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Record W3136517886 · doi:10.1049/ote2.12036

Absorption enhanced thin‐film solar cells using fractal nano‐structures

2021· article· en· W3136517886 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

VenueIET Optoelectronics · 2021
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceSurface plasmonFractalWavelengthOptoelectronicsOpticsSurface plasmon polaritonAbsorption (acoustics)Solar cellPlasmonSiliconAbsorptancePlasmonic solar cellRayNano-Short circuitThin filmNanotechnologyMonocrystalline siliconPhysicsReflectivity

Abstract

fetched live from OpenAlex

Abstract In this article, a new structure for development of thin film solar cells is proposed in which elements with fractal shapes are integrated inside the cell to enhance its performance in a wide range of wavelengths. Two different structures are studied. In the first structure, a metallic fractal nano‐carpet is integrated inside the silicon layer in order to trap and absorb sunlight by exciting surface plasmon polaritons and local surface plasmons at different wavelengths. Numerical analysis shows that this technique increases the short circuit current provided by the cell by a factor of 2.40 for both TM and TE polarisations of the incident light. The second structure has an active layer shaped as a fractal structure, and absorbs sunlight through Mie and Fabry‐Perot resonances occurring at different wavelengths. The short circuit current enhancement for this structure is 2.97 for both TM and TE polarisations of the incident light, representing a significant improvement when compared with the previous works.

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 score0.075
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 integrity0.0000.001
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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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