Combined <scp>UV LED</scp> and Chlorine for Synergistic Drinking Water Disinfection and Assessment of Disinfection By‐Product Formation
Bibliographic record
Abstract
ABSTRACT This study provides a comprehensive evaluation of the effectiveness of 280 nm UV LEDs in enhancing chlorine disinfection in natural water sources. The results of this work indicate sequential treatments (UV‐chlorine and chlorine‐UV) further enhanced the disinfection efficiency of T1 in natural waters, especially at higher UV doses, such as 40 mJ cm −2 . The log reduction value for the chlorine‐UV sequence reached 6.65, slightly higher than the 6.29 for the UV‐chlorine sequence, suggesting that sequencing influences disinfection efficacy at higher fluences. UV LED‐enhanced chlorine disinfection did not significantly alter concentrations of THMs and HAAs. Overall, the findings of this study open new avenues for the application of UV LEDs in drinking water disinfection, demonstrating their potential as an alternative to traditional disinfection methods. Future research should further explore the effects of UV‐chlorine combined treatments under different water quality conditions to optimize disinfection processes and provide theoretical support for innovation in water treatment technologies.
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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".