Effect of Low Alloy Steel Powder- Ethyl Silicate Based Coatings on Corrosion Behaviour of Weathering Steel Exposed to Salt Environment
Bibliographic record
Abstract
Abstract Weathering steel is used almost exclusively by Ministry of Transportation Ontario (MTO) for construction of steel bridges since 1968. This steel, under normal weathering cycles, oxidises to form a tough layer of rust or ‘patina’, which protects the steel from unabated corrosion. Although many of these bridges are exhibiting stable patina in most of the locations, in recent years some bridges were found to exhibit accelerated corrosion over the driving lanes (from road salt exposure) and the corrosion products were de-bonding from the parent steel material. Analysis of the corrosion products indicated that the de-bonded patina regions exhibited greater amounts of akaganeite (β-FeOOH), while greater amounts of goethite (α-FeOOH) were observed in the regions away from road salt exposure. Hence the objective of the present study is to form and stabilize the goethite phase on weathering steel surface even under salt exposure conditions. To meet this objective, weathering steel panels have been spray painted with low alloy steel powders containing varying concentrations of copper, nickel, chromium and molybdenum using an ethyl silicate-based paint system. These elements are thought to be primarily responsible for the formation and stabilization of goethite phase. ASTM G85-A5 (Prohesion) test has been used to evaluate the corrosion behaviour of these painted panels. Corrosion product chemistry from salt spray exposures has been determined using scanning electron microscopy coupled with energy dispersive X-ray analysis, laser Raman spectroscopy and Mossbauer spectroscopy measurements. In this paper, the results from these measurements are presented and the effect of various low alloy steel powder paint formulations on the corrosion product composition is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.002 | 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 teacher head, 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".