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Record W4285654775 · doi:10.1063/5.0099255

CPV module to rate antireflective and encapsulant coating in outdoor conditions

2022· article· en· W4285654775 on OpenAlexafffundabout
Arnaud Ritou, Philippe St-Pierre, P. O. Provost, Gavin P. Forcade, Christian Dubuc, O. Delléa, Gwénaëlle Hamon, Maïté Volatier, Abdelatif Jaouad, Christopher E. Valdivia, Karin Hinzer, Vincent Aimez, Maxime Darnon

Bibliographic record

VenueAIP conference proceedings · 2022
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsGeneral Electric (Canada)University of OttawaInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersÉcole Centrale de LyonInstitut National des Sciences Appliquées de LyonCentre National de la Recherche ScientifiqueFonds de recherche du Québec – Nature et technologiesUniversité Grenoble AlpesUniversité de SherbrookeMitacsIndian National Science Academy
KeywordsAnti-reflective coatingCoatingMaterials sciencePhotovoltaic systemOptoelectronicsSolar cellMoistureComposite materialElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Reflections are the most important channel losses in CPV modules. Since high efficiency solar cells need a protection against moisture and oxidation, we study an antireflective coating which also encapsulates the solar cells. It is based on a monolayer of microbeads partially submerged into PDMS. In this work, a CPV module is designed to compare the electrical performance of encapsulated and bare solar cells. A preliminary study demonstrates an increase in short- circuit current by 3.8% with EQE measurements and simulations. Outdoor measurements in Sherbrooke, Quebec, Canada gave a 6.4% increase in current for a 280X module on a clear cold day in September, after rejecting aberrant measurements, which confirms the interest of using microbeads as an antireflective coating for CPV applications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.235
Teacher spread0.219 · 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 designBench or experimental
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
Published2022
Admission routes3
Has abstractyes

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