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Record W2009608495 · doi:10.5539/mas.v3n7p73

Gallium Arsenide Nanowires Formed by Au-assisted Metal-organic Chemical Vapor Deposition: Effect of Growth Temperature

2009· article· en· W2009608495 on OpenAlexvenueno aff
Rosnita Muhammad, Zulkafli Othaman, Yussof Wahab, Samsudi Sakrani, Waleed Ahmad, Mohd Nazri

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsnot available
FundersMinisterio de Ciencia, Tecnología y Medio Ambiente
KeywordsNanowireMaterials scienceMetalorganic vapour phase epitaxyEutectic systemScanning electron microscopeSubstrate (aquarium)Chemical vapor depositionVapor–liquid–solid methodChemical engineeringGallium arsenideEvaporationDeposition (geology)NanotechnologyGalliumAlloyLayer (electronics)EpitaxyComposite materialOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

We have investigated the growth of gallium arsenide (GaAs) nanowires as a function of temperatures in metal organic chemical vapor deposition (MOCVD) to establish the mechanisms that govern wire growth and to optimize growth conditions. The growth follows the vapor-liquid-solid method by applying nanoparticle gold colloid as a catalyst to forms a eutectic liquid alloy with the substrate. The semi insulating undoped (111)B GaAs was first dipped in the poly-L-lysine solution before 30nm gold colloid dropped on the substrate surface. Growth process in the MOCVD system were done at temperatures between 380 and 600ºC with growth time set is 30 min. All the grown samples were analyzed using a field emmission scanning electron microscope (FE-SEM) and scanning electron microscopy (SEM). With increasing temperature the nanowire height increases but leads to significant tapering of the nanowire due to competing growth at the (111) substrate surface. At low temperatures nanowires grown are cylindrical-shaped with diameter wires between 50 and 100 nm.

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 score0.021
Threshold uncertainty score0.759

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)0.0000.000

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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