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Record W2565337900 · doi:10.1088/2053-1591/3/12/125023

Production and characterization of a novel carbon nanotube/titanium nitride nanocomposite

2016· article· en· W2565337900 on OpenAlexafffund
Carole E. Baddour, Kaushik Das, Srikar Vengallatore, Jean‐Luc Meunier

Bibliographic record

VenueMaterials Research Express · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanocompositeNanoindentationWettingTinCarbon nanotubeTitanium nitrideCoatingComposite materialContact angleChemical vapor depositionTitaniumNitrideNanotechnologyMetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

A novel titanium nitride (TiN)/carbon nanotube (CNT) nanocomposite is produced with the purpose to mechanically, structurally and chemically stabilize a 'felt-like' CNT growth structure. The CNTs are grown on stainless steel (SS) 304 by chemical vapor deposition using the direct growth method previously developed, which does not require the use of an additional catalyst precursor. The TiN coating is achieved by physical vapor deposition and is shown here to generate a nanocomposite with a porous three-dimensional architecture. The contact stiffness is evaluated using nanoindentation, and wetting properties of the TiN/CNT nanocomposites are determined from contact angle measurements. An increase in contact stiffness and effective elastic modulus with TiN coating time was observed. The TiN coating on the non-wetting CNT felt results in a wetting nanocomposite surface. The wetting property is found to be a function of the TiN coating thickness on the CNT structure.

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)0.000
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 score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.265
Teacher spread0.228 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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