Impact of low levels of silver, zinc and copper nanoparticles on bacterial removal and potential synergy in water treatment applications
Bibliographic record
Abstract
Abstract BACKGROUND Point‐of‐use filtration units often incorporate silver as a disinfection aid. However, system performance and robustness may be increased by considering co‐application of additional transition metal nanoparticles (NPs) to support treated water disinfection. In this paper, the use of silver (Ag), zinc (Zn) and copper (Cu) NPs as disinfectants within drinking water applications was explored. Disinfection efficiency against E. coli was investigated over 72 h in batch‐phase experiments using NP concentrations within or lower than drinking water limits. Concentration ranges of the NPs were from 0 to 200 μg L−1, reflecting typical concentration reports for Ag in similar applications. Samples were examined with respect to pH and two water types. The effect of co‐application of NPs was assessed for potential synergy using the Bliss model, which compares individual treatment performance to combined treatment results. RESULTS Disinfection efficacy when applying NPs individually was Ag > Cu > Zn with, for instance, complete removal (≥3 log) of E. coli observed with 50 ppb Ag at 24 h, 1 log removal by Cu and no removal achieved with application of Zn. The Bliss model analysis demonstrated the co‐application of NPs resulted in synergistic behavior with the combinations. Zn‐containing combinations (Ag–Zn and Cu–Zn) were significantly more synergistic than the Ag–Cu combination. CONCLUSIONS Low‐level (0–200 ppb) concentrations of Ag, Cu and Zn demonstrated effective bacterial control and disinfection for E. coli under various water quality scenarios. In particular, co‐application of transition metal NPs increased system robustness and synergy, demonstrating potential for disinfection with water treatment applications. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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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 | 0.001 |
| 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".