Electrochemical and Passive Layer Characterizations of 304L, 316L, and Duplex 2205 Stainless Steels in Thiosulfate Gold Leaching Solutions
Bibliographic record
Abstract
Thiosulfate extraction of gold has sparked widespread interest in recovering gold from carbonaceous and copper-containing gold ores. However, thiosulfate ions are known to be dangerous corrosion promoters of structural materials. Accordingly, this study aims at characterizing the electrochemical corrosion performance of various structural steels in calcium thiosulfate and ammonium thiosulfate gold leaching solutions. The corrosion behavior was investigated using cyclic potentiodynamic polarization, open circuit potential monitoring, and electrochemical impedance spectroscopy. It was found that the stainless steels, namely 304L, 316L and duplex 2205, manifested no sign of pitting corrosion in the calcium thiosulfate leaching solution. Thiosulfate ions at a high concentration of 0.1 M in the calcium thiosulfate leaching solution played a protective role against pitting corrosion. In the ammonium thiosulfate leaching solution also, findings indicated the absence of pitting/localized corrosion for the duplex 2205 stainless steel. The thicknesses of the passive films formed on the stainless steels at different operating conditions were calculated using a variety of proposed electrochemical models and the obtained results were validated with ToF-SIMS experimental results. It was determined that the recently-developed power-law (P-L) model predicts the film thickness more accurately than the other models studied.
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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) | 0.000 | 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 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".