Mechanistic study of the atmospheric corrosion of BC550 weathering steel in simulated marine environments by in-situ electrochemical noise
Bibliographic record
Abstract
Purpose This study aims to investigate the corrosion behavior of BC550 weathering steel in a simulated marine atmospheric environment, with a focus on the accelerating effect of pollutants on the corrosion process. Design/methodology/approach A systematic study was conducted using electrochemical noise (EN) measurements, which enabled in-situ monitoring and real-time investigation of the corrosion process of BC550 weathering steel. The reliability of the EN data was further validated through correlation with weight gain measurements. The corrosion products and rust layer evolution were characterized using X-ray diffraction and scanning electron microscopy. Findings The introduction of pollutants into the marine atmosphere increased the corrosion rate of BC550 weathering steel by approximately 10%, confirming their accelerating effect. The rust layer exhibited distinct color transformations, progressing from orange/yellow to red/brown and finally to black over time. A protective layer formed on the steel surface due to a decrease in β-FeOOH and an increase in a-FeOOH content. Originality/value This study reveals the impact of pollutants on BC550 weathering steel corrosion in marine atmospheres and provides insights into the rust layer’s morphological and compositional evolution. The findings offer valuable guidance for improving corrosion resistance in weathering steel under polluted marine environments.
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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".