Corrosion performance of 2205 duplex stainless steel in simulated hydrometallurgical processes: A study of uniform and crevice corrosion
Bibliographic record
Abstract
The corrosion behavior of SAF 2205 duplex stainless steel (DSS) is studied under acidic conditions of hydrometallurgical processes. The focus is on uniform and crevice corrosion. A Central Composite Design (CCD) was employed to evaluate the effects of temperature, sulfuric acid, hydrogen peroxide, and chloride concentration on the corrosion rate and critical crevice temperature (CCT) of 2205 DSS. Analysis of variance (ANOVA) revealed that the uniform corrosion rate is primarily influenced by temperature and chloride concentration, with both factors accelerating the corrosion of the alloy. However, the uniform corrosion rate of 2205 DSS remained generally below the 100 µm/yr threshold. Moreover, the CCT of the alloy decreased with increasing concentrations of Cl − and H 2 O 2 , while higher concentrations of H 2 SO 4 improved CCT, likely due to inhibition by sulfate ions. These findings enhance the current understanding of the corrosion resistance of 2205 DSS in aggressive environments, offering valuable insights for the application of this alloy in hydrometallurgical processes. • SAF 2205 DSS showed uniform corrosion rates below 100 µm/yr, influenced by temperature and chloride concentration. • Cl⁻ and H₂O₂ reduced the CCT of the alloy, while H₂SO₄ improved it by inhibiting localized corrosion. • Oxidizing agents like H₂O₂ and Cu(II) reduced uniform corrosion by shifting potentials to the passive region. • High Cl⁻ concentrations increase crevice corrosion risk, highlighting the need for chloride control.
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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".