On the surface segregation of Sn in cold-rolled Fe under continuous annealing conditions
Bibliographic record
Abstract
The surface segregation of Sn in cold-rolled Fe-0.03Sn and Fe-0.01Sn (at.%) model alloys was investigated for annealing parameters characteristic of continuous galvanizing lines (CGLs). The most significant increase in surface coverage occurred during linear heating between 500 and 675°C, where no significant change in segregation was observed with isothermal holding for 60 – 480 s at peak annealing temperatures of 675 – 825°C. While the bulk diffusion of Sn in Fe determined the segregation rate during extended isothermal holding up to 10800 s, it could not account for the rapid increase in coverage during heating. It was determined that Sn segregation was accelerated during linear heating by rapid diffusion along dislocation pipes in the cold-rolled starting microstructure. Integrating the decrease in diffusivity due to recrystallization into the McLean model for interfacial segregation resulted in an experimentally verified description of segregation kinetics during linear heating. It was also able to predict the experimentally observed increase in segregation when the linear heating rate between 500°C and 675°C was decreased from 5 to 1°C/s. No significant difference in surface segregation was observed between the 0.01 and 0.03 at.% Sn addition for isothermal holding of 480 s or less, which can be explained by the saturation of easy adsorption sites. A bond-breaking model was used to illustrate their origin from imperfect coordination within the surface layer. As significant surface segregation can be achieved within the CGL processing window, Sn microalloying appears as a promising strategy to improve galvanized coating quality.
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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 | 0.000 | 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) | 0.003 | 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".