A New Method for Corrosion Current Measurement: the Dual-Electrochemical Cell (DEC)
Bibliographic record
Abstract
This paper presents the “dual electrochemical cell” (DEC) method, a new technique that allows monitoring of corrosion current ( i corr ) in real time with a corrosion potential E corr that may change with time. In this method, the E corr of a metal corroding in a solution containing the oxidant of interest is measured in the 1st cell. This potential is then continuously applied in real time to a second cell using the same metal electrode in the same solution but free of the oxidant, and the current of the 2nd cell, which represents the i corr of the 1st cell, is monitored. This setup allows direct measurement of i corr without having to polarize the corroding electrode away from E corr . The advantages of DEC over conventional methods are that it does not require the corroding system to be at steady state, and avoids any ambiguities associated with the extrapolation of the measured current-potential relationship to extract i corr . The i corr values obtained using the DEC method were compared with those obtained using conventional polarization techniques, using dissolved metal concentrations and surface analysis observations, and the results showed that the DEC method provides the most accurate measurement of i corr .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".