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Record W3199897446 · doi:10.20964/2021.10.02

Effect of Copper(II) Ions on Corrosion Resistance of Al-Zn Coated 5052 Aluminum Alloy in Seawater

2021· article· en· W3199897446 on OpenAlexaff
Guofeng Lv, Yanming Xia, Xiong Chen, Zhao Liu, Fang Zhuang, Zhiming Gao

Bibliographic record

VenueInternational Journal of Electrochemical Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsSeawaterCopperAluminiumAlloyCorrosionMetallurgyMaterials science5052 aluminium alloy6111 aluminium alloyIonChemistryOceanographyGeology

Abstract

fetched live from OpenAlex

The corrosion resistance of the composite anti-corrosion coating formed by an aluminum-zinc coating and epoxy sealer on the surface of an open rack vaporizer (ORV) in seawater was investigated at different copper ion (Cu 2+ ) concentrations. The surface microstructure of the coating was characterized by ultra-field 3D observation, SEM and energy spectrum analysis (EDS). The sealer was found to be effective in shielding the coating surface and significantly reduced the corrosion rate of the coating. A dissolution process of the passive film on the coating surface is evident from the potentiodynamic polarization curves and surface morphology. Local corrosion occurs on the coating surface, and the number and area of surface corrosion pits increase with the concentration of copper in solution, indicating a decrease of the corrosion resistance of the coating. Weight loss of the samples increases from 4.5 mg to 5.4 mg after a 32 days immersion, and the damage of copper ions on the coating originates from weak positions of the surface. The electrochemical corrosion behavior of the coating was investigated by dynamic potential polarization and electrochemical impedance spectroscopy (EIS), with a positive effect found after a short immersion period in a solution with copper ion concentration lower than 5 ug/L, with this effect not evident at higher copper concentrations.

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)0.001
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 score0.001
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.292
Teacher spread0.284 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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