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Record W2104591924 · doi:10.5539/mas.v9n11p119

Effect of Nitrogen on the Corrosion Behavior of Austenitic Stainless Steel in Chloride Solutions

2015· article· en· W2104591924 on OpenAlexvenueno aff
Wafaa A. Ghanem, W. HUSSEIN, S. N. Saeed, S. Bäder, Rabab M. Abou Shahba

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceAusteniteCorrosionPassivationNickelDielectric spectroscopyMetallurgyChlorideNitrogenAustenitic stainless steelPolarization (electrochemistry)ElectrochemistryFerrite (magnet)MicrostructureElectrodeComposite materialChemistry

Abstract

fetched live from OpenAlex

The effect of partial replacement of nickel with nitrogen on the corrosion resistance of newly designed austenitic stainless steel samples without and with heat treated was investigated in 3.5wt% and 5wt% NaCl solution using open-circuit, potentiodynamic, cyclic anodic polarization and electrochemical impedance spectroscopy techniques. The results showed that, passivation in sample 1 where the highest addition of nickel and low addition of nitrogen is different from that for sample 4 where the nitrogen is greatest and the nickel is reduced almost to the third comparing sample 1. The difference in responses of heat treated samples to localized and general corrosion could be attributed to the difference in their phase compositions. The appearance of ferrite phase for samples (2, 4, 5 and 6) after heat treatment resulted in lowering the general and localized corrosion resistance than as forged samples in contrast with samples 1 and 3, where they still pure austenite. The obtained results are confirmed by surface examination.

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)0.005
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 score0.005
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.290
Teacher spread0.250 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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