MétaCan
Menu
Back to cohort
Record W4385541961 · doi:10.1109/ectc51909.2023.00385

New Triple-Junction Solar Cell Assembly Process for Concentrator Photovoltaic Applications

2023· preprint· en· W4385541961 on OpenAlexafffund
Konan Kouame, Payam Haghparast, Pierre Albert, Artur Turala, Thomas Bidaud, Abdelatif Jaouad, David Danovitch, Gwénaëlle Hamon, Maïté Volatier, Vincent Aimez, Maxime Darnon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersCentre National de la Recherche ScientifiqueInstitut National des Sciences Appliquées de LyonUniversité Grenoble AlpesNatural Sciences and Engineering Research Council of CanadaUniversité de SherbrookeIndian National Science Academy
KeywordsMicroelectronicsConcentratorPhotovoltaic systemProcess (computing)Materials scienceSolar cellTriple junctionThermalSurface-mount technologyPrinted circuit boardMechanical engineeringComputer scienceOptoelectronicsElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

We developed a new assembly process to fabricate concentrator photovoltaic (CPV) modules based on microelectronic surface mount technologies (SMT). Functional characterizations of resultant modules demonstrated that the configuration and process did not degrade solar cell performance. Dimensional characterizations showed high placement accuracy. The thermal performance was compared to a standard CPV module using ANSYS finite element thermal simulations. The SMT based module was shown to dissipate heat more efficiently than the conventional module with a device temperature of 58±0.24°C compared to 69±0.34°C. This proof of concept demonstrates viability of the new assembly process with a strong potential to reduce CPV integration costs.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same topicsolar cell performance optimizationFrench-language works237,207