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Record W2081779515 · doi:10.1021/cg900313b

Hierarchical Al<sub>2</sub>O<sub>3</sub>Nanobelts and Nanowires: Morphology Control and Growth Mechanism

2009· article· en· W2081779515 on OpenAlexafffund
Yong Zhang, Ruying Li, Xiaorong Zhou, Mei Cai, Xueliang Sun

Bibliographic record

VenueCrystal Growth & Design · 2009
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireTransmission electron microscopyScanning electron microscopeNanotechnologyMaterials scienceFabricationVapor–liquid–solid methodNanostructureHigh-resolution transmission electron microscopySelected area diffractionChemical engineeringMorphology (biology)SpectroscopyEnergy-dispersive X-ray spectroscopyComposite material

Abstract

fetched live from OpenAlex

We report here a tunable synthesis of single crystalline hierarchical α-Al 2 O 3 nanobelts and nanowires by selectively applying a vapor−liquid−solid (VLS) and vapor−solid (VS) strategy in a chemical vapor process. The resultant nanostructures were characterized by scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, energy dispersive X-ray spectroscopy, and X-ray powder diffraction. The hierarchical nanobelts were generated by a noncatalytic oriented growth of Al 2 O 3 vertical to the {110} planes enclosed with {001} and {100} planes following a VS mode. The hierarchical nanowires were obtained through a catalytic growth in a VLS process. This well-controlled synthesis strategy is expected to be applicable to fabrication of other hierarchical nanobelts or 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.193
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2009
Admission routes2
Has abstractyes

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