Simple and green technique for sequestration and concentration of silver nanoparticles by polysaccharides immobilized on glass beads in aqueous media
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Bibliographic record
Abstract
BACKGROUND: Engineered nanoparticles have unique properties compared to bulk materials and their commercial uses growing rapidly. They represent a potential risk for environment and health and could be eventually released in water. Silver nanoparticles (Ag NP) are applied in various products and are well-known for their antibacterial properties. Nowadays, pre-concentration and separation methods for Ag NP possess some limitations. Here, we present a simple, green method to sequestrate and concentrate Ag NP from different aqueous media. RESULTS: Supported polysaccharides on glass beads synthesized in water by a single step reaction show high sequestration capacity of citrate-coated Ag NP in aqueous media. Supported polysaccharides were characterized by infrared spectroscopy, scanning electron microscopy (SEM) and elemental analysis. Sequestration of 83.0 % of Ag NP was attained from a 20 μg.L(-1) aqueous solution with supported chitosan in water whereas supported 2-hydroxyethylcellulose (HEC) reached 64.0 % in synthetic seawater in 2 h. The influence of polymer/glass beads ratio and molecular weight of polysaccharides was also studied. The effect of the salinity and humic acids on sequestration of Ag NP was investigated. Supported polymers have shown high performance for sequestration of ionic silver. Sequestration of 82.5 % and 80.8 % were obtained from a 60 μg.L(-1) silver ion (as nitrate salt) with supported HEC and chitosan, respectively. Sequestrated Ag NP was characterized with transmission electron microscopy (TEM) where images showed Ag NP with unchanged size and shape. CONCLUSIONS: This sequestration method, involving green synthesis, allows efficient concentration and characterization of Ag NP from different aqueous media. This simple and fast method is a potential sustainable technique for elimination of Ag NP and ionic silver from waste waters and waters at different salinities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it