Resolving the Dependence of Aluminum Alloy Corrosion on Grain Orientation by Scanning Electrochemical Cell Microscopy
Bibliographic record
Abstract
The study of grain-dependent corrosion behaviors of practical polycrystalline metals remains challenging due to the influences of other microstructural features, such as intermetallic particles and grain boundaries. In this work, we employed the oil-immersed scanning electrochemical cell microscopy (SECCM) to investigate the effect of grain orientations of a practical aluminum alloy AA7075-T73 on the surface electrochemical behaviors. Thousands of spatially resolved microscopic potentiodynamic polarization (PDP) measurements were performed, allowing the extraction of information only from grain interior areas excluding intermetallic particles and grain boundaries. The differences in the corrosion behaviors between grains were revealed. Cathodic currents exhibited a strong grain orientation dependence with a decreasing order of {101} > {001} > {111}, agreeing with the prediction from the order of atomic planar density. By contrast, the dependence of anodic currents on grain orientation was weak, and pitting was independent of grain orientation, which could be due to the limited mass transport of ions within the surface oxide film. This work highlights the capability of oil-immersed scanning electrochemical cell microscopy in resolving small electrochemical differences, which will greatly promote the study of grain-dependent behaviors of practical polycrystalline samples.
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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".