Interface Segregation and Nitrogen Measurement in Fe–Mn–N Steel by Atom Probe Tomography
Bibliographic record
Abstract
Improved understanding of the interactions between solutes and the austenite/ferrite interface can benefit modeling of ferrite growth during austenite decomposition, as the transformation kinetic is significantly affected by solutes that influence interface mobility. Solute-interface interactions dominate solute segregation at the interface in binary systems, but in multi-component alloys, solute-solute interactions may also affect segregation. In this study, interface segregation in Fe-Mn-N is examined and compared with Fe-Mn-C, to reveal the extent to which C affects the segregation of Mn. Atom probe tomography (APT) is well-suited to analyze solute concentrations across the interface, as this technique combines high spatial resolution and compositional sensitivity. Measurements of Mn show that segregation is only observed for Fe-Mn-C. This demonstrates that Mn segregation is primarily driven by an affinity for C, which also segregates to the interface. However, the measurement of N in steels by APT may be affected by a variety of experimental factors. Therefore, in verifying the Fe-Mn-N result, systematic examination is conducted on the influence of pulsing method (voltage versus laser), sample preparation (ion milling versus electropolishing), and vacuum storage on the measured N concentration. Both laser pulsing and focused ion beam sample preparation are observed to decrease the apparent N concentration.
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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 | 0.000 |
| 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) | 0.001 | 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 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".