Biological Activity and Nanostructuration of Fe3O4-Ag/High Density Polyethylene Nanocomposites
Bibliographic record
Abstract
We report here the synthesis of uniform nanospheres-like silver nanoparticles (Ag NPs, 5–10 nm) and the dumbbell-like Fe3O4-Ag hybrid nanoparticles (FeAg NPs, 8–16 nm) by the use of a seeding growth method in the presence of oleic acid (OA)/oleylamine (OLA) as surfactants. The antibacterial activity of pure nanoparticles and nanocomposites by monitoring the bacterial lag–log growth has been investigated. The electron transfer from Ag NPs to Fe3O4 NPs which enhances the biological of silver nanoparticles has been proven by nanoscale Raman spectroscopy. The lamellae structure in the spherulite of FeAg NPs/High Density Polyethylene (HDPE) nanocomposites seems to play the key role in the antibacterial activity of nanocomposites, which has been proven by nanoscale AFM-IR. An atomic force microscopy coupled with nanoscale infrared microscopy (AFM-IR) is used to highlight the distribution of nanoparticles on the surface of nanocomposite at the nanoscale. The presence of FeAg NPs in PE nanocomposites has a better antibacterial activity than that reinforced by Ag NPs due to the faster Ag+ release rate from the Fe3O4-Ag hybrid nanoparticles and the ionization of Ag NPs in hybrid nanostructure.
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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) | 0.000 | 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 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".