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Record W4391527198 · doi:10.1088/1361-6528/ad263a

Long indium-rich InGaAs nanowires by SAG-HVPE

2024· article· en· W4391527198 on OpenAlexafffund
Emmanuel Chereau, Gabin Grégoire, Geoffrey Avit, T. Taliercio, Philipp Staudinger, Heinz Schmid, Catherine Bougerol, Agnès Trassoudaine, Evelyne Gil, Ray LaPierre, Yamina André

Bibliographic record

VenueNanotechnology · 2024
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
FundersHorizon 2020 Framework ProgrammeRégion Auvergne-Rhône-AlpesAgence Nationale de la RechercheCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsMaterials scienceWurtzite crystal structureNanowireIndiumTransmission electron microscopyPhotoluminescenceEpitaxyStackingFacetingOptoelectronicsSpectroscopyNanotechnologyCrystallographyZincLayer (electronics)Metallurgy

Abstract

fetched live from OpenAlex

Abstract We demonstrate the selective area growth of InGaAs nanowires (NWs) on GaAs (111)B substrates using hydride vapor phase epitaxy (HVPE). A high growth rate of more than 50 μ m h −1 and high aspect ratio NWs were obtained. Composition along the NWs was investigated by energy dispersive x-ray spectroscopy giving an average indium composition of 84%. This is consistent with the composition of 78% estimated from the photoluminescence spectrum of the NWs. Crystal structure analysis of the NWs by transmission electron microscopy indicated random stacking faults related to zinc-blende/wurtzite polytypism. This work demonstrates the ability of HVPE for growing high aspect ratio InGaAs NW arrays.

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 score0.393
Threshold uncertainty score0.885

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)0.0000.001

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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