Predicting Carbon Dioxide Corrosion of Bare Steel Under an Aqueous Boundary Layer
Bibliographic record
Abstract
The corrosion of bare steel under an aqueous boundary layer with dissolved carbon dioxide (CO2) was modeled to investigate the effect of CO2. The model incorporated the coupled effect of CO2 diffusion, hydration, local ionic equilibria, ferrous carbonate (FeCO3) precipitation, and steel corrosion. The model was verified against published experimental data under both FeCO3-saturated and unsaturated boundary layers. Good agreement was shown under a variety of conditions. For saturated boundary layers, the results show that the corrosion rate in carbonic acid (H2CO3) is greater than in hydrochloric acid (HCl) for a given pH and that H2CO3 reduction is the cause for the increase of corrosion rate in H2CO3. Increasing temperature was found to increase corrosion rate substantially. This work provides further understanding of the CO2 corrosion mechanism and is a reliable, convenient, and practical tool for predicting the rate of CO2 corrosion.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".