From Microscale to Road Scale: Bridging the Gaps of Predictive Aluminum Corrosion Using SECM
Bibliographic record
Abstract
Abstract Aluminum (Al) corrosion starts off at the micron or even submicron scale and if it is coating protected, it occurs at the metal‐coating interface. These corrosion events are by and large studied using bulk corrosion measurements making the understanding incomplete due to its micrometric scale occurrence. This gap is therefore targeted in current study by using a combination of SECM mapping modes together with a new strategy of employing redox‐mediator mixtures. These combinations allow the exploration of both Al surface topographic features as well as corrosion hotspots. Nine differently finished AAxxxx surfaces (namely, AA5083‐rolled‐Zr, AA6061‐rolled‐Zr, AA6061‐grinded‐Zr, AA6111‐rolled‐Zr, AA6111‐grinded‐Zr, AA7075‐grinded‐Zr, AA7075‐rolled‐Zr, AA7075‐rolled‐ZnPh with sealer and AA7075‐rolled‐ZnPh without sealer) are investigated by SECM in their as‐received state for corrosion and mapped on a 1 mm2 scale with high precision. The most interesting outcome is that typically grinded samples show more cathodic current and a higher number of hotspots. The resultant SECM maps are then quantified to extract corrosion hotspots and correlate them with both bulk corrosion outcomes and the real‐life corrosion road tests performed for 2 years. These investigations present a strong corrosion predictive strategy, which makes this study comprehensive and highly applicable to sectors like automobiles and aerospace) employing Al surfaces.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".