Comparison Between Electrochemical Noise and Electrochemical Frequency Modulation Measurements during Pitting Corrosion
Bibliographic record
Abstract
The electrochemical noise (EN) and electrochemical frequency modulation (EFM) techniques have been compared for their use to detect pitting corrosion. To do this, experiments on different corroding systems showing passivation and pitting corrosion were carried out. These corroding systems were: (1) Aluminum in borate buffer solution with and without chlorides (pitting corrosion + passivation behavior), (2) AISI 304SS in 0.3 wt.% FeCl3 solution and 6 wt.% FeCl3 solution at room temperature (passivation behavior + pitting and/or crevice corrosion), and (3) AISI 304SS in 6 wt.% FeCl3 at elevated temperature of 57 °C (pitting corrosion). Both EN and EFM were measured on-line meanwhile changing the corrosive environment by adding chlorides or by increasing the temperature. A potential perturbation composed of two sine waves is applied with the help of EFM to get current response at various frequencies. As the corrosion process is nonlinear in nature, the ac-response contains components at harmonic and intermodulation frequencies. Analysis of current components at different frequencies yields the information about the corrosion behavior under investigation. EFM may be considered to detect pitting initiation and its further development due to the results obtained by measuring the so-called "causality factors", which are the ratio of the current components in the ac-response.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".