Scanning Micropipette Contract Method Measurement of Aluminum Alloy: Effect of Approach Parameters on Corrosion Potential
Bibliographic record
Abstract
Scanning electrochemical cell microscopy (SECCM) enables direct electrochemical measurements at microscopic sits by scanning a droplet cell over a substrate surface. Scanning micropipette contact method (SMCM) is a type of single-channel SECCM. It has been used to record the spatially resolved electrochemical activities across metal surfaces to investigate corrosion at the (sub)microscale. In SMCM, the applied potential during the approach of micropipette to the substrate (E appr ), generates a transient current upon droplet contact with the substrate. Once the transient current exceeds a set threshold, the micropipette is automatically halted. In the investigation of aluminum alloy, we found that E appr affected the subsequent measurements of corrosion potential (E corr ) in the open circuit potential (OCP) and potentiodynamic polarization (PDP), which was considered to be inconsequential previously. For aluminum alloys, the dense oxide film restricts the surface conductivity, increasing the difficulty of droplet landing. This leads to pipette-substrate contact and droplet-substrate contact landings using different E appr . Additional oxygen flux from the droplet-oil interface in the droplet-substrate contact resulted in more positive E corr (OCP). In the anodic PDP at a high scan rate of 100 mV/s, E corr (PDP) moved away from E corr (OCP) to larger extent as E appr increased to more cathodic values. The systematic interpretation of the effect of E appr will promote the understanding of SMCM measurement especially in the field of metal corrosion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".