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Record W1611606410 · doi:10.1002/pssr.201307142

Study of radial growth in patterned self‐catalyzed GaAs nanowire arrays by gas source molecular beam epitaxy

2013· article· en· W1611606410 on OpenAlexafffund
S. J. Gibson, Ray LaPierre

Bibliographic record

Venuephysica status solidi (RRL) - Rapid Research Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireMolecular beam epitaxyMaterials scienceGrowth rateSiliconNanotechnologyOxideGalliumOptoelectronicsEpitaxyLayer (electronics)Geometry

Abstract

fetched live from OpenAlex

Abstract magnified image Ordered arrays of vertically aligned self‐catalyzed GaAs nanowires have been grown by gas source molecular beam epitaxy (GS‐MBE) on silicon substrates using nano‐patterned oxide templates. Several growth processes of different duration were performed under identical conditions and with identical sample preparation. To determine the influence of pattern parameters, the samples were prepared with 20 patterned areas, each with progressively increasing hole diameters and pitch. Measurements of the average lengths and diameters of the vertically oriented nanowire areas were then used to calculate the overall axial and radial growth rates. These experiments confirm that significant accompanying radial growth occurs. Furthermore, the rate of radial growth increases with increasing pattern pitch. We propose that gallium‐rich conditions may increase the size of the liquid droplet, resulting in an inverse tapered morphology which promotes step‐flow radial growth via secondary adsorption on the nanowire sidewalls. The pitch dependence of the radial growth rate may therefore be due to shadowing or competition for the flux of material desorbing from the oxide surface between the nanowires. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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 categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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.

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

Citations22
Published2013
Admission routes2
Has abstractyes

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