A Mechanistic Study on Lead-Induced Passivity-Degradation of Nickel-Based Alloy
Bibliographic record
Abstract
The mechanism of lead-induced passivity-degradation in a nickel-based alloy (UNS N06690) was explored in a simulated stream generator alkaline crevice chemistry. The pitting induction test indicated that the detrimental impact of lead contamination on pitting resistance was significantly enhanced by the presence of calcium ions. An X-ray photoelectron spectroscopy analysis revealed the incorporation of lead into the passive film. Calcium ions in the alkaline chemistry entered the passive films with the aid of the lead species but the ingress of calcium did not occur in the solution free of lead contamination. The incorporation of lead reduced chromium and iron content in passive films and hindered the dehydration processes during the passivation. Mott-Schottky and photoelectrochemical measurements showed that passive films on the nickel-based alloy are p-type semiconductors and the incorporation of lead may reduce the acceptors in the passive films. A further analysis suggested that the lead-induced passivity degradation of nickel-based alloys may be related to the increases in M–O and bonds, the electronic structural changes of spinel oxides in the passive film, and reduced ion-selectivity due to the surface adsorption of the lead species.
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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".