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Record W2462839132 · doi:10.20964/2016.08.03

Study on the Electrochemical Corrosion and Scale Growth of Ductile Iron in Water Distribution System

2016· article· en· W2462839132 on OpenAlexaff
Hao Guo, Yimei Tian, Hailiang Shen, Xingfei Liu, Ying Chen

Bibliographic record

VenueInternational Journal of Electrochemical Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCorrosionElectrochemistryDistribution (mathematics)MetallurgyMaterials scienceScale (ratio)ChemistryMathematicsGeographyElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

A simulated water distribution system (WDS) combining specialized coupon test units and an electrochemical measurement cell is designed to provide an actual pipe corrosion environment. Coupon test is used to obtain the scale morphology and corrosion rate (CS), and provide scale samples for physicochemical characteristic analysis. Electrochemical impedance spectroscopy (EIS) is conducted simultaneously to display the scale structure change. Three corrosion stages are observed during a 32-day circulation. In initial stage (before day 4), the thin double-layer scales fast form inducing a sharp decline of CS (0.9455~0.1492 mm/a). In developmental stage (day 4~16), the loose scales grow uniformly and the CS decreases slowly (0.1492~0.0936 mm/a). In stable stage (after day 16), the protective scales with a compact outside layer finally form, and the CS maintains at a low value around 0.0900 mm/a. EIS fitting results showed that the stable scales consisted of a compact outside layer and a porous inside layer. In the stable stage, the increasing content of goethite and calcite slows the rate of dissolved oxygen diffusing through the scales, resulting in mass diffusion turning to be the rate-determining step of corrosion. Finally the mechanism of scale growth and localized tubercle formation is proposed.

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)0.001
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 score0.004
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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