Distribution of relaxation times analysis of rotating disk electrode impedance spectra
Bibliographic record
Abstract
Distribution of relaxation times (DRT) is a valuable analytical tool to identify and quantify individual contributions in electrochemical impedance spectroscopy (EIS) data. This study introduces the DRT method for rotating disk electrode (RDE) measurements using the example of the oxygen reduction reaction (ORR) in alkaline media. A comprehensive peak assignment is presented based on the variation of different parameters, such as the oxygen saturation level in the electrolyte, the rotation rate, and the current density. Two prominent peaks in the low-frequency area are attributed to the oxygen mass transport and the charge transfer of the ORR. Additionally, three minor high-frequency peaks are identified. This assignment may serve as a reference for other researchers who intend to employ DRT for the RDE, regardless of the investigated reaction. Furthermore, a direct correlation between the Koutecký-Levich evaluation and the DRT analysis is demonstrated, indicating that DRT can be used similarly to the Koutecký-Levich analysis to extract kinetic information. This enables the possibility of performing and evaluating long-term in-situ steady-state measurements where the conventional Koutecký-Levich analysis is not applicable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".