Pourbaix Diagrams As a Root for the Simulation of Polarization Curves for Corroding Metal Surfaces
Bibliographic record
Abstract
The corrosion of metals is a much researched area thanks to its importance in the development of materials for a wide range of applications. A common means of experimental analysis of the corrosion rate of a specific metal is the recording of polarization curves and subsequent Tafel analysis. Comparison of such data with simulated examples provides useful validation of experimental data, as well as a better understanding of the specific reactions occurring at the metal surface. Many existing models require knowledge of parameters such as the exchange current density and Tafel slope, which requires the experiment to be conducted before the model is run. Here, we propose a model based around the Pourbaix diagram, where input parameters are either simply calculated from reaction schemes, or are easily accessible from thermodynamic data tables. In this work we use Pourbaix diagrams as a means for simulating a polarization curve at a corroding iron surface. Pourbaix diagrams show the boundaries between the changing thermodynamically stable species at a metal surface in an aqueous environment as a function of the applied potential and pH. The position of these boundaries can therefore be used to model the onset of the corresponding oxidation and reduction reactions by combining the equation of the appropriate boundary line with Butler-Volmer kinetics. At the same time, the change in pH local to the metal surface is monitored by simulating the flux of protons generated during the oxidation process, and the impact of this on the corrosion potentials and rate is taken into account. This is of great importance as the corrosion rate and the corrosion product varies according to the pH at the metal surface. In this way, we show a simple means for the simple simulation of a polarisation curve at an iron surface, which is in excellent agreement with an experimentally recorded curve under the same conditions. This same method can then be applied to more complex metal alloys such as stainless steels, by combining the Pourbaix diagrams for the appropriate alloy components. This allows the model to be used as a standalone analytical tool for the prediction of corrosion behaviour of novel alloys before they are developed, as well as for the validation of experimental data obtained from existing samples. Figure 1: Pourbaix diagram for the iron - water system Figure 1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".