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Record W2858037768 · doi:10.20964/2018.08.38

Influence of Micro-arc Oxidation Coatings on Corrosion Performances of AZ80 cast alloy

2018· article· en· W2858037768 on OpenAlexaff
Yuna Xue, Xin Pang, Bailing Jiang, Hamid Jahed

Bibliographic record

VenueInternational Journal of Electrochemical Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsNatural Resources CanadaUniversity of Waterloo
Fundersnot available
KeywordsMicro arc oxidationAlloyMetallurgyMaterials scienceCorrosionArc (geometry)Magnesium alloyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

To enhance the corrosion performance of cast AZ80 alloy, micro-arc oxidation (MAO) coating was synthesized at various processing current densities in a basic silicate-fluoride solution. The microstructure, composition, corrosion performance and Mott-Schottky characteristics of MAO coatings at different processing current densities were investigated using various microscopic characterization and electrochemical methods. It was found that a thinner (5.04 μm) MAO coating produced at the low processing current density obtained a more uniform and smaller discharge pores morphology and higher fluoride content compared to the other coatings produced at higher current densities. The open-circuit potential, corrosion current density and polarization resistance values of this coating were -1.28 V vs. Ag/AgCl electrode, 0.00589 μA/cm 2 and 1.53×10 6 Ω∙cm 2 in 3.5 wt.% NaCl, respectively. After the Mott-Schottky test, the analysis of the coating showed that the uncoated and MAO coated AZ80 alloy exhibited p-type semiconductor characteristics. For the MAO coated specimens, the coating synthesized at the lower applied current density showed lower acceptor concentration and highly negative flat band potential. These features are associated with the reduced reactivity and improved corrosion resistance of this new MAO coating.

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)0.001
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 score0.002
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

Explore more

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