Using macro and micro electrochemical methods to understand the corrosion behavior of stainless steel thermal spray coatings
Bibliographic record
Abstract
Abstract High-velocity oxygen fuel thermal spray stainless steel coatings are desirable for their excellent erosion resistance. However, the fabrication process can lead to a decrease in corrosion resistance in comparison to the original bulk material. Here we produced stainless steel coatings on stainless steel substrates using varying deposition parameters to investigate the corrosion properties of the resulting composite steels and elucidate the corrosion behavior both on the macro and micro scale. Macro potentiodynamic polarization measurements carried out in corroding environments demonstrated the rate of degradation of the Fe-Cr alloy coating. After short immersion periods, the coatings showed iron-like active corroding behavior and no passivation regions on the anodic branch. Over time, the coating’s corrosion behavior began to change to signify similar results to that of pure chromium. Ex-situ electron microscopy and elemental composition revealed a Cr oxide rich layer left on the coating’s surface. Micro electrochemical techniques including scanning electrochemical microscopy and scanning micropipette contact method were employed over the coatings and powdered material, respectively, to show that the lack of protective passivity the thermal spray coatings possess is mostly inherited from the atomized powdered stainless steel material.
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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".