(Invited) Phenomena in Mass-Transport Electrochemical Impedance Spectroscopy at Channel Electrodes
Bibliographic record
Abstract
Microfluidic devices in general and channel electrodes in particular offer a wide variety of opportunities for analytical electrochemistry, especially as an easily manufactured rotating disk analog. In terms of this, electrochemical impedance spectroscopy as a method is a quickly recorded method that allows for fast characterization of the system. However, channel electrodes are not uniformly accessible, which greatly complicates the numerical treatment of the mass-transport to these electrodes. Using a reversible hexaammineruthenium(II/III) redox couple, the mass-transport impedance was measured, shown in Fig. 1a, and modelled using commercially available numerical software (Comsol Multiphysics ®). The results using numerical modelling were discussed in terms of common assumptions and used as a benchmark to test the validity of these approximations [1]. In addition, a new method using galvanostatic impedance spectroscopy at the upstream electrode and detecting the frequency dependent potential at a downstream electrode was discussed, Fig. 1b. Potentially, this allows for quick acquisition of geometric, kinetic and mass-transport parameters to allow quick characterization of a double channel electrode setup. [1] T. Holm, M. Ingdal, E.V. Fanavoll, S. Sunde, F. Seland, D.A. Harrington, Electrochimica Acta 202 (2016) p. 84. Figure 1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".