Kinetic Modeling of Ozone Decomposition and Peroxone Oxidation of Toluene in an Aqueous Phase Using <i>ab Initio</i> Calculations
Bibliographic record
Abstract
The application of ozone along with hydrogen peroxide, commonly referred to as peroxone oxidation, is a widely investigated technique for wastewater treatment. Degradation of ozone in water is a key step in the pollutant degradation mechanism, particularly in peroxone oxidation. However, the degradation of ozone in water is not understood at a low pH (<6). This study reveals that current ozone degradation models overestimate degradation at a low pH because the rate constants involved in the dissociation equilibrium of the hydroperoxyl radical are inaccurate. Here, the rate constants of forward and backward reactions were calculated with ab initio quantum chemical calculations computed from the CCSD (T) theory to be 1.45 × 10 3 s –1 and 8.6 × 10 7 m 3 kmol –1 s –1, respectively. After modifying the current kinetic model by using the calculated rate constants, the predictions of ozone half-lives at a low pH (<6) are improved by 1–2 orders of magnitude in pure water (without organic matter and carbonate species) in comparison with the available experimental results. The ozone decomposition kinetic model was used to develop a comprehensive kinetic model for peroxone oxidation of toluene. The results demonstrate that the new rate constants considerably improve the peroxone oxidation process as well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".