Direct observation of the influence of grain orientation on the corrosion of pipeline steels
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Bibliographic record
Abstract
This study aims to correlate the crystallographic texture of pipeline steels with their corrosion resistance. Electron backscatter diffraction (EBSD) was used to investigate the impact of grain orientation on the corrosion behavior of steels in various corrosive environments. Corrosion is influenced by several microstructural features, such as phase composition, grain size, grain boundary character, dislocation density, and crystallographic texture. To analyze the influence of specific grain orientations on corrosion resistance, steel samples were subjected to planned thermomechanical treatments to obtain samples with similar microstructures but different crystallographic textures. These samples were then exposed to controlled corrosive environments, and their microstructural changes were mapped using EBSD, scanning electron microscopy, and optical 3D profilometry. The results obtained indicated that corrosion response depends on grain orientations. Notably, the steel with more 〈111〉//ND and 〈100〉//ND grains exhibited improved corrosion resistance compared to the steel with a more random texture. The resistance of grains to active dissolution in all the corrosive solutions was in this order: {110} < {111} < {100}. The corrosion products were analysed using XPS, revealing variations in the composition and nature of the surface films on the samples across the selected electrolytes. • The corrosion behavior of specific grain orientations in pipeline steels was assessed in various environments. • Steel samples with similar microstructures but different crystallographic textures were examined. • SEM, EBSD, XPS and 3D optical profilometry were employed to map texture and grain-specific corrosion behavior. • <111>//ND and < 100>//ND texture exhibited superior corrosion resistance than random texture. • Corrosion resistance followed {110} < {111} < {100}, with <100>//ND being the most resistant to dissolution.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it