Kinetic Modelling of Phenol Oxidation in a Bioremediation Medium Using Fenton's Reagent
Bibliographic record
Abstract
The present study is aimed at kinetic modeling of phenol oxidation using Fentons reagent in a medium suitable for bioremediation of organic pollutants. Batch experiments were conducted to study the effects of H2O2 concentration (29.26 to 146.31 mM), temperature (5 to 35°C), and to compare the oxidation of phenol in a bioremediation medium to that in pure water. The reaction mechanism used for kinetic modeling is based on the intermediate oxidation products identified in this study using LC-MS and ion chromatography. Progress of the chemical oxidation by Fentons reagent was monitored by determining the residual phenol concentration and concentrations of evolved intermediate compounds (catechol and hydroquinone) at regular time intervals. The rate of phenol oxidation and ultimate conversion of phenol were found to increase with increase in hydrogen peroxide concentration. The increase in temperatures has a positive effect on phenol oxidation and the rate of phenol oxidation was found to increase with temperature in the range of 5-35°C. Kinetic parameters, namely rate constants and activation energies for reactions involved, were determined by best-fitting the experimental data to the proposed reaction model. The values of the rate constants for oxidation of phenol and intermediate compounds, k1 (phenol to catechol), k2 (phenol to hydroquinone), k3 (catechol to maleic acid), k4 (hydroquinone to maleic acid) at 25°C were found to be 7.02x10-5±4.63x10-5, 7.22x10-4±6.09x10-4, 1.82x10-4±1.08x10-4, 1.68x10-3±1.29x10-3 L/mM min, respectively.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".