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Designing High-Efficiency Thin Silicon Solar Cells Using Parabolic-Pore Photonic Crystals

2018· article· en· W2795542312 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Review Applied · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Toronto
FundersBasic Energy SciencesU.S. Department of Energy
KeywordsMaterials scienceOptoelectronicsSiliconPhotovoltaicsEnergy conversion efficiencyPlasmonic solar cellSolar cellPhotovoltaic systemQuantum dot solar cellThin filmPhotonicsPolymer solar cellNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

A flexible thin-film silicon solar cell with power conversion efficiency approaching 30% would be a game-changer for the photovoltaics industry. This dream has been considered unattainable, though, due to Si's indirect band gap. Also, silicon solar cells are typically thick, inflexible, and limited in efficiency by nonradiative charge-carrier losses in the large bulk volume of the cell. This research demonstrates how light trapping based on wave interference in photonic crystals could raise conversion efficiency to ~28%, over a large wavelength range of 300---1100 nm. This would set a new record for silicon-based photovoltaic technology.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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