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Record W3113296019 · doi:10.3390/nano10122507

Direct Synthesis of Oxynitride Nanowires through Atmospheric Pressure Chemical Vapor Deposition

2020· article· en· W3113296019 on OpenAlexaff
Babak Adeli, Fariborz Taghipour

Bibliographic record

VenueNanomaterials · 2020
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceChemical vapor depositionNanowireFabricationAtmospheric pressureNanotechnologyNitrideOxideChemical engineering

Abstract

fetched live from OpenAlex

Binary and ternary oxynitride solid alloys were studied extensively in the past decade due to their wide spectrum of applications, as well as their peculiar characteristics when compared to their bulk counterparts. Direct bottom-up synthesis of one-dimensional oxynitrides through solution-based routes cannot be realized because nitridation strategies are limited to high-temperature solid-state ammonolysis. Further, the facile fabrication of oxynitride thin films through vapor phase strategies has remained extremely challenging due to the low vapor pressure of gaseous building blocks at atmospheric pressure. Here, we present a direct and scalable catalytic vapor-liquid-solid epitaxy (VLSE) route for the fabrication of oxynitride solid solution nanowires from their oxide precursors through enhancing the local mass transfer flux of vapor deposition. For the model oxynitride material, we investigated the fabrication of gallium nitride and zinc oxide oxynitride solid solution (GaN:ZnO) thin film. GaN:ZnO nanowires were synthesized directly at atmospheric pressure, unlike the methods reported in the literature, which involved multiple-step processing and/or vacuum operating conditions. Moreover, the dimensions (i.e., diameters and length) of the synthesized nanowires were tailored within a wide range.

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 score0.028
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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
Published2020
Admission routes1
Has abstractyes

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