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Electrochemical Investigation of Stellite 6 Alloy in Morpholine Corrosive Environment

2018· article· en· W2896595772 on OpenAlexaff
Rong Liu, Yi Li, G.A. McRae

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsCarleton University
Fundersnot available
KeywordsStelliteMaterials sciencePassivationAlloyMetallurgyCorrosionPourbaix diagramMorpholineOxideBoiler (water heating)ElectrochemistryComposite materialElectrodeWaste management

Abstract

fetched live from OpenAlex

The corrosion resistance of Stellite 6 alloy in morpholine solution with pH 9.5 is investigated using the electrochemical test method, simulating the amine environment of the boiler feed water service condition in coal power plants. Polarization test is performed on Stellite 6 alloy under the low potential varying from-0.4 VSCE to 1.2 VSCE and is also conducted under a constant high potential (4 VSCE) in order to fail the sample surface. 17-4PH stainless steel, which is also a common material for the application of the boiler feed water in coal power plants, is tested simultaneously under the same conditions for comparison. It is shown that the polarization curve 17-4PH steel from the low potential test has an apparent passivation region indicating a protective oxide film formed on the sample surface, but Stellite 6 only exhibits a tendency to passivate. Both samples after the failure tests under the high potential (4 VSC) are analyzed using SEM/EDX. The surface morphologies indicate that the former is severely corroded in the solution while the latter is less corroded. The corrosion mechanisms of Stellite 6 alloy and 174PH stainless steel in morpholine solution are discussed with assistance of the Pourbaix diagrams.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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