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Record W4390561444 · doi:10.1002/solr.202470011

High‐Efficiency GaAs Solar Cells Grown on Porous Germanium Substrate with PEELER Technology

2024· article· en· W4390561444 on OpenAlexaff
Valentin Daniel, Thomas Bidaud, Jérémie Chretien, Nicolas Paupy, Ahmed Ayari, Thierno Mamoudou Diallo, Tadeáš Hanuš, Jonathan Henriques, Abdelatif Jaouad, Jean-François Lerat, Bouraoui Ilahi, Jinyoun Cho, Kristof Dessein, Christian Dubuc, Gwénaëlle Hamon, Abderraouf Boucherif, Maxime Darnon

Bibliographic record

VenueSolar RRL · 2024
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsGeneral Electric (Canada)Institut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsGermaniumMonocrystalline siliconWaferMaterials scienceEpitaxyOptoelectronicsSubstrate (aquarium)Chemical vapor depositionPhotovoltaic systemSolar cellNanotechnologyEngineering physicsSiliconElectrical engineeringLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

GaAs Solar Cells In article number 2300643, Valentin Daniel and co-workers have grown monocrystalline GaAs/Ge epitaxial layers on 100 mm (4") porosified germanium wafers using metal-organic chemical vapor deposition. The single-junction photovoltaic cells, fabricated through front-side processing on these structures, demonstrate efficiencies of up to 23.1%. This work paves the way for detachable III–V solar devices and reusable germanium wafers, for ecological and economic benefits.

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 score0.004

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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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