Oxidation of phenol in a bioremediation medium using chlorine dioxide
Bibliographic record
Abstract
Abstract BACKGROUND: High concentrations of phenol in wastewater are difficult to remove by purely biological methods. Chemical oxidation is one way to treat high concentrations of phenol but complete oxidation will make the treatment process uneconomical. For the purpose of integrating chemical and biological treatment, the oxidation of phenol using chlorine dioxide was investigated in a medium suitable for bioremediation. The effects of chlorine dioxide concentration (500 to 2000 mg L−1), temperature (10 to 40 °C) and pH (3 to 7) on the oxidation of 2000 mg L−1 of phenol were determined. RESULTS: Chlorine dioxide concentration was found to be the dominant parameter for the removal of phenol in the nutrient rich medium. The optimal concentration of chlorine dioxide to completely oxidize 2000 mg L−1 of phenol was 2000 mg L−1. Compared with Fenton's reagent, half as much chlorine dioxide was needed to oxidize 2000 mg L−1 phenol. The reaction of chlorine dioxide with phenol was very fast and reached equilibrium within 10 min. The main oxidation products were identified as 1,4‐benzoquinone and 2‐chloro‐1,4‐benzoquinone. CONCLUSION: Compared with Fenton's reagent, chlorine dioxide is a superior oxidant for removal of phenol from both pure water and bioremediation medium. Copyright © 2010 Society of Chemical Industry
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".