Development of Charge Transport Model to Study the Diffusion-Controlled Dissolution and Multi-Pit Interaction in Pitting Corrosion
Bibliographic record
Abstract
Recently, we developed a reaction-transport model that incorporates diffusion, migration, and complexation to examine the charge transport inside a one-dimensional corrosion pit [1]. In this presentation, we will demonstrate the application of this model to study the “diffusion-controlled” dissolution of a metal in a pit from a more advanced point of view, such that the limiting anodic current density varies with the bulk NaCl concentration. An analytical result on anodic limiting current density as functions of pit depth, diffusivity, and chloride concentration was derived explicitly and coupled with artificial pit experiments. In a dilute chloride solution with low conductivity, the limiting current density is increased due to the increase of the migration flux near the pit mouth. On the other hand, the presence of Na+ at the bottom of the pit is significant for moderate to high bulk NaCl concentrations, which increases the limiting current density to some degree. The proposed full mass transport model can be used to predict limiting current densities at different ranges of bulk NaCl solutions. At above 2.5 M NaCl, the conventional diffusion-only model can give reasonably accurate results, but the effect of Na+ at the bottom of the pit on the solubility of metal ions and the limiting current density should be considered. Finally, we will discuss the extension of the current work to the two-dimensional pit model to investigate the propagation and the interaction mechanism of multiple pits. The 2D multi-pit simulation under galvanostatic conditions shows the interaction between pits of different sizes. Due to the increase of the local chemistry inside the larger pit, the dissolution rate at the surface is faster and suppresses the growth of the neighboring pits. Reference: V. A. Nguyen, A. G. Carcea, M. Ghaznavi and R. C. Newman, The Effect of Cation Complexation on the Predicted “B” Value in Galvele’s Pit Model, J. Electrochem. Soc., 166, C3297 (2019).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".