Manipulation of particle microorganism association for improved UV disinfection of surface waters
Bibliographic record
Abstract
The performance of ultraviolet (UV) disinfection is subject to the quality of water supplied to the reactor, which could often be poor in facilities lacking adequate pre-treatment. Particulate matter in low-quality water can interfere UV disinfection by shielding microorganisms from light through particle-microorganism (p-m) associations. This study investigates Zeta Potential (ZP) manipulation as a pre-treatment to improve UV effectiveness by reducing p-m associations. ZP manipulation is hypothesized to free microorganisms from particulate attachments, increasing their UV vulnerability. Water samples from a drinking water treatment plant applying UV disinfection without pre-filtration were altered for ZP, achieving five different ZP levels. A protocol was developed to distinguish between viable microorganisms attached to particles and free-floating microorganisms. UV experiments were conducted to establish the relationship between UV efficiency and ZP. Results indicated that neutral ZP results in the highest p-m association and lowest disinfection achieved. Disinfection kinetic studies revealed that highly negative ZP enhanced UV efficiency as delivered UV dose increased due to dominant repulsive forces. This study demonstrated that optimizing ZP effectively controlled the degree of p-m association for both viruses and bacteria, which could be a viable approach for mitigating p-m association and leveraged for advancements in UV disinfection.
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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".