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Record W2946739236 · doi:10.1002/smtd.201900111

Kinetically Favorable Vapor–Adsorbate–Solid Growth of Rutile Nanowires

2019· article· en· W2946739236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmall Methods · 2019
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsNanowireMaterials scienceVapor–liquid–solid methodRutileNanotechnologyOxideBimetallic stripChemical vapor depositionChemical engineeringMetalMetallurgy

Abstract

fetched live from OpenAlex

Abstract The vapor‐based synthesis of nanowires, particularly vapor–liquid–solid growth and its variants, is an undoubtedly promising method for fabricating high‐quality silicon‐based and group III–V semiconductors. However, the assembly of oxide nanowires has limited success while adopting these methods. Herein, a simple and scalable approach is developed to synthesize single‐crystal oxide nanowires with controllable morphology. Using titanium oxide as the model system for validation, this approach emphasizes the essential role of the surface characteristic of the seed for the space‐confined growth of nanowires. The shape and growth directions of nanowires can be additionally tailored by using bimetallic seeds, with compositional segregations and dissimilar surface characteristics. Since the source material has the same thermodynamic conditions as the produced nanowires within a closed vessel, the results suggest that the growth of nanowires can be dominated by the kinetic enrichment at seed surfaces or referred to as kinetically favorable vapor–adsorbate–solid growth. While identifying the key thermodynamic and kinetic parameters during growth, this approach is applicable for a wide range of 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.

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 score0.116
Threshold uncertainty score0.619

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 head0.015
GPT teacher head0.283
Teacher spread0.267 · 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