Role of grain size on the corrosion resistance of pipeline steels in acidic environment
Bibliographic record
Abstract
The microstructure of pipeline steels plays an important role in determining their resistance to corrosion. Among various microstructural features, grain size has been a topic of ongoing debate regarding its influence on the corrosion resistance of pipeline steels. While it is established that grain size inversely correlates with yield strength, its impact on corrosion resistance in acidic environments remains unclear. This study investigated the effects of grain size on the corrosion resistance of pipeline steels exposed to an acidic environment. Heat treatments were conducted to vary the grain size from 8 to 19 µm while minimizing contributions from other microstructural features. The findings revealed that reducing grain size significantly enhanced corrosion resistance by promoting passivation. Grain boundaries were identified as the preferred sites for forming protective oxide layers, compared to grain interiors. Consequently, samples with finer grains, which inherently possess a higher density of grain boundaries, exhibited enhanced passivation, resulting in greater surface coverage by protective oxide layers. In contrast, samples with larger grains primarily formed oxide layers along the grain boundaries, leaving the grain interiors more susceptible to attack by corrosive species. Additionally, a phenomenological model was developed based on the experimental results. This model was validated through independent measurements, confirming that passivation coverage increases with decreasing grain size in acidic environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".