Bactericidal Paper Impregnated with Silver Nanoparticles for Point-of-Use Water Treatment
Bibliographic record
Abstract
There is an urgent need for cheap point-of-use methods to purify drinking water. We describe a method to deactivate pathogenic bacteria by percolation through a paper sheet containing silver nanoparticles. The silver nanoparticles are deposited by the in situ reduction of silver nitrate on the cellulose fibers of an absorbent blotting paper sheet. The aim is to achieve inactivation of bacteria during percolation through the sheet, rather than removal of bacteria from the effluent by filtration. The silver-nanoparticle containing (AgNP) papers were tested for performance in the laboratory with respect to bacteria inactivation and silver leaching as suspensions of bacteria percolated through the paper. The AgNP sheets exhibited antibacterial properties toward suspensions of Escherichia coli and Enterococcus faecalis, with log reduction values in the effluent of over log 6 and log 3, respectively. The silver loss from the AgNP sheets was minimal, with values under 0.1 ppm (the current US EPA and WHO limit for silver in drinking water). These results show promise that percolation of bacterially contaminated water through paper embedded with silver nanoparticles could be an effective emergency water treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".