Role of Nb in 13Cr super-martensitic stainless steel
Bibliographic record
Abstract
The effect of Nb microalloying on structure and physical properties of quenched and tempered 13%Cr martensitic stainless steel was investigated. Excellent strength and adequate toughness properties were obtained by 0.10 wt% Nb addition to low interstitial (N 0.01wt%, C < 0.02wt%) steel. The effect of Nb in 13%Cr steels with high N content was also studied in a commercial martensitic stainless steel sample containing higher levels of N and also alloyed with V. The microstructure, precipitate morphology and dispersion and volume fraction of reverse austenite were characterized. The strength properties obtained in the steel with 0.10%Nb are significantly higher than those of the V-containing steel. The study shows that whereas amount, size and dispersion of precipitates of microalloying elements contribute to enhanced strength, the optimum volume fraction of reverse austenite formed contributes to enhanced ductility and toughness properties. More importantly, high Nb additions to low N -13%Cr-1%Mo steel are found to improve significantly resistance against pitting corrosion significantly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".