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Record W4401069112 · doi:10.1021/acsanm.4c02466

Selective Area HVPE of InGaAs Nanowires with Widely Tunable Composition on Si Substrates for Nanoscale Device Integration on Si Platforms

2024· article· en· W4401069112 on OpenAlexafffund
Emmanuel Chereau, В. Г. Дубровский, Ethan Diak, Elias Semlali, Geoffrey Avit, Agnès Trassoudaine, Evelyne Gil, Ray LaPierre, Yamina André

Bibliographic record

VenueACS Applied Nano Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
FundersEuropean CommissionNatural Sciences and Engineering Research Council of CanadaRégion Auvergne-Rhône-AlpesH2020 European Research CouncilSaint Petersburg State UniversityAgence Nationale de la Recherche
KeywordsNanowireMaterials scienceHydrideNanoscopic scaleOptoelectronicsHomogeneousPhase (matter)EpitaxyNanotechnologyChemical engineeringLayer (electronics)ChemistryMetal

Abstract

fetched live from OpenAlex

Nanowires (NWs) are promising for the integration of III–V compound-based electrical and optical devices on Si. Selective area growth (SAG) of In x Ga 1– x As NWs with compositions x varying from 0.34 to 0.90 is achieved. NWs are grown on patterned Si(111) substrates by hydride vapor phase epitaxy (HVPE) at temperatures around 690 °C. The composition is measured using EDX profiles along their lengths and found to be quite homogeneous. Theoretical analysis revealed that the NW composition is kinetically controlled and well-fit by the one-parametric Langmuir–McLean formula, with no thermodynamic factors influencing the compositional trend. This study highlights the capability of catalyst-free HVPE SAG to grow highly uniform InGaAs NW arrays with a widely tunable composition on Si substrates.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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