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Record W2993661167 · doi:10.3390/coatings9120835

Electrochemical Behavior, Microstructure, and Surface Chemistry of Thermal-Sprayed Stainless-Steel Coatings

2019· article· en· W2993661167 on OpenAlexafffund
Alexandre Romão Costa Nascimento, Samantha Michelle Gateman, Janine Mauzeroll, Sylvio Savoie, Robert Schulz, Christian Moreau

Bibliographic record

VenueCoatings · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsMcGill UniversityConcordia UniversityHydro-Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureCoatingMetallurgyCorrosionX-ray photoelectron spectroscopyConversion coatingIngotPolarization (electrochemistry)Thermal sprayingElectrochemistryComposite materialChemical engineeringElectrodeAlloyChemistry

Abstract

fetched live from OpenAlex

Thermally sprayed stainless-steel coatings were produced with a wide range of deposition parameters. The electrochemical behavior of polished coatings was monitored for 3 weeks in 3.5 wt.% NaCl aqueous solution and compared to that of reference materials including a wrought stainless steel plate and a bulk ingot produced by arc melting of the spraying powder feedstock. Transitions in the polarization behavior are discussed based on the observed changes in coating microstructures as well as on the shifts in X-ray photoelectron spectra (XPS). Results show that the deposition parameters have a strong effect on the coating microstructures but the small differences in the polarization behavior of coatings mostly disappear after 1 week of testing. Microstructure evidence shows preferential corrosion at splats experiencing melting prior to deposition. Pitting and corrosion products between splat boundaries are also reported. XPS analysis shows that the coating surfaces are enriched in chromium oxides and hydroxides. Comparison between the coating and bulk stainless steels suggests that coating inherent defects play a major role on their impaired corrosion resistance.

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 categoriesMeta-epidemiology (narrow)
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 score0.020
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.005
GPT teacher head0.208
Teacher spread0.204 · 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.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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