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Record W1983689693 · doi:10.1063/1.4794741

Growth of randomly oriented single-crystalline tin (IV) oxide nanobelts: Control on the predominant crystalline growth axis

2013· article· en· W1983689693 on OpenAlexaff
Samad Bazargan, K. T. Leung

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceTransmission electron microscopyNanorodTinTin oxideCrystal growthNanotechnologyChemical engineeringCrystallographyScanning electron microscopeOxideSubstrate (aquarium)ChemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

One-dimensional nanobelts of tin (IV) oxide are synthesized by using a newly introduced catalyst-assisted pulsed laser deposition method along two growth directions of [010] and [101]. An ex situ mask-induced growth gradient technique is employed to investigate the growth evolution of the nanobelts on oxidized-Si, H-terminated Si, and Al2O3(0001) substrates by helium ion microscopy, which reveals four stages of growth including catalyst detachment, horizontal nanorod growth, deflection, and the final nanobelt growth. X-ray diffraction and transmission electron microscopy studies show that in spite of the deflections and changes in the growth direction, the nanobelts have, remarkably, maintained their single-crystalline structure throughout the growth by only changing their crystalline growth axis. This has enabled us to influence the preferred growth axis by establishing a crystalline relation between the nanobelts and an appropriate substrate that pins the nanobelts in the initial growth stage. This growth control provides an important means to selectively promote growth of the predominant side planes of the nanobelts, which can then be separated for appropriate applications based on the different growth kinetics of [010] and [101] growth direction.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.200
Teacher spread0.186 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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