Determination of the local corrosion rate of magnesium alloys using a shear force mounted scanning microcapillary method
Bibliographic record
Abstract
The successful development of scanning probe techniques to characterize corrosion in situ using multifunctional probes is intrinsically tied to surface topography signal decoupling from the measured electrochemical fluxes. One viable strategy is the shear force controlled scanning microcapillary method. Using this method, pulled quartz micropipettes with an aperture of 500 nm diameter were used to resolve small and large variations in topography in order to quantify the local corrosion rate of microgalvanically and galvanically corroded Mg alloys. To achieve topography monitoring of corroded surfaces, shear force feedback was employed to position the micropipette at a reproducible working height above the substrate. We present proof of concept measurements over a galvanic couple of a magnesium alloy (AE44) and mild steel along with a microgalvanically corroded ZEK100 Mg alloy, which illustrates the ability of shear force to track small (1.4 μm) and large (700 μm) topographic variations from high aspect ratio features. Furthermore, we demonstrate the robustness of the technique by acquiring topographic data for 4 mm along the magnesium-steel galvanic couple sample and a 250 × 30 μm topography map over the ZEK100 Mg alloy. All topography results were benchmarked using standard optical microscopies (profilometry and confocal laser scanning microscopy).
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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 | 0.001 | 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".