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Epitaxial lift-off process for III-V solar cells by using porous germanium for substrate re-use

2020· preprint· en· W3120895830 on OpenAlexaff
Roxana Arvinte, Samuel Cailleaux, Alex Brice Poungoué Mbeunmi, Alexandre Heintz, Richard Arès, Abderraouf Boucherif

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceGermaniumAnnealing (glass)PorosityEpitaxyLift (data mining)Substrate (aquarium)SinteringOptoelectronicsKinetic energyLayer (electronics)SiliconChemical engineeringNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Germanium substrate re-use by porous sacrificial layer appears to be a promising approach for next generation high efficiency III- V solar cells. The morphological evolution of the double porous Ge layer upon ultra-high-vacuum annealing both experimentally and numerically was studied. We introduced the three-dimensional kinetic Monte Carlo model based on thermally activated jumps of atoms to simulate the porous Ge layer evolution during high temperature annealing driven by minimization of the total surface energy. It was demonstrated that the simulated sintering of double layer of randomly distributed Ge pores concurs quantitatively with experimental findings.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.056
GPT teacher head0.275
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 teacher head, not a consensus.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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