Challenges in Quantitative Scanning Electrochemical Microscopy for Corrosion Studies of Ferrous Materials: The Role of Redox Mediator–Substrate Interactions
Bibliographic record
Abstract
A key challenge in using scanning electrochemical microscopy (SECM) in feedback mode for corrosion studies is decoupling the redox mediator's (RM) influence from the intrinsic reactivity of the substrate. In this work, macro‐ and microelectrochemical experiments are combined with finite element modeling to investigate how two widely used RMs, ferrocenemethanol (FcMeOH) and hexaammineruthenium (III) chloride ( ), affect the corrosion behavior and SECM response of iron and stainless steel (SS‐316L). The apparent rate constants extracted from SECM measurements highlight a clear dependence on substrate passivation. SECM measurements over iron revealed that the applied potential required to induce FcMeOH oxidation causes ultramicroelectrode fouling via iron oxide deposition, thereby compromising measurement reliability. In contrast, undergoes reduction at the active Fe surface, leading to local RM depletion and a feedback response characterized by a steeper current decay than typically observed over passive surfaces. On SS‐316L, negative feedback was observed for both mediators, reflecting the presence of a stable passive film. This study identifies key pitfalls in SECM corrosion analysis and demonstrates how RM–substrate interactions can affect interpretation. These findings offer practical guidance for improving the quantitative reliability of SECM in probing localized corrosion processes of ferrous alloys.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".