The Relationship Between Pit Chemistry and Pit Geometry Near the Critical Pitting Temperature
Bibliographic record
Abstract
In pitting corrosion, the pit stability product is the product of anodic current density and depth, i a , which for an open hemispherical cavity would be proportional to the product of the cation diffusivity and some interfacial concentration of the dissolving cations, DC . Many authors believe that pit growth is diffusion-controlled, i.e., C = C s (solubility of the metal salt). Temporarily, prior to salt film precipitation, supersaturation is possible up to some maximum value: C = C ss . Estimates of i a for real pits can be used to analyze their degree of occlusion, that is, by how much the effective value of pit radius ( a eff = α n F D C s ∕ i , where α is a geometrical factor close to 0.5) differs from the geometrical pit radius calculated using Faraday’s law ( a ) . Small pits are found to be highly occluded, i.e., a eff ≫ a . For metastable pits formed in a high-alloy 904L stainless steel, the value of i a increases linearly with pit depth, and for a temperature 7 ° below the critical pitting temperature (CPT), approaches D C s at the largest observed pit depth of ca. 10 μ m , but very large metastable pits formed closer to the CPT have i a values that exceed even D C ss . We show that this is consistent with the time-dependent development of a dish-shaped pit geometry, and that by suitable correction the stability products become consistent with diffusion-controlled pit growth.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".