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Record W1999549085 · doi:10.1116/1.1798771

Structure and mechanical properties of Ti–Si–N films deposited by combined DC/RF reactive unbalanced magnetron sputtering

2004· article· en· W1999549085 on OpenAlexaff
Xing-zhao Ding, Xin Zeng, Y. C. Liu, Qi Yang, L.R. Zhao

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2004
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceTinCrystalliteNanoindentationSputter depositionSputteringThin filmCavity magnetronAnalytical Chemistry (journal)Pulsed DCNanocompositeComposite materialMetallurgyNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Ti–Si–N nanocomposite films with Si content between 0 and 13.5at.% were deposited by combined DC/RF reactive unbalanced magnetron sputtering. The composition, structure, and mechanical properties of the as-deposited Ti–Si–N films were measured by energy dispersive analysis of x rays, x-ray diffraction (XRD), and nanoindentation experiments, respectively. All of the Ti–Si–N films exhibited a higher hardness than pure TiN films deposited under similar conditions. The highest hardness (∼41GPa) was obtained in the film with Si content of about 8at.%. Ti–Si–N films also exhibited a higher resistance to plastic deformation (i.e., higher ratio H3∕E*2) than pure TiN. XRD patterns revealed that the as-deposited films were composed of cubic TiN crystallites with a preferential orientation of (111). With increase of RF power applied to the Si targets, the TiN (111) peak intensity or TiN crystallite size increased in the lower RF power range but decreased in the higher RF power range, showing a maximum at an RF power of 500W (power density ∼1.14W∕cm2), corresponding to a Si content of about 5at.% in the film.

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 score0.012
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations30
Published2004
Admission routes1
Has abstractyes

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