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Record W2995057276 · doi:10.1002/maco.201911384

Characterization of the corrosion performances of as‐cast Mg–Al and Mg–Zn magnesium alloys with microarc oxidation coatings

2019· article· en· W2995057276 on OpenAlexafffund
Yuna Xue, Xin Pang, Bailing Jiang, Hamid Jahed, Di Wang

Bibliographic record

VenueMaterials and Corrosion · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsNatural Resources CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionMaterials scienceMetallurgyMicrostructureCoatingAlloyMagnesiumMagnesium alloySalt spray testElectrolytePlasma electrolytic oxidationElectrochemistryComposite materialElectrodeChemistry

Abstract

fetched live from OpenAlex

Abstract In the present study, corrosion‐protective microarc oxidation (MAO) coatings were prepared on AZ31B, AZ80, and ZK60 cast magnesium alloy substrates in an alkaline silicate electrolyte. The corrosion performances of the uncoated and MAO‐coated alloys were investigated using electrochemical and salt spray chamber corrosion tests. The microstructure characterization and experimental results show that among the three alloys studied, the ZK60 Mg alloy exhibited the best and AZ31B the least corrosion resistance under the salt spray conditions. The MAO coating provided robust corrosion protection of the Mg substrates and resulted in a significant decrease in the corrosion rate of the alloys by 3–4 orders of magnitude. The MAO coating on ZK60 alloy showed better corrosion protectiveness than that on the AZ series alloys due to the incorporation of different alloying elements in the coating, especially the Zn and Al elements, which are from the Mg substrate. The corrosion performances and mechanisms of the uncoated and MAO‐coated Mg alloys are interpreted in terms of the microstructure and phase/chemical compositions of both the substrates and coatings.

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 score0.027
Threshold uncertainty score0.540

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)0.0000.000

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 head0.005
GPT teacher head0.188
Teacher spread0.182 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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