The Diffusion Potential and Impedance Behaviour Associated with an Anodic Metal Dissolution Reaction
Bibliographic record
Abstract
The authors have read many textbook formulations of the DC and AC response of an anodically dissolving metal electrode (with no extraneous cathodic reactions such as water reduction), and we believe - rightly or wrongly - that most of these miss one or more important points. Under steady state DC anodic dissolution conditions, we can assume that the net anodic current density is affected by the basic kinetic parameters of the partial reactions, and by the concentration of the product cations at the electrode. One outcome of such an assumption is that the reaction may appear to obey Tafel's law, but with a different (apparent) b value from that of either the dissolution or deposition reaction taken individually. This is a very easy derivation, but does not appear in most sources. For example, dissolution of Ag in aqueous perchloric acid at room temperature shows an apparent b value of about 60 mV, yet it is well known that a rate-determining single electron transfer should give a b value of about 120 mV, for either direction of the reaction. For AC measurements, one should beware of using the wrong formulation of the "Warburg" impedance. Most textbook derivations are for dissolved species only (such as ferricyanide and ferrocyanide mixtures), not for a process involving a solid metal electrode. A very simple model can be derived using similar assumptions to the DC case, and can be tested experimentally with good results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".