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Microstructure and Adhesion Properties of a-CN and Ti/a-CN Nanocomposite Thin Films Prepared by Hybrid Ion Beam Deposition Technique

2014· article· en· W2009320556 on OpenAlexaff
P. Vijai Bharathy, Q. Yang, D. Nataraj

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceMicrostructureNanocompositeAmorphous solidAmorphous carbonThin filmTinCarbon nitrideIon beam-assisted depositionCarbon fibersComposite materialNitrideCarbon filmChemical engineeringTitanium nitrideIon beamLayer (electronics)NanotechnologyMetallurgyComposite numberIonCrystallographyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Carbon based materials have attracted much for its unique surface microstructure and nanomechanical properties among researchers. In this study, the influence of microstructure on the nanomechanical properties of thin carbon based films was studied in detail. For which amorphous Carbon nitride (a-CN) and Titanium incorporated amorphous Carbon nitride (Ti/a-CN) thin films were prepared with a thickness of less than 100 nm using hybrid ion beam deposition technique. The incorporation of Ti into the a-CN matrix greatly modified the sp 3 /sp 2 hybridized bonding ratio and it is reflected in the mechanical hardness of Ti/a-CN thin film. Most of the incorporated Ti reacts with carbon and nitrogen to form TiN and TiCN phases respectively. On the other hand, owing to the usage of energetic ion bombardment and the presence of TiN/TiCN phases in the carbon nitride matrix, the Ti/a-CN nanocomposite film shows improved adhesion strength compared to that of pure a-CN film. Overall the presence of hard metallic phase in the amorphous carbon network alters the microstructure and improves the adhesion strength of a-CN films suitable for protective coating applications.

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)0.003
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 score0.002
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.280
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

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
Published2014
Admission routes1
Has abstractyes

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