On the Capability of In-Situ Exposure in Scanning and Auger Electron Spectroscopy for Investigating Corrosion Property of Engineering Alloys
Bibliographic record
Abstract
Background: Potentiodynamic polarization experiments were performed on Alloys IN601 and C22 to evaluate their susceptibility to localized corrosion in hydrochloric acid (HCl) at pH2. The tested specimens were evaluated by auger electron spectroscopy (AES) and scanning microscopy (SEM). Nickel chromium alloys consist of surface oxide layers and corrosion products with metallic materials beneath. Methods: Electrochemical techniques are used to examine the pitting tendencies of specimens of IN601 and C22 immersed in a concentrated hydrochloric acid solution (pH2). Techniques include potentiodynamic anodic polarization to determine the active-passive characteristics of alloys and scanning, X-ray, and Auger electron spectroscopy to characterize specimen surfaces in terms of pitting morphologies and for surface analysis of oxide layers. Results: This paper examines the effect of HCl solution on the pitting behaviour of these alloys. Results show that IN601 alloy was characterized by more pitting than C22. Conclusion: There are deeper oxide layers and corrosion products formed on sample surfaces of alloys C22 than in IN601. Furthermore, C22 exhibits a smaller hysteresis loop than IN601, thereby indicating that it has no pitting corrosion in concentrated HCl than IN601. Keywords: Analysis, corrosion, C22, IN601, morphology, oxide layer, pits.
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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".