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Multiple-reuse of Ge Substrates: Towards Cost-effective and Sustainable III-V Solar Cells Fabrication

2023· article· en· W4390188889 on OpenAlexaff
Alexandre Chapotot, Bouraoui Ilahi, Tadeáš Hanuš, Gwénaëlle Hamon, Jinyoun Cho, Kristof Dessein, Christian Dubuc, Maxime Darnon, Abderraouf Boucherif

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologique
Fundersnot available
KeywordsReuseMaterials scienceWaferSolar cellSubstrate (aquarium)Surface roughnessPhotovoltaic systemMonocrystalline siliconNanotechnologyEpitaxyProcess engineeringFabricationOptoelectronicsLayer (electronics)SiliconWaste managementComposite materialEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

High efficiency solar cells based on III-V layers on Ge substrates are commonly used for space devices, but their high cost, partly due to Ge substrate, make them less suitable for terrestrial photovoltaic applications. The porous lift-off process offers a solution for detaching a thin, flexible, and lightweight cell and reusing the substrate, potentially making these solar cells more cost-effective. To date, only one reuse of the substrate has been proven. However, further examination of multiple reuse cycles is crucial in understanding the trends in the surface of the wafer over cycles. The HF-based wet chemistry employed in this study, allows for the substrate to be reused three times by removing the broken pillars that remain after each porous lift-off cycle. The reusability of the substrate is guaranteed due to the smooth and blemish-free surfaces with low RMS roughness achieved through reconditioning. Even after three porous lift-off cycles, the epitaxial layer remains monocrystalline with a roughness of only 3 nm, making it suitable for solar cell growth. These findings open up the possibility for a more affordable and sustainable method of producing high efficiency solar cells, reducing costs by a factor of 4 and Ge consumption by a factor of 19 compared to traditional manufacturing methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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