New method for sequestration of silver nanoparticles in aqueous media: in route toward municipal wastewater
Bibliographic record
Abstract
BACKGROUND: Nanomaterials are widely used in industry for their specific properties. Silver nanoparticles (Ag NPs) are largely used in several consumer products notably for their antibacterial properties and will likely be found in wastewater, then in the receiving environment. The development of a product capable to sequestrate those released contaminants is needed. Under environmental conditions, the biopolymer chitosan can generally coordinate the cationic metals. Ag NPs present unique properties due to their high surface/mass ratio which are promising for their sequestration. RESULTS: The immobilization of chitosan on functionalized silica assisted by microwaves gives a sequestering agent of silver while being easily recoverable. The IR spectrum confirmed the immobilization of N,N-dimethylchitosan (DMC) on silica core. The immobilized DMC gave a percentage of sequestration of Ag NPs (120 µg L(-1)) in nanopure water of 84.2 % in 4 h. The sequestration efficiency was largely dependent of temperature. By addition of hydrosulfide ions, the percentage of sequestration increased up to 100 %. The immobilized DMC recovered a large portion of silver regardless the speciation (Ag NP or Ag(+)). In wastewater, the immobilized DMC sequestered less Ag NPs (51.7 % in 97 % wastewater). The presence of anionic (sodium dodecyl sulfate and sodium N-lauroylsarcosinate) and non-ionic surfactants (cetyl alcohol) increased the hydrophobicity of Ag NPs and decreased the percentage of sequestration. CONCLUSIONS: The immobilized DMC is a promising tool for sequestrating such emerging pollutant at low concentrations in a large volume of sample that would allow the characterization of concentrated Ag NPs by transmission electron microscopy. The efficiency of the support to sequestrate would likely be influenced by the chemical environment of the Ag NP solution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".