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Record W2461235763 · doi:10.1002/sia.6071

<i>In‐situ</i> characterization of the early stage of pipeline steel corrosion in bicarbonate solutions by electrochemical atomic force microscopy

2016· article· en· W2461235763 on OpenAlexafffund
Yuan Li, Y. Frank Cheng

Bibliographic record

VenueSurface and Interface Analysis · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionElectrochemistryDissolutionSurface roughnessSurface finishMaterials scienceCathodic protectionMetallurgyImmersion (mathematics)Atomic force microscopyKelvin probe force microscopeComposite materialElectrodeChemistryNanotechnology

Abstract

fetched live from OpenAlex

An electrochemical atomic force microscope was used to characterize the early stage of corrosion of an X100 pipeline steel in bicarbonate solutions with varied concentrations by synchronous measurements of electrochemical potential of the steel and its topographic evolution with time. Upon immersion of the steel in 0.01 M NaHCO 3 solution, both electrochemical potential and topographic profile are associated with the dissolution of air‐formed oxides present on the steel surface. The potential drops and the surface roughness increases rapidly. When corrosion of the steel occurs, the potential further drops and the surface roughness of the steel increases gradually. As the steel corrosion achieves a steady state, the generation of corrosion products reaches a dynamic equilibrium state. The surface roughness maintain an approximately stable value. In solutions containing increased bicarbonate concentrations, such as 0.1 M and 0.5 M NaHCO 3 , the steel can be passivated. The formed passive film can eliminate some surface features and improves the surface roughness. The topographic profile of the steel surface in 0.5 M NaHCO 3 solution is smoother than that in 0.1 M solution. The surface features within 20 nm become eliminated after 4500 s of immersion in 0.1 M NaHCO 3 solution, while larger features within 50 nm in size are eliminated in 0.5 M NaHCO 3 solution in the same time period. Copyright © 2016 John Wiley &amp; Sons, Ltd.

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 score0.013
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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