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Record W2051793268 · doi:10.1116/1.3463709

Microstructure and tribological performance of nanocomposite Ti–Si–C–N coatings deposited using hexamethyldisilazane precursor

2010· article· en· W2051793268 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

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2010
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceMicrostructureNanoindentationNanocompositeIndentation hardnessCoatingTribologyComposite materialNanocrystalline materialAmorphous solidSputter depositionScanning electron microscopeTinNanoindenterSputteringMetallurgyThin filmNanotechnologyCrystallography

Abstract

fetched live from OpenAlex

Thick nanocomposite Ti–Si–C–N coatings (20–30 μm) were deposited on Ti–6Al–4V substrate by magnetron sputtering of Ti in a gas mixture of Ar, N2, and hexamethyldisilazane (HMDSN) under various deposition conditions. Microstructure and composition of the coatings were studied using scanning electron microscopy, x-ray diffraction, and energy dispersive x-ray spectroscopy, while the mechanical and tribological properties of these coatings were studied using Rc indentation, and micro- and nanoindentations, solid particle erosion testing, and ball-on-disk wear testing. It has been observed that the Si concentration of these coatings is varied from 0% (TiN) to 15% (Ti–Si–C–N), while the structure of these coatings is similar to the nanocomposite Ti–Si–N coatings and consists of nanocrystalline B1 structured Ti(C,N) in an amorphous matrix of SiCxNy with the grain size of 5−>100 nm, depending on the coating preparation process. These coatings exhibit excellent adhesion when subjected to Rc indentation tests. The microhardness of these coatings varies from 1200 to 3400 HV25, while the nanohardness varies from 10 to 26 GPa. Both the microhardness and nanohardness are slightly lower than those of similar coatings prepared using trimethylsilane. However, the erosion test using a microsand erosion tester at both 30° and 90° incident angles shows that these coatings have very high erosion resistance and up to a few hundred times of improvement has been observed. These coatings also exhibit very high resistance to sliding wear with a low coefficient of friction of about 0.2 in dry sliding. There are a few advantages of using the HMDSN precursor to prepare the Ti–Si–C–N coatings over conventional magnetron sputtered deposition of Ti–Si–N coatings including composition uniformity, precursor handling safety, and high deposition rate. The coatings can be applied to protect gas turbine compressor blades from solid particle erosion and steam turbine blades from liquid droplet erosion, as well as other mechanical components that experience severe abrasion. These coatings may also be used in areas where both high wear resistance and low friction are required.

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)0.001
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 score0.061
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.218
Teacher spread0.211 · 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