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Record W2537228049 · doi:10.5006/2070

Formation and Evolution of Oxide/Oxyhydroxide Corrosion Products on Low-Alloy Steel During Exposure to Near-Neutral pH Solutions Containing Oxygen and Nitrate

2016· article· en· W2537228049 on OpenAlexaff
Ibrahim M. Gadala, Hung M. Ha, Paul Rostron, Akram Alfantazi

Bibliographic record

VenueCORROSION · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCorrosionNitrateOxygenAlloyOxideOxygen evolutionMetallurgyAlloy steelMaterials scienceInorganic chemistryChemistryElectrochemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

The evolution of corrosion product and the transient behavior of corrosion during the first 24 h of low-alloy steel exposure to near-neutral pH solutions containing O2 and are investigated in this work through periodic electrochemical polarization, spectroscopy, and characterization techniques. The formation of porous FeOOH products occurs a few hours following immersion, with morphologies distinctly dependent on O2 concentration. O2 diffusion is less in the FeOOH tubercle formations formed in 20 ppm O2 conditions, whereas the presence of Fe2O3 is exclusive to corrosion products of 6 ppm O2 environments. Charge transfer on the compounds involved governs corrosion protectiveness trends of the overall multi-layered product and reaction kinetics at sub-tubercle surface sites. Atypical 0.005 M and 0.015 M presence in the environment minimizes the cathodic potential range of O2 reduction and intensifies the corrosion of the low-alloy steel specimen. The chronology of corrosion product evolution is corroborated with x-ray photoelectron spectroscopy, Raman spectroscopy, and x-ray diffraction, while critical interactions of dissolved species with developed surface layers are quantified with electrochemical impedance spectroscopy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.228
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueCORROSIONSame topicCorrosion Behavior and InhibitionFrench-language works237,207