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Record W2074458172 · doi:10.2298/jsc0206425m

Comparison of the influence of nitrate ions on the electrochemical behaviour of iron and carbon steels in sulphate solutions

2002· article· en· W2074458172 on OpenAlexaff
Houy Ma, Shenhao Chen, Chao Yang, Jing‐Li Luo

Bibliographic record

VenueJournal of the Serbian Chemical Society · 2002
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDielectric spectroscopyNitrateFerrite (magnet)Pitting corrosionCorrosionCyclic voltammetryMaterials scienceCarbon steelElectrochemistryInorganic chemistrySulfideIonCapacitive sensingElectrical impedanceMetallurgyChemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

The effect of nitrate ions on the electrochemical behaviour of iron (ferrite) and two carbon steels (martensite and pearlite) in sulphate solutions of different pH values was investigated by cyclic voltammetry polarization and electrochemical impedance spectroscopy. The pitting inhibiting effect of nitrate ions on ferrite in sulphate media is pH dependent. Nitrate ions were unable to inhibit the pitting on ferrite in neutral sulphate solutions, but did effectively protect passivated ferrite from pitting in acidic sulphate solutions. No pitting occurred on the surface of the martensite and pearlite specimens in sulphate solutions regardless of the pH of the solutions. At the open-circuit corrosion potentials, the three materials underwent general corrosion. The impedance spectra for the three materials measured in neutral sulphate solutions containing nitrates and chlorides at the corrosion potentials all showed a capacitive loop, while in acidic sulphate solutions their impedance spectra were greatly reduced in size and displayed at least a low frequency impedance loop (inductive or capacitive loop) besides the well-known high frequency capacitive loop. The variation of the impedance behaviour with pH is explained.

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 score0.211

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 head0.029
GPT teacher head0.266
Teacher spread0.237 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Serbian Chemical SocietySame topicCorrosion Behavior and InhibitionFrench-language works237,207