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Record W4401933855

Croissance épitaxiale de Germanium et de matériaux III-V sur des substrats de Germanium mésoporeux, pour des applications photovoltaïques

2024· dissertation· fr· W4401933855 on OpenAlexfundno aff
Nicolas Paupy

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2024
Typedissertation
Languagefr
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
FundersInstitut National des Sciences Appliquées de LyonCentre National de la Recherche ScientifiqueFonds de recherche du Québec – Nature et technologiesUniversité Grenoble AlpesNatural Sciences and Engineering Research Council of CanadaUniversité de SherbrookeMitacsIndian National Science Academy
KeywordsGermaniumMaterials scienceOptoelectronicsSilicon
DOInot available

Abstract

fetched live from OpenAlex

In recent years, our society has been facing a continuous growth in energy demand across various sectors such as transportation, telecommunications and more. To meet this demand and gradually replace the use of fossil fuels (oil, gas, coal, etc.), the renewable energy sector is increasingly in demand. Among the available renewable energies, photovoltaic energy has undergone numerous advancements, especially in the civil and space domain. Currently, the most efficient solar cells are the III-V multijunctions type solar cells, based on Germanium (Ge) and/or Gallium Arsenide (GaAs) substrates. With this type of cell, efficiencies exceeding 45% can now be achieved. However, their use is limited to niche markets such as space industry due to their high production costs. Ge is considered as a rare material, significantly impacting cell prices. Additionally, the substrates used typically have thicknesses ranging from 150 µm to 180 µm, while only a few micrometers would be necessary to maintain cell performances. This substantial thickness, representing about 95% of the total weight, poses a significant limitation in popularizing cells in sectors where weight reduction is a challenge, such as in space or automotive applications. Furthermore, these substrates cannot be reused, resulting in a substantial loss of materials. Therefore, it is essential to find a reliable, cost effective and industrially scalable approach to reduce the amount of material used and enable the reuse of the Ge substrate. In the context of this thesis, we optimized the homoepitaxy approach of Ge on a porous Germanium monolayer at low and high temperature, now known as PEELER approach. This innovative approach demonstrated the creation of an epitaxial growth template in the form of a detachable Ge membrane with monocrystalline quality and low surface roughness (< 1 nm), enabling the growth of high quality III-V materials. It will also be shown that the substrate can be reused multiple times to repeat the process. The parameters used for this demonstration arise from the study of the impact of various factors, such as the Ge growth temperature and the thickness of the mesoporous Ge layer, on the porous structure reorganization and the membrane adhesion strength. It was observed that increasing the growth temperature leads to more significant porous structure reorganization, resulting in an increased membrane adhesion strength. By varying the porous Ge layer thickness, it was demonstrated that a wide range of Ge membrane adhesion strength can be obtained, ranging from a detachable membrane with a simple adhesive strip, with a low adhesion strength, to a nondetachable membrane. This study has shown the possibility of controlling the adhesion strength of the Ge membrane, opening up the potential use of the PEELER approach in other domains beyond that of solar cells.

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: Other · Consensus signal: none
Teacher disagreement score0.002
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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