The Pitting Corrosion of Titanium in Aggressive Environments: a Review
Bibliographic record
Abstract
Abstract This study presents an overview of a number of factors influencing the pitting corrosion of Ti in aggressive environments. Firstly, the effects of temperature and metal cations on the pitting corrosion of Ti are summarized based on previous research. The pitting corrosion of Ti in chloride solutions is strongly temperature-dependent; the breakdown potential Eb decreases as the operating temperature increases. The presence of oxidizing metal cations can cause severe pitting on Ti in chloride solutions. Secondly, the inhibition effects of different oxygen-containing anions, such as CrO42−, HPO42−, SO42−, SeO42− and S2O32−, on the pitting corrosion of Ti in Cl− solutions at 150°C are explored. SO42−, SeO42−, CrO42− and HPO42− anions all retards the pitting corrosion of Ti in Cl− solutions. The inhibition effect decreases in the order: CrO42− > SO42− = SeO42− > HPO42−. On the other hand, S2O32− and Cl− ions have a synergistic effect in inducing localized corrosion on Ti.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".