Corrosion Products and Surface Morphology for Coated, Uncoated, and Insulated A333 Steel Pipelines in Marine Harsh Environment
Bibliographic record
Abstract
Abstract In situ studies with CUI and corrosion under coating is rare, especially for full scale tests in marine harsh environment. A333 low temperature carbon steel is selected for its versatile use in cold environments. This material is not widely studied in marine environment. Therefore, this work reports corrosion type, products, morphology and mechanism for thirty-six model pipelines (insulated, uninsulated, coated and uncoated) placed at Argentia, NL. Corrosion products were identified using x-ray diffraction (XRD). The detection and semi-quantification of elements on pipe surfaces was performed using energy disruptive spectroscopy (EDS) which was coupled to a scanning electron microscope (SEM). SEM images confirmed the formation of characteristic morphological structures such as sandy crystal (lepidocrocite γ-FeOOH), cotton ball (goethite α-FeOOH), and small grain (akageneite β-FeOOH) structures. For insulated uncoated pipes, the main phases were goethite, akageneite, and hematite(α-Fe2O3). For uncoated uninsulated pipes, akageneite, goethite, and hematite were detected as main phases. For coated pipes, goethite was the main phase. When ferrihydrite was detected with akageneite, there was less lepidocrocite and goethite than when ferrihydrite was not present. Uncoated pipes had deepest pits and highest corrosion rates as previously reported in [1]. Magnetite (Fe3O4) was present in these samples only in year two. Magnetite is a passive oxide formed on the iron surface but can also be a product of microbial reduction of ferrihydrite in certain conditions. A proposed mechanism for the high corrosion rate and pits in uncoated pipes is due to the increased localized corrosion from akageneite due to excess chloride and moisture from seawater spray as well as stabilized ferrihydrite limiting goethite formation, thus reducing steel pipe surface passivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".