Combined <scp>UV LED</scp> and Chlorine for Synergistic Drinking Water Disinfection and Assessment of Disinfection By‐Product Formation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it