Impact of Low-Levels of Silver, Zinc and Copper Nanoparticles on Bacterial Removal and Potential Synergy for Point-of-Use Water Treatment Solutions
Bibliographic record
Abstract
Silver (Ag) nanoparticles (NPs) are incorporated into multiple Point-of-Use (POU) drinking water solutions including ceramic water filters (CWFs).Despite silver's promise in supporting disinfection in the POU solutions, its disinfection action remains under-studied.Drawing on research in the medical field which revealed synergistic impact of co-applying Ag NPs with copper (Cu) NPs, and zinc (Zn) NPs on disinfection, this research investigates the potential for coapplying these NPs for water disinfection.The methodology consisted of using batch-test experiments to assess E. coli inactivation using combinations of Ag-Cu, Ag-Zn and Cu-Zn NPs at safe concentrations (0-0.2 mg/L) and different ratios, over typical storage period (72 hours).Bliss model was used to analyze the effect of co-application.A set of Ag NPs and combinations was challenged with pH levels of 6-9 and with natural water.The disinfection efficiency of individual NPs was Ag>Cu>Zn, with threshold levels between 0.01-0.02mg/L for Ag, 0.05 mg/L for Cu, and 0.2 mg/L for Zn.Synergy was observed between the combinations, as complete removal was achieved in 48 hours when co-applying 0.01 mg/L Ag with 0.05 mg/L Zn or Cu while individual nanoparticles resulted in no removal.The degrees of synergy were Ag-Zn>Cu-Zn>Ag-Cu.Within Ag-Zn and Ag-Cu combinations, lower Ag:Zn and Ag:Cu ratios yielded higher synergy.There was no clear correlation between pH and disinfection efficiency by Ag NPs nor the combinations except for the Zn-Cu combination which performance negatively correlated to pH level.Using natural water medium resulted in suppressed disinfection by Ag individually, Ag-Cu, and Ag-Zn, and a more variable disinfection by Zn-Cu.Together the results highlight a clear synergy between the combinations, and the Ag-Zn and Ag-Cu combinations at lower Ag:Zn/Cu ratio being the most promising for E. coli inactivation, demonstrating a good potential for use as water disinfectants, and providing additional protection of water treated by POU solutions during storage periods.Firstly, I would like to thank my supervisor, Dr. Onita Basu for giving me the opportunity to do this research and for supporting me in every step along the way.I am very blessed to have had her as my supervisor and mentor.I am also very thankful for Robbie Venis, and Chaitanya Narendrakumar Luhar who trained me on the lab research method
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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) | 0.002 | 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".