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Record W2923222777 · doi:10.1364/ao.58.002505

Reducing shadowing losses in silicon solar cells using cellulose nanocrystal: polymer hybrid diffusers

2019· article· en· W2923222777 on OpenAlex
Qiwei Xu, Lingju Meng, Xihua Wang

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Optics · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates Bio Solutions
KeywordsMaterials scienceOptoelectronicsSiliconPolydimethylsiloxaneSolar cellPolymer solar cellCrystalline siliconOpticsDiffuser (optics)PolymerNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Gridline shadowing is one of the main factors affecting the performance of silicon solar cells. In this demonstration, a straightforward, scalable approach is reported to reduce shadowing losses from metallic contacts on silicon solar cells by employing cellulose nanocrystals (CNC) mixed in a polymer- polydimethylsiloxane. The method is highly compatible with current solar cell module manufacturing. The CNC:polymer (CNP) hybrid diffusers, offering highly efficient broadband light diffusion, are applied atop the metallization areas to deflect the light impinging on metallic gridlines toward uncovered active areas on the solar cell. Simulations showed that the CNP diffuser is an excellent candidate for reducing shadowing losses within a wide range of incident angles, as it can reduce more than 30% of shadowing losses at normal incidence, and nearly 50% of the lost light can be recycled at the incident angle of 60°. Taking advantage of reduced shadowing losses, a new 6-busbar technology based on the CNP diffusers is proposed with lower manufacturing complexity and higher overall efficiency.

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)0.000
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 score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.197
Teacher spread0.188 · 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