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Record W2079642270 · doi:10.1115/gt2013-94024

Electrochemical Behaviours of Titanium Nitride (TiN) and Chromium Nitride (CrN) Based PVD Coating Systems

2013· article· en· W2079642270 on OpenAlexaff
Feng Cai, Xiao Huang, Qi Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNational Research Council CanadaCarleton University
FundersSouthwest Research Institute
KeywordsMaterials scienceCorrosionCoatingTinMetallurgyTitanium nitrideDielectric spectroscopyLayer (electronics)MicrostructureDiffusion layerNitrideChromium nitridePolarization (electrochemistry)TitaniumPitting corrosionComposite materialElectrochemistryElectrode

Abstract

fetched live from OpenAlex

Hard coatings applied to steel components prevent corrosion attacks while at the same time mitigate erosion attack. However, the presence of process related through-coating defects such as pin holes, voids and growth defects, provides accesses for corrosive media to metal substrates, initializing pitting corrosion and eventually resulting in coating failure. This research studies the corrosion behavior of PVD TiN and CrN (CrSiCN) coated steels in 3.5 wt. % NaCl aqueous solution using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. The results revealed that in a coating-substrate system, effective diffusion coefficient and diffusion layer thickness control the corrosion resistance; both factors are found to be related to coating microstructure. A denser and thicker coating structure are shown to have lower effective diffusion coefficients and greater effective diffusion layer thicknesses and consequently provided a high resistance to electrochemical corrosion.

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 categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

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