Influence of Cupric, Ferric, and Chloride on the Corrosion of Titanium in Sulfuric Acid Solutions Up to 85°C
Bibliographic record
Abstract
Titanium is chosen as the construction material of autoclaves for pressure acid leaching of metal ores. The corrosion behavior of titanium was studied in sulfuric acid solutions with different additions of Cl−, Cu2+, and Fe3+ to simulate hydrometallurgical lixiviants at 25, 55, and 85°C. Electrochemical methods like open-circuit potential measurement, potentiodynamic polarization, potentiostatic polarization, and electrochemical impedance spectroscopy (EIS) were used to study the influence of these ions on the corrosion response of titanium in sulfuric acid. The chemical composition of titanium oxide films was examined further using x-ray photoelectron spectroscopy (XPS). Potentiodynamic polarization experiments demonstrated that the presence of Cl−, Cu2+, and Fe3+ facilitated the anodic passivity of titanium in sulfuric acid. EIS experiments showed that modest addition of Fe3+ (1.0 g/L) increased the polarization resistance most significantly. Both electrochemical experiments and surface analysis showed that the presence of Cu2+ affected the titanium oxide films, and the mechanism behind this effect is discussed in view of the obtained results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".