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Record W2938750900 · doi:10.1080/21870764.2019.1604609

Influence of different sodium-based additives on corrosion resistance of PEO coatings on pure Ti

2019· article· en· W2938750900 on OpenAlexaff
Maryam Molaei, Arash Fattah‐alhosseini, Mohsen K. Keshavarz

Bibliographic record

VenueJournal of Asian Ceramic Societies · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceCorrosionMicrostructureSodium silicateDielectric spectroscopyPlasma electrolytic oxidationSodiumDiffractometerScanning electron microscopeSodium hydroxideCoatingMetallurgyElectrolyteSodium aluminateSodium fluorideChemical engineeringElectrochemistryInorganic chemistryFluorideComposite materialAluminiumChemistry

Abstract

fetched live from OpenAlex

In the present study, the plasma electrolytic oxidation method was used to form ceramic coatings on pure Ti substrates. For this purpose, five different aluminate-based electrolytes containing various sodium-based additives (sodium hydroxide, sodium phosphate, sodium silicate, sodium tetraborate and sodium fluoride) were used. The role of additives in the growth, macrostructure, surface and cross-sectional microstructure, phase composition and corrosion resistance of the coatings was evaluated. An X-ray diffractometer and scanning electron microscope were used to study the microstructure and phase composition of the coatings, respectively. The corrosion resistance of the coatings was investigated by applying electrochemical impedance spectroscopy and potentiodynamic polarization tests in a 3.5 wt. % NaCl solution. The results revealed that use of a sodium phosphate additive led to formation of a coating (4.33 µm thick) with the most suitable microstructure and highest corrosion resistance (4.36 × 106 Ω cm2), which was 53 times higher than that of uncoated Ti.

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 score0.035
Threshold uncertainty score0.427

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations63
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Asian Ceramic SocietiesSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207