Evaluation of Electrocatalytic Ozonation Process for Hydroxyl Radical Production
Bibliographic record
Abstract
This paper seeks to evaluate the effect of reaction parameters on iron electrolysis-catalyzed ozonation (ECO) performance as a promising approach for micropollutant removal. ECO is proposed to be an environmentally and economically suitable technology for the removal of biologically recalcitrant organics in wastewater. In this process, iron ions generated via electrolysis of low-carbon steel react with dissolved ozone to produce hydroxyl radicals. The removal of tert-Butyl alcohol (TBA) was selected as a performance indicator based on its significant resistance to direct ozonation compared to hydroxyl radicals, such that TBA removal denotes catalytic breakdown of ozone. TBA removal was measured with an HS-SPME-GC-MS method for precise quantification. ECO performance ranged from 7 to 77% TBA removal (from 0.73 mM initial concentration), varying depending on the tested levels of initial pH of 5, 7, and 9, applied current between 0.065 and 0.470 A, and ozone supply rate between 3.9 and 6.4 g/h. Performance was generally increased by pH, applied current, and ozone generation, converging at high applied current rates. The most efficient use of ozone was observed at pH 9 and 0.323 A, removing 32.2% of TBA per gram of ozone supplied.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".