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Record W2102139604 · doi:10.1149/1.3568842

Nanobonding for Multi-Junction Solar Cells at Room Temperature

2011· article· en· W2102139604 on OpenAlexafffund
Tian Yu, Matiar R. Howlader, Fangfang Zhang, Mohamed H. Bakr

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsAnnealing (glass)Materials scienceAmorphous solidWaferFabricationOptoelectronicsThermal stabilitySiliconAmorphous siliconComposite materialChemical engineeringChemistryCrystalline siliconCrystallography

Abstract

fetched live from OpenAlex

Direct nanobonding of p-Si/n-GaAs wafers based on surface activation that uses an Argon (Ar)-fast atom beam at room temperature has been investigated. The bonding strength of the interface was 14.4 MPa at room temperature, and remained nearly constant after annealing up to 600 °C. An amorphous layer with a thickness of 11.5 nm was found across the interface without annealing. After annealing, the electrical current-voltage (I-V) characteristics were improved and the amorphous layer was diminished across the interface. The thermal stability of the interfacial properties of Si/GaAs indicates its potential use on the fabrication of multi-junction solar cells with Si substrate to lower the cost while improving the solar cells' efficiency. The thickness dependence of p-Si/n-Si interfacial I-V characteristics using COMSOL simulation indicates the decrease of breakdown voltage and current with the increase of the junction thickness.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.032
GPT teacher head0.208
Teacher spread0.176 · 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

Citations17
Published2011
Admission routes2
Has abstractyes

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