower quality material, larger band gaps compensate for loss of open-circuit voltage.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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