Nernst–Planck analysis of propagating reaction-diffusion fronts in the aqueous iodate–arsenous acid system
Bibliographic record
Abstract
Propagating fronts can be generated in solution by combining diffusion and chemical reactions with an autocatalytic feedback mechanism. Front propagation is usually analyzed in terms of the rate equations for the chemical reactions and Fick's laws of molecular diffusion. In practice, however, reaction-diffusion fronts are known mainly for aqueous electrolyte solutions. A more accurate description of front propagation in these systems is developed by using Nernst-Planck (NP) transport equations. This treatment includes diffusion fluxes driven by the concentration gradients and, for the ionic species, the migration fluxes driven by the electric field which is generated internally by the diffusion of ions of different mobility. NP equations are used to describe propagating fronts for the iodate oxidation of aqueous arsenous acid. The analysis provides a detailed picture of front structure and propagation, including concentration profiles, reaction rate profiles and velocity profiles for the solution species. After a short induction period, fully-developed fronts reach steady velocities and the profiles across the fronts transformed from laboratory coordinates to the frame of reference moving with the front become time-independent. The velocities of the autocatalytic I(-) ions ahead of the fronts are nearly identical to the steady front velocities. Electric fields generated by ionic diffusion across the fronts reach maximum strengths of about 0.4 V cm(-1), producing ion migration velocities as large as 50% of the front velocities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".