Bibliographic record
Abstract
Diffusion and convection to an electrode with an irreversible reaction is allegedly a much simpler mass-transport problem than the same problem for a quasireversible reaction. This seems self-evident, since in the quasireversible case the mass transport of the product species has to be included, not just the reactant species. This leads to a coupled mass transport problem, and that has to be harder, right? Well, not necessarily. Under the common assumption that the diffusivities are equal, the quasireversible concentration profiles and current density can be trivially derived from the irreversible ones. One just replaces the forward rate constant by the sum of the forward and backward rate constants, scales the result and adds a constant. What could be simpler than that? I show that this works under most of the convective diffusion situations encountered in electrochemistry. Of course there are some restrictions. On the other hand, for some cases the condition of equal diffusivities can be relaxed. I give some examples of where this is more than just a curiosity in relating known results in classic electrochemistry. In particular, it can lead to more efficient numerical analysis schemes. [1] D.A. Harrington, Electrochim. Acta., 308, 152 (2015).
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".