Relationship between Metallurgical States and Corrosion Resistance of Nitrided Martensitic Steels in Marine Environment
Bibliographic record
Abstract
The objective is focused on the comprehension of the relationship between the metallurgical state and the functional properties of nitrided martensitic stainless steels. Nitriding is performed by a low temperature plasma treatment (Direct Current mode) in various conditions. Metallurgy of the nitrided layer was characterized at different scales, from the macroscopic to the nanometric ones. An increase of the thickness of the nitrided layer with temperature of the plasma treatment is observed. Precipitation of CrN and others nitrided phases in relation with crystallographic defects is observed. Moreover, the TEM analyses on FIB samples extracted from the surface highlight the formation of a succession of different nitrided layers depending on the nitrogen content : expanded martensite, iron-nitride phases [2]. The electrochemical properties of nitrided samples are also assessed by voltammetry test and extended immersion test. Results highlight differences on the corrosion behaviour of the nitrided X17CrNi16-2 steels with temperature of the plasma treatment. The reactivity evolution is linked to the formation of a “composite” system composed of different layers. SECM was used to understand the reactivity evolution through these layers, and successive surface grinding permits to corroborate the reactivity evolution and to understand the origin of the corrosion resistance evolution after nitriding process. [1] H.E. Boyer, T.L. Gall. Metal Handbook, Desk edition, ASM, Ohio, USA, 1986 [2] J. Yang &al. Materials and Design, Vol. 32 (2011) 808-814
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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".