Effect of Ionic Interactions on the Initiation of Crevice Corrosion in Passive Metals
Bibliographic record
Abstract
In the crevice corrosion process, oxygen reduction occurs faster than diffusion into an occluded crevice, causing deoxygenation of the crevice solution. Once oxygen is depleted in the crevice, oxygen reduction can only occur on the metal surface outside of the crevice. The cations produced by metal dissolution are hydrolyzed, which causes the pH of the crevice solution to drop. This increases the rate of metal dissolution, forming an autocatalytic coupling that causes concentrated electrolyte solutions to form in the crevice. The focus of this paper is the prediction of the effect of nonideal solution behavior on crevice corrosion using the ionic interaction model of Pitzer coupled with an electrolyte mass-transport model. This mathematical model was used to simulate the type 304 stainless steel crevice corrosion experiment of Alavi and Cottis. The results are in excellent agreement with experimental observations. The model was then applied to simulate the crevice corrosion initiation period of a titanium crevice. Comparison of the predictions to those generated via an ideal solution crevice corrosion model indicates that interionic forces draw chloride ions into the crevice and hinder the transport of hydrogen ions out of the crevice. © 2005 The Electrochemical Society. All rights reserved.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".