Effect of Sulfate‐Based Stabilizers on the Rust Layer Formation and Corrosion Resistance of Q420qNH Weathering Steel
Bibliographic record
Abstract
ABSTRACT This study investigated the use of sulfate‐based stabilizers to stabilize the surface of Q420qNH weathering steel (WS) and their effect on rust layer formation and corrosion resistance. Dry/wet cyclic corrosion tests (CCT) were used to examine the effects of the various treatment regimens. The results demonstrated that sulfate‐based stabilizers accelerated the formation of α‐FeOOH, thereby enhancing the corrosion resistance of WS in simulated marine atmospheres. Particularly, the A1 stabilizer consisting of the CuSO4, FeSO4, NaHSO3, and Cr2(SO4)3 solution system shows better applicability under the simulated marine atmosphere. Specifically, after 8+64CCTs, the corrosion rate of sample A1 decreased by 54%, while the proportion of α‐FeOOH, the α/γ* value, and the self‐corrosion potential (Ecorr) increased by 15%, 0.55, and 0.29 V, respectively. The rust layer of Q420qNH WS primarily consists of γ‐FeOOH, α‐FeOOH, Fe3O4, and γ‐Fe2O3. Surface stabilization treatment with sulfate‐based stabilizers promoted the enrichment of Cu and Cr in the cracks, facilitating the filling of these defects and enhancing the stability and corrosion potential of the rust layer.
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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".