Modeling study for oscillatory reaction of chlorite – iodide – ethyl acetoacetate
Bibliographic record
Abstract
Chlorine dioxide based chemical oscillating behavior was modeled by a simple scheme consisting of three component reactions. Furthermore, little is known about the influence of the pH value. In this study, four component reactions were used to model the chlorite – iodide – ethyl acetoacetate oscillating reaction by dynamic analysis software. The oscillatory phenomenon is observed for concentration changes of triiodide ion, chlorite ion, and hydrogen ion. The initial concentration of ethyl acetoacetate, chlorite ion, iodide ion, and hydrogen ion has great influence on oscillations. The amplitude and number of oscillations are associated with the initial reactant concentrations. The equation of the reaction rate of triiodide ion, chlorite ion, or hydrogen ion changing with reaction time and initial concentrations in the oscillation stage was obtained. The bifurcation surface between oscillatory and nonoscillatory behavior with different pH values was obtained. The spatial zone for the occurrence of oscillation is reduced with an increase in the pH value. The range of oscillation as concentrations of chlorine dioxide, iodine, and ethyl acetoacetate is well described by an equation. There is a lower limit on ethyl acetoacetate initial concentration for oscillation. However, there is a higher limit on chlorine dioxide and iodine concentration for oscillation. The concentrations of chlorine dioxide and iodine for oscillation decrease with an increase in the pH value. The results provide new theoretical evidence of the importance of pH value, which can affect the bifurcation surface between oscillatory and nonoscillatory behavior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".