Probing Passivating Porous Films by Scanning Electrochemical Microscopy
Bibliographic record
Abstract
Porous films are ubiquitous in electrochemistry. They frequently form on active electrodes due to the precipitation of insoluble reaction products. They can have beneficial effects, like the protection from electrochemical corrosion, or be of parasitic nature, as in the poisoning of fuel cell air cathodes. The effects of such layers on the electrochemical response of the substrate can be probed by Scanning Electrochemical Microscopy (SECM). Herein, we present modifications to the conventional analytical expressions for SECM microelectrode approach curves, to account for the effects of a porous layer. The modified expressions can be used to fit experimental approach curves and obtain film thickness and porosity parameters. Their performance is demonstrated through comparison to results obtained by finite element modeling, and by fitting experimental approach curves over well-defined filter membranes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".