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Record W2335494215 · doi:10.1364/pv.2015.pth2b.1

Controlling disorder for improved light management in solar cells

2015· preprint· en· W2335494215 on OpenAlexaff
Guillaume Gomard, Donie Yidenekachew, Radwanul Hasan Siddique, Ruben Huenig, Hendrik Hölscher, Karsten Bittkau, Valérie Depauw, Romain Peretti, Xianqin Meng, Emmanuel Drouard, Christian Seassal, Uli Lemmer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhotonicsComputer scienceBroadbandPhotovoltaic systemLight scatteringBenchmarkingPolarization (electrochemistry)OptoelectronicsOmnidirectional antennaExploitPhotonic crystalMaterials scienceScatteringOpticsPhysicsTelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Due to their excellent light collection and light trapping capabilities, random structures are often used in photovoltaic applications for benchmarking periodical photonic systems whose optical properties can be easily predicted and adjusted. Recent studies reported that working at the interface between those two concepts enables to retain the deterministic trait of periodical structures while benefiting from the broadband, omnidirectional and polarization-independent characteristics found in random systems. This approach exploits the effects arising from controlled disorder. In this communication, various ways to intentionally introduce disorder at the different levels of thin-film solar cells will be discussed. Several aspects of this rich topic, including perturbed photonic crystals, scattering layers and bio-inspired light collection structures, will be covered.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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 teacher head, not a consensus.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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