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Record W2011814955 · doi:10.1088/0957-4484/20/2/025602

Synthesis and characterization of TiO<sub><i>x</i></sub>nanowires using a novel silicon oxide support layer

2008· article· en· W2011814955 on OpenAlexaff
M.Y. Lau, Letian Dai, Ken Bosnick, Stéphane Evoy

Bibliographic record

VenueNanotechnology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceNanowireRutileAuger electron spectroscopyTitaniumVapor–liquid–solid methodTransmission electron microscopySiliconLayer (electronics)Chemical engineeringSilicon oxideArgonOxideTitanium oxideAnalytical Chemistry (journal)NanotechnologyMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Titanium oxide (TiO(x)) nanowires of various compositions were synthesized using a vapour-liquid-solid growth process without the need of a titanium-based support layer. The process utilized liquid gold droplets, evaporated titanium vapours, and an argon-5% oxygen gas mixture at 1100 degrees C. A thin layer of thermally grown silicon oxide was used as the support layer. The resulting nanowires were 80-100 nm in diameter with lengths varying from 0.5 to 10 microm. Nanowires of TiO, as opposed to TiO(2), were formed in low pressures of argon-oxygen. Environmental pressure was found to greatly affect nanowire quality. Surface abrasion did not contribute to or inhibit the overall growth process. X-ray photon spectroscopy and Auger electron spectroscopy confirmed the presence of titanium. X-ray diffraction and transmission electron microscopy electron diffraction analysis confirmed the presence of rutile and face centred cubic crystal structures in both materials.

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 score0.001
Threshold uncertainty score0.453

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 head0.033
GPT teacher head0.229
Teacher spread0.196 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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