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Record W1604491443 · doi:10.5006/c2004-04659

Chemo-Mechanical Effect in Erosion-Corrosion Process of Carbon Steel

2004· article· en· W1604491443 on OpenAlexaff
B.T. Lu, Jingyi Luo, Jianfeng Lu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarbon steelCorrosionMaterials scienceErosionMetallurgyErosion corrosionProcess (computing)Carbon fibersComposite materialComputer science

Abstract

fetched live from OpenAlex

Abstract In this work, the chemo-mechanical effect in slurry-erosion was investigated and an attempt was made to understand the mechanism of the corrosion-enhanced erosion. The chemo-mechanical effect was evaluated with the hardness degradation due to the presence of anodic current on the surface. The micro-hardness measurements of A1045 steel was determined in de-ionized water and in an aqueous solution of 1M NaHCO3 while anodic current was applied. The results showed that the hardness decreased with increasing anodic current density and the relative hardness degradation (ΔHv / ΔHv0) is a linear function of the logarithm of the anodic current density. The dependence of hardness drop on the anodic current density is almost independent of the corrosive media although the carbon steels displayed different polarization behavior in the test solutions used in the current experiments. The erosion and erosion-corrosion tests were conducted with A1045 steel in a slurry comprising 0.1M Na2SO4 aqueous solution + 30% sand under condition of cathodic protection or action of constant applied anodic current, respectively, and the steel was annealed at different temperatures to achieve different hardness. The degradation of mechanical erosion resistance with decreasing hardness implied that a synergistic mechanism would result from the chemo-mechanical effect. It was confirmed again by the test results. The material loss rate due to the corrosion-enhanced erosion increased with increasing applied anodic current density.

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 score0.046
Threshold uncertainty score0.529

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 head0.007
GPT teacher head0.245
Teacher spread0.239 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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