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Record W2057637666 · doi:10.1063/1.4889801

Modelling of InGaP nanowires morphology and composition on molecular beam epitaxy growth conditions

2014· article· en· W2057637666 on OpenAlexaff
Ahmed Fakhr, Yaser M. Haddara

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNanowireTernary operationMaterials scienceGrowth rateMolecular beam epitaxyBinary numberSemiconductorMorphology (biology)NanotechnologyChemical physicsEpitaxyOptoelectronicsChemistryLayer (electronics)MathematicsGeometry

Abstract

fetched live from OpenAlex

An analytical kinetic model has been developed within this framework to describe the growth of ternary III-V semiconductor nanowires. The key to apply the model is to divide the ternary system into two separate binary systems and model each binary system separately. The model is used to describe the growth of InGaP nanowires. The growth conditions were varied among several samples, and the model was able to predict the temperature and growth rate behaviors. The model predicts the axial and radial elemental distribution along the nanowires and the dependence of the elemental distribution on the nanowire's diameter size for all growth rates. The model reveals the limitations of In incorporation into the nanowires for high temperatures or low growth rates and the effects of the group-V elements on the In incorporation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.356

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 head0.010
GPT teacher head0.204
Teacher spread0.195 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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