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Record W4393128287 · doi:10.1088/1361-6528/ad3741

Circumventing the ammonia-related growth suppression for obtaining regular GaN nanowires by HVPE

2024· article· en· W4393128287 on OpenAlexfundno aff
Elias Semlali, Geoffrey Avit, Yamina André, Evelyne Gil, А. В. Москаленко, Philip A. Shields, В. Г. Дубровский, Andréa Cattoni, Jean‐Christophe Harmand, Agnès Trassoudaine

Bibliographic record

VenueNanotechnology · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
FundersEuropean Research CouncilEngineering and Physical Sciences Research CouncilHorizon 2020 Framework ProgrammeLabEx GANEXH2020 European Research CouncilSaint Petersburg State UniversityAgence Nationale de la RechercheNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsMaterials scienceNanowireSapphireVapor phaseNanocrystalAmmoniaGrowth rateEpitaxyHydridePhase (matter)Chemical vapor depositionOptoelectronicsChemical engineeringTemplateNanotechnologyChemical physicsThermodynamicsOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Selective area growth by hydride vapor phase epitaxy of GaN nanostructures with different shapes was investigated versus the deposition conditions including temperature and ammonia flux. Growth experiments were carried out on templates of GaN on sapphire masked with SiN x . We discuss two occurrences related to axial and radial growth of GaN nanowires. A growth suppression phenomenon was observed under certain conditions, which was circumvented by applying the cyclic growth mode. A theoretical model involving inhibiting species was developed to understand the growth suppression phenomenon on the masked substrates. Various morphologies of GaN nanocrystals were obtained by controlling the competition between the growth and blocking mechanisms as a function of the temperature and vapor phase composition. The optimal growth conditions were revealed for obtaining regular arrays of ∼5 μ m long GaN nanowires.

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 score0.079
Threshold uncertainty score0.549

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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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
Published2024
Admission routes1
Has abstractyes

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