Effects of Slurry pH on the Surface Mechanical Properties and Erosion-Corrosion Resistance
Bibliographic record
Abstract
Abstract Effects of anodic dissolution in corrosive slurries with different chemical compositions and pHs on the in-situ surface mechanical properties and slurry erosion-corrosion resistance of carbon steel were investigated. The experimental measurements indicate the existence of corrosion-enhanced erosion because the erosion is accelerated by the anodic current and it depends heavily on the slurry pH and chemistry. The in-situ nanoindentation shows that the presence of anodic current on the surface reduces the surface hardness. When the anodic current density was held unchanged, both the corrosion-enhanced erosion in acidic slurries and the corrosion-induced surface hardness degradation in the acidic solutions were much more pronounced than those in neutral and alkaline slurries/solutions. In the neutral and alkaline media, the corrosion-enhanced erosion and the in-situ surface hardness degradation were hardly affected by the chemical composition of aqueous media but a remarkable impact of chemical composition was observed when corrosive media are acidic. The agreement in the high-to-low order of erosion rates in corrosive slurries and the corrosion-induced surface hardness degradation suggests that the degradation of corrosion-induced surface mechanical property is an important mechanism of corrosion-enhanced erosion.
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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 | 0.000 | 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) | 0.000 | 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".