The effect of dispersant on toughening mechanism and structure behaviors of Polypropylene Nanocomposites reinforced with nano α-alumina particles
Bibliographic record
Abstract
This article presents a comparative study on the effects of using nano α-alumina (Al 2 O 3 ) on toughening mechanisms and structural behaviors of polypropylene (PP) nanocomposites. The role of using dispersant in nanocomposite preparation was also investigated. For nanocomposite preparation, mixing of the elements was performed using a Haake Poly Drive blending machine at 175°C and the rotor speed of 50 rpm. The notched Izod impact energy obtained for PP was about 27 J/m and by the addition of nano α-Al 2 O 3 (4 wt%) to PP, the notched Izod impact energy increases up to ∼43 J/m. However, higher concentration of nano α-Al 2 O 3 in the nanocomposite resulted in the reduction of Izod impact property due to nano α-Al 2 O 3 agglomeration. Fourier transform infrared spectroscopy (FTIR) spectra of pure PP and PP/nano α-Al 2 O 3 composites demonstrated Al–O bond at 568 cm −1 for nanocomposite spectrum that indicates the creation of nano α-Al 2 O 3 particles. The x-ray diffraction patterns and FTIR spectra of PP/nano α-Al 2 O 3 composites showed that the intensity of the peaks when dispersant was used slightly increased and the arrangement of the peaks are normalized. This observation is attributed to homogeneous dispersion of nano α-Al 2 O 3 filler in the matrix when dispersant was used. Scanning electron micrograph of impact fractured surface showed that the fracture surface of PP/nano α-Al 2 O 3 composite becomes rougher with increasing the content of filler.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".