Synthesis of Nanosilver Particles in the Texture of Bank Notes to Produce Antibacterial Effect
Bibliographic record
Abstract
Silver particles show antibacterial and antiseptic properties at the nanoscale. Such properties result from an alteration in the binding capacity of silver atoms in bits of less than 6.5[Formula: see text]nm which enables them to kill harmful organisms. Silver nanoparticles are now the most broadly used agents in the area of nanotechnology after carbon nanotubes. Given that currency bills are one of the major sources of bacterial disseminations and their contamination has recently been nominated as a critical factor in gastrointestinal infections and possibly colon cancers, here we propose a new method for producing antibacterial bank notes by using silver nanoparticles. Older bank notes are sprayed with acetone to clean the surface. The bank note is put into a petri-dish containing a solution of silver nitrate and ammonia so that it is impregnated. The bank notes are then reduced with the formaldehyde gas, which penetrates its texture and produces silver nanoparticles in the cellulose matrix. The side products of the reactions are quickly dried off and the procedure ends with the drying of the bank note. The transmission electron microscope (TEM) images confirmed the nanoscale size range for the formed particles while spectroscopy methods, such as XRD, provided proof for the metallic nature of the particles. Bacterial challenge tests then showed that no colonies of the three tested bacterium (Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa survived on the sample after a 72[Formula: see text]h incubation period. This study has provided a method for synthesizing silver NPs directly into the texture of fabrics and textiles (like that of bank notes) which can result in lower production costs, making the use of silver NPs economically beneficial. The method, specifically works on the fabric of bank notes, suggesting a method to tackle the transmission of bacteria through bank notes. Moreover, this study is a testament to the strong antibacterial nature of even low concentrations of silver NPs.
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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".