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Record W4385656662 · doi:10.1109/jphotov.2023.3295498

Outdoor Characterization of Solar Cells With Microstructured Antireflective Coating in a Concentrator Photovoltaic Monomodule

2023· article· en· W4385656662 on OpenAlexafffundabout
Arnaud Joël Kinfack Leoga, Arnaud Ritou, Mathieu Blanchard, Lysandre Dirand, Yanis Prunier, Philippe St‐Pierre, David Chuet, Philippe‐Olivier Provost, Maïté Volatier, Vincent Aimez, Gwénaëlle Hamon, Abdelatif Jaouad, Christian Dubuc, Maxime Darnon

Bibliographic record

VenueIEEE Journal of Photovoltaics · 2023
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsGeneral Electric (Canada)Institut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersInstitut 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 coatingPolydimethylsiloxaneMaterials scienceArc (geometry)PhotovoltaicsPhotovoltaic systemCoatingOptoelectronicsNanotechnologyMechanical engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Microstructured antireflective coatings (ARCs) have been identified as a promising solution to reduce optical losses in concentrator photovoltaics’ (CPV) modules. We fabricated and tested in field a CPV module made of four monomodules with a concentration factor of 250× that embed either solar cells with microstructured encapsulating ARC or solar cells with multilayer ARC as a reference. The microstructured encapsulating ARC was made of semiburied silica beads in polydimethylsiloxane. The module was in operation for one year in the severe climatic conditions of Sherbrooke, QC, Canada, before extracting the monomodules performance. Despite a suboptimal module design, we report a monomodule efficiency of 29.7% at 900 W/m2for a cell with microstructured encapsulating ARC. This proves the potential of microstructured encapsulating ARC to enable high-performance CPV systems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 science0.0000.000
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 designObservational
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

Citations8
Published2023
Admission routes3
Has abstractyes

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