Silver and zinc oxide nanoparticle disinfection in water treatment applications: synergy and water quality influences
Bibliographic record
Abstract
Abstract The synergistic potential of silver and zinc oxide nanoparticles for water disinfection was investigated herein. By causing cell death through membrane interactions, oxidative killing, and DNA deactivation, metallic nanoparticles may be integrated with point-of-use water treatment systems for applications in rural and remote geographies. Disinfection efficacy was evaluated in batch-phase experiments under both synthetic and real water conditions, where synthetic water was varied by pH and dissolved oxygen levels. Ceramic pot filters with comparative nanoparticle concentrations were also investigated. In all cases, combinations of silver and zinc nanoparticles resulted in improved disinfection in comparison to either metal in isolation. In batch experiments, dissolved oxygen proved to be particularly impactful, with kinetic rates reducing approximately 45% when in low oxygen environment (<3 mg/L) versus high oxygen (>8 mg/L). Log removal values (LRVs) were further, on average, 31% lower in real water than synthetic water after 300 min, though silver–zinc combinations were still superior to either metal alone. In filters, those impregnated with 67% silver and 33% zinc achieved average LRVs of 2.7 and 2.9 after 60 min of filtration and 24 h of storage, respectively, while those with only silver achieved average LRVs of 2.0 and 3.1 at those same times.
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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 | 0.001 | 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".