Effect of the Crevice Gap on the Initiation of Crevice Corrosion in Passive Metals
Bibliographic record
Abstract
Past research into the mechanism governing the time to active crevice corrosion—the incubation period—of a passive metal crevice has produced theoretical models coupled with the B-dot model, the Debye-Hückel limiting law, and other activity models to correct for nonideal behavior at moderately high concentrations. In this research, the transport model of Watson and Postlethwaite is coupled with the ionic interaction model of Pitzer to predict the effect of the crevice gap on the iR drop and chemical activity of the crevice solution. Two cathodic reactions, crevice external oxygen reduction and crevice internal hydrogen ion reduction, are assumed to balance metal dissolution. To validate the model, the experimental Type 304 (UNS S30400) stainless steel crevice of Alavi and Cottis is simulated. Model predictions improve upon predictions of past models and match observations of this experimental work within experimental uncertainty. The effect of crevice gap on a titanium crevice immersed in 0.5 M aqueous sodium chloride (NaCl) solution at 25°C also is predicted. The iR drop, electrical conductivity, and chemical activity of the solution increases as the crevice gap decreases. The relationship between iR drop and deviation from charge electroneutrality of the solution is investigated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".