Photodegradation of micropollutants by vacuum-UV (VUV) radiation in potable reuse waters: Promotive and inhibitory effects of free chlorine oxidant
Bibliographic record
Abstract
The occurrence of a variety of harmful micropollutants in potable reuse trains has been a matter of concern.In this study, the performance of the vacuum-ultraviolet / ultraviolet advanced oxidation process (VUV/UV AOP) without and with free chlorine (VUV/UV/Cl 2 AOP) for elimination of carbamazepine (CBZ) and 1,4-dioxane (1,4-D) from potable reuse systems was evaluated.The addition of free chlorine improved CBZ degradation by ~12 % while reducing the rate of 1,4-D removal by ~50 %, highlighting the selective effectiveness of the VUV/UV/Cl 2 AOP based on micropollutant type.The effects of operational conditions, including free chlorine dosage, solution pH and contributions of different radical species, were also determined.Both high chlorine concentrations as well as alkaline pH were shown to slow down the removal of both contaminants in the VUV/UV/Cl 2 .Also, the VUV/UV/Cl 2 AOP was considered to be promising for treatment of 1,4-D in reverse osmosis (RO) permeate due to photochemical chain reactions between the added chlorine and water constituents which lead to effective formation of Cl 2•-.The findings of this research demonstrate the broader potential of the VUV/UV/Cl 2 AOP as a tailored, adaptable solution for optimizing micropollutant removal in potable reuse systems, based on contaminant type and water matrix.
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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".