Investigating the Role of Mo and Cr during the Activation and Passivation of Ni-Based Alloys in Acidic Chloride Solution
Bibliographic record
Abstract
The elemental dissolution behaviour of commercially available Ni-based alloys was investigated in the context of surface activation, spontaneous passivation, and electrochemically-assisted passivation in hydrochloric acid solution using atomic emission spectroelectrochemistry (AESEC). Following surface activation, the spontaneous passivation of each alloy was found to proceed by the accumulation of mainly Mo-rich, but also Cr-rich oxides. An alloy’s ability to recover from surface activation was found to improve with increased Mo content. For the alloy with the lowest Mo content considered here, approximately 8 wt.% Mo, spontaneous passivation was unsuccessful and active dissolution was observed. For alloys with higher Mo contents, greater than 13 wt.% Mo, spontaneous passivation occurred quickly, and dissolution rates stabilized at values comparable to those found for the native oxide, i.e., before perturbation by an applied potential. Mo(IV) oxides were found to be the species accumulating during spontaneous passivation using ex situ X-ray photoelectron spectroscopy (XPS). During electrochemically-assisted passivation, i.e., applying a potential within the passive region, a portion of the previously accumulated Mo was removed while Cr oxides accumulated at the surface. However, based on the dissolution rates observed after electrochemically-assisted passivation, Cr-content did not dictate the barrier layer properties.
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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 | 0.001 |
| 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 | 0.001 | 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".