(Invited) Oil-Immersed Scanning Micropipette Contact Method for Long-Term Corrosion Mapping
Bibliographic record
Abstract
Scanning micropipette contact method (SMCM) suffers from the droplet evaporation and crystallization which limits the use of most saline electrolyte solutions under natural air humidity. We advanced this technique by scanning the droplet under mineral oil that was placed on the surface of substrate, which significantly improved the stability of droplet. This allowed for the use of 3.5 wt% NaCl solution to map the localized corrosion of AA7075-T73 aluminum alloy regardless of ambient humidity levels. Maps of corrosion potentials and corrosion currents extracted from potentiodynamic polarization curves showed good correlations with the surface features. We also optimized the long-term oil-immersed SMCM scanning by eliminating the Ag + contamination that was released by the commonly used non-isolated Ag/AgCl quasi-reference counter electrode (QRCE). Ag + ions were reduced at the alloy surface when they diffused to the droplet, generating unwanted cathodic current, causing the corrosion potential to shift in the positive direction over time. This work demonstrates the viability of the oil-immersed SMCM and opens up the avenue to mechanistic corrosion investigations at the microscale level using aqueous solutions that are prone to evaporation under noncontrolled humidity levels.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".