Crystallization behavior of polypropylene/silver nanocomposites using polyethylene glycol as reducing agent and interface modifier
Bibliographic record
Abstract
In this work, isotactic polypropylene (iPP) nanocomposites were prepared containing silver nanoparticles (Ag-NPs) with a novel and easy method, using polyethylene glycol (PEG) as reducing agent and surface modifier. Ag-NPs were prepared using different amounts in weight of silver nitrate into PEG to induce the formation of Ag-NPs. PP/Ag nano compounds were prepared by melt blend method: single-screw extruder and internal Brabender mixer. The effects of Ag-NPs and PEG on the crystallization, morphology, thermal, and mechanical properties were evaluated. Ag-NPs with a particle size of 80 nm and typical growth of the β-form in iPP were observed. The presence of PEG in samples of PP/Ag-NPs was detected by infrared spectrometry and the peak characteristic of Ag-NPs by ultraviolet–visible analysis. X-Ray diffraction patterns and differential scanning calorimetry thermograms showed the β-phase formation in both of the dispersion methods, but Brabender mixer showed higher percentages of crystallinity (31% of β-phase). The elongation at break was increased and it was directly dependent on the relative amount of crystalline β-phase. PEG is an excellent precursor to get Ag-NPs and a good interface modifier of iPP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".