In Operando Analysis of Passive Film Growth on Ni-Cr and Ni-Cr-Mo Alloys in Chloride Solutions
Bibliographic record
Abstract
The instantaneous kinetics of oxide formation and growth, in competition with passive film dissolution and breakdown, were investigated for Ni-22 Cr and Ni-22 Cr-6 Mo (wt%) during single step passivation at +0.2 V SCE . Experiments were conducted in selected acidic and alkaline chloride-containing environments using simultaneous AC and DC electrochemistry; including on-line Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). In parallel experiments, in-situ neutron reflectometry (NR) and ex-situ X-ray photoelectron spectroscopy (XPS) were utilized to characterize the formation of surface oxide films as a function of time. The specific roles of pH and Mo during passivation and breakdown kinetics are highlighted, providing an insight into the fate of the elements which comprise the alloys, and their effects on passivation behavior. It was observed that early oxidation of both Ni and Cr-species occurred in acidic electrolyte. Preferential dissolution of Ni 2+ at later times enabled gradual Cr 3+ enrichment within the surface film. However, greater relative stability of NiO and Ni(OH) 2 was observed in the alkaline condition. Upon alloying Ni-Cr with Mo, Cr 3+ became increasingly enriched in the surface film during anodic polarization. Oxides were interpreted to consist of non-stoichiometric solid solutions formed via solute capture.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".