Cyclic potentiodynamic passivation of <scp>316L</scp> stainless steels of different crystallographic orientation produced by laser powder bed fusion: Towards the improvement of corrosion resistance
Bibliographic record
Abstract
Abstract The influence of cyclic potentiodynamic passivation (CPP) of 316L stainless steels (SS) of different crystallographic orientation produced by laser powder bed fusion (LPBF) on the resulting general and pitting corrosion resistance is discussed. CPP was performed by cyclic voltammetry in aqueous 0.1 M NaNO 3 . Electrochemical tests including open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and linear potentiodynamic polarization were employed to evaluate the resulting corrosion properties of the surfaces in aqueous 3.5 wt.% NaCl. It was found that the CPP method enables the formation of a passive oxide surface film which significantly improved the materials' general and pitting corrosion resistance in comparison to the naturally‐formed passive film under the experimental conditions investigated. It was also found that the general corrosion resistance, for both the unmodified (naturally‐passivated) and CPP‐modified LPBF 316L samples, decreased in the order of {111} > {100} > polycrystalline > {110}. Although the CPP‐modified samples showed a significantly lower current in the passive region and higher pitting potentials, in comparison to the unmodified samples, their crystalline structure was found not to have any influence on the corresponding behaviours.
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".