Probing passivity of corroding metals using scanning electrochemical probe microscopy
Bibliographic record
Abstract
Abstract Passive films are essential for the longevity of metals and alloys in corrosive environments. A great deal of research has been devoted to understanding and characterizing passive films, including their chemical composition, uniformity, thickness, porosity, and conductivity. Many characterization techniques are conducted under vacuum, which do not portray the true in‐service environments passive films will endure. Scanning electrochemical probe microscopy (SEPM) techniques have emerged as necessary tools to complement research on characterizing passive films to enable the in situ extraction of passive film parameters and monitoring of local breakdown events of compromised films. Herein, we review the current research efforts using scanning electrochemical microscopy, scanning electrochemical cell microscopy (or droplet cell measurements), and local electrochemical impedance spectroscopy techniques to advance the knowledge of local properties of passivated metals. The future use of SEPM for quantitative extraction of local film characteristics within in‐service environments (i.e., with varying pH, solution composition, and applied potential) is promising, which can be correlated to nanostructural and microstructural features of the passive film and underlying metal using complementary microscopy and spectroscopy methods. The outlook on this topic is highlighted, including exciting avenues and challenges of these methods in characterizing advanced alloy systems and protective surface films.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".