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Record W4393389689 · doi:10.1080/2374068x.2024.2335705

Corrosion resistance of squeeze cast magnesium alloy AM60-based hybrid nanocomposite coated with plasma electrolytic oxidation

2024· article· en· W4393389689 on OpenAlexafffund
Anita Hu, Xinyu Geng, Henry Hu, Xueyuan Nie

Bibliographic record

VenueAdvances in Materials and Processing Technologies · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMetallurgyPlasma electrolytic oxidationCorrosionMagnesium alloyNanocompositeAlloyElectrolyteMagnesiumComposite materialElectrode

Abstract

fetched live from OpenAlex

Plasma electrolytic oxidation (PEO) coating with NaAlO2 and KOH electrolytes was applied on the surfaces of as-cast AM60 (PEO-AM60), 7 vol.% Al2O3 fibres/AM60 (PEO-7FC) and 7 vol.% Al2O3 fibres + 3 vol.% Al2O3 nano-sized particles/AM60 (PEO-MHNC-7F3NP) to improve their corrosion resistance. The SEM and EDS analyses indicated that the PEO coating process formed a porous and dense layer on the substrate surface, as well as MgAl2O4 was the major concentration of the coating. The thickness of PEO coating on the 7FC and MHNC-7F3NP composites was slightly less than the coating on the AM60. The electrochemical corrosion tests were carried out in 3.5% NaCl aqueous solution at room temperature for investigating the corrosion behaviour of the as-cast AM60 and the composites after applying the PEO coating. The PEO coating increased the corrosion resistance of the AM60 alloy and the 7FC and MHNC-7F3NP composites to 342.81, 301.73 and 290.44 from 4.07, 2.08 and 1.88 kΩ∙cm2. By comparing the corrosion test results of the coated AM60 and the composites with those of the uncoated counterparts, it was found that the PEO coating significantly improved the corrosion resistance by up to 154 times. The addition of nano-sized particles barely increased the corrosion rate of the composite.

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 score0.010
Threshold uncertainty score0.610

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 communication0.0000.001
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAdvances in Materials and Processing TechnologiesSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207