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Record W3140849043 · doi:10.1111/ijac.13763

Enhancement of the Ti‐6Al‐4V alloy corrosion resistance by applying CrN/CrAlN multilayer coating via Arc‐PVD method

2021· article· en· W3140849043 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

VenueInternational Journal of Applied Ceramic Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsBrock University
Fundersnot available
KeywordsMaterials scienceMetallurgyCoatingArc (geometry)AlloyCorrosionPhysical vapor depositionComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract In this study, the Ti‐6Al‐4V substrate was coated by CrN‐CrN/TiN‐TiN and CrN/CrAlN multilayer coatings using the cathodic arc physical vapor deposition (Arc‐PVD) method. The results of potentiodynamic polarization (PDP) have shown the lowest and highest corrosion current density belong to the double‐layer (0.16 µA/Cm 2 ) and TiN (0.51 µA/Cm 2 ) samples, indicating the higher corrosion resistance of the double‐layer coating. The field emission electron microscope (FESEM), X‐ray diffraction pattern (XRD), open circuit potential (OCP), PDP, and electrochemical impedance spectroscopy (EIS) analysis were employed in order to characterize the coatings and evaluate their corrosion behavior. Finally, applying the double‐layer coating resulted in the significant improvement of the protective behavior of the Ti‐6Al‐4V alloy, as compared to the sample coated with TiN in corrosive environments.

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)0.000
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: none
Teacher disagreement score0.645
Threshold uncertainty score0.546

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 science0.0010.000
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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