A TIME-DOMAIN APPROACH TO EXTRACTING POLARIZATION RESISTANCE FROM ELECTROCHEMICAL NOISE DATA
Bibliographic record
Abstract
Measuring the corrosion rate of a corroding metal is of interest in many situations including monitoring industrial processes and fundamental research in laboratories. The corrosion rate of a metal can be measured electrochemically by determining its polarization resistance, which is inversely proportional to the corrosion rate. What is introduced in this letter is a novel technique for extracting polarization resistance from electrochemical noise (EN) data. An advantage of this approach is that very short time records, of the order of a few seconds, can be assessed to see if they reveal a polarization resistance. The theoretical framework for this approach is based on a time-domain analysis of an electrical circuit model of an EN experimental arrangement. The analysis indicates that polarization resistance can be interpreted only if one electrode, not both electrodes, is predomi-nately generating electrochemical transients during a given time record. An algorithm for extracting polarization resistance from EN measurements is described and examples of its implementation on EN data support the features of the theoretical framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".