Use of Salicylic Acid as a Model Compound to Investigate Hydroxyl Radical Reaction in an Ozonation–Membrane Filtration Hybrid Process
Bibliographic record
Abstract
In this study, experiments were conducted using salicylic acid (SA) as a hydroxyl radical probe to determine the importance of hydroxyl reactions in ozone–membrane filtration processes. Four processes were investigated: ozonation alone, membrane filtration alone, ozonation–ceramic membrane filtration, and ozonation–iron oxide-coated ceramic membrane filtration. Experiments were conducted at two different pH values: pH ca. 2.5 and pH ca. 7.0. The results show that at pH values <3.0, SA was not removed by either the reaction with molecular ozone, filtration, or sorption. The reaction rate increased significantly at pH values >7.0, suggesting that hydroxyl radical reactions control the reaction mechanism at higher pH. The results also show that the salicylate ion sorbed to the membrane, whereas SA acid did not. Greater than 95% removal of SA was achieved with the iron oxide-coated membranes compared to 80% percentage with the uncoated membrane (ozone dosage of 0.25 mg/min, treatment time: 240 min). The presence of 2,3-dihydroxybenzoic acid (2,3-DHBA) and 2,5-dihydroxybenzoic acid (2,5-DHBA) in the permeate were confirmed using GC/MS. 2,3-DHBA was found to be the predominate byproduct. DBHA is known to form as a result of the reaction of the hydroxyl radical with SA. A reaction mechanism is suggested to explain the enhanced SA removal by the hybrid process.
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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) | 0.000 | 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".