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Record W2007486265 · doi:10.1116/1.1559922

Thermal stability of polycrystalline TiN/CrN superlattice coatings

2003· article· en· W2007486265 on OpenAlexaff
Qi Yang, L.R. Zhao

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2003
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceSuperlatticeTinCrystalliteAnnealing (glass)Sputter depositionMicrostructureThermal stabilitySputteringBilayerDiffractionMetallurgyComposite materialThin filmOpticsOptoelectronicsNanotechnologyChemistry

Abstract

fetched live from OpenAlex

This article reports the structure and hardness responses of TiN/CrN superlattice coatings to elevated-temperature annealing. Polycrystalline TiN/CrN superlattices with bilayer periods of 5.6–39 nm were deposited on a Ni-base alloy substrate by reactive unbalanced magnetron sputtering. The superlattices were subsequently annealed in vacuum at elevated temperatures for 2–100 h, followed by characterization using small-angle x-ray reflection, high-angle x-ray diffraction, and hardness testing. The superlattices can sustain their hardness up to 650 °C for 4 h or 575 °C for 100 h. A strong correlation exists between the hardness and the x-ray reflection or x-ray diffraction characteristics. A marked reduction in the high-angle satellite/Bragg peak ratio or in the small-angle reflection intensity corresponds to a rapid decrease in the hardness. This phenomenon is related to a physical transition during which the loss of hardness is caused by the structural instability resulting from accelerated interdiffusion between TiN and CrN layers, which leads to reduced compositional modulation amplitude and diffuse layer interfaces.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2003
Admission routes1
Has abstractyes

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