Impact of Metal Ions, Peroxymonosulfate (PMS), and pH on Sulfolane Degradation by Pressurized Ozonation
Bibliographic record
Abstract
This study investigated the degradation of sulfolane using pressurized ozonation under varying initial concentrations and the influence of different catalysts and peroxymonosulfate activation methods on the degradation efficiency. Initial sulfolane concentrations of 1 mg L−1, 20 mg L−1, and 100 mg L−1 were tested over 120 min, revealing a degradation efficiency of 73%, 41%, and 18%, respectively. The addition of various metal ions (Zn2+, Mg2+, Cu2+, Ni2+, and Co2+) demonstrated that only zinc and magnesium enhanced degradation, with zinc achieving a 92% removal efficiency and magnesium achieving 86%. Different doses of magnesium and zinc were further tested, showing optimal degradation at specific concentrations. The combination of PMS with ozonation was explored, revealing that zinc activation did not significantly enhance degradation, while NaOH activation achieved near-total degradation, with a 100 mg L−1 NaOH concentration. Varying PMS concentrations indicated that altering pH was more effective than changing PMS dosage. Finally, the impact of pH changes in both reverse osmosis water and tap water matrices confirmed that higher pH levels significantly improved degradation efficacy, achieving up to 98% removal with NaOH concentrations of 50 mg L−1 in reverse osmosis water. These results suggest that optimizing pH and catalyst type are critical for enhancing sulfolane degradation in pressurized ozonation systems.
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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) | 0.000 | 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) | 0.001 | 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".