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Effect of bicarbonate concentration on corrosion of high strength steel

2014· article· en· W1974802631 on OpenAlexaff
Faysal Fayez Eliyan, Akram Alfantazi

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPassivationCorrosionBicarbonateCathodic protectionElectrochemistryDissolutionAnodeMaterials scienceMetallurgyCyclic voltammetryInorganic chemistryChemistryElectrodeComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This research evaluates the passivation process, in relation to the anodic and cathodic reactions, in deoxygenated solutions of different bicarbonate concentrations. Two types of API-X100 steel microstructures were examined. They are similar to near fusion heat affected zones (HAZs) which were produced by special thermal cycles. By monitoring the open circuit potentials, the passivation process exhibited electrochemical signs that it forms faster with higher bicarbonate concentration. During cyclic voltammetry, bicarbonate in concentrations less than 0·1M impedes the passivation, by catalysing the anodic dissolution. In higher concentrations, bicarbonate seemed more protective in facilitating the development of thicker passive films, from an electrochemical perspective that encourages corresponding physicochemical investigations in the future. The transpassivation seemed to depend more on the chemistry of the passive film than on the formation of FeCO3. Cooling down the HAZs at high rates could make them more corrosion resistant, but probably of less protective corrosion products.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.034
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

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