FE-SEM Study of Fine Nb Precipitates in Carbon Extraction Replicas
Bibliographic record
Abstract
Precipitation strengthening is an important parameter controlling the mechanical properties of low carbon steels. These precipitates are very fine and are normally analyzed using either thin foils or carbon extraction replicas under a transmission electron microscopy (TEM). In this work, field emission gun scanning electron microscope (FE-SEM) was applied successfully in the characterization of niobium (Nb) carbo-nitride (C,N) precipitates using carbon extraction replicas. FE-SEM observation of high strength linepipe steel replicas before and after aging at 400°C for 1 hr confirmed the presence of Nb(C,N) precipitates in ferrite. The FE-SEM could analyze small particles (below 50 nm) embedded in the steel but the analysis had to be carried out at low voltages to maximize spatial resolution resulting in a poor signal. However, carbon extraction replicas in the FE-SEM can be analyzed using high voltages, since the interaction volume effect is no longer a problem.
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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) | 0.002 | 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".