Polypropylene-Clay Nanocomposites Fibers Structure and Performance
Bibliographic record
Abstract
Abstract Fibers from Polypropylene (PP) and polypropylene nanocomposite (PNC) were produced using a fiber spinning process. Various amounts of different clays were added to the PP matrix. The structure of PP and PNC fibers was evaluated using field emission scanning electron microscopy (FE-SEM) and X-ray diffraction. The crystalline axes orientation factors were determined from wide-angle X-ray diffraction pole figures of (110) and (040) reflections for PP and of (001) for clay. The d-spacing of the (001) clay crystalline plane, indicative of the clay dispersion, was determined from the intensity −2 θ plots superimposed on the two dimensional X-ray diagrams. The results obtained for the d-spacing at small angles indicated that the clay in the PNC maintained its intercalated distance at low loadings whereas at higher loadings, some aggregation was observed. Results on orientation of the clay (001) axis (normal to the clay platelets plane) indicated that it was oriented perpendicular to the machine direction, which is expected. Its orientation in the normal and transverse directions was different, suggesting that clay platelets were not randomly distributed in the transverse plane, which was confirmed from SEM observations. The orientation of PP c-axis in the fibers was slightly higher for the nano-composite that the pure resin. Tensile modulus, strength and elongation at break for both PP and PNC fibers increased with increasing draw ratio. Flexural modulus and strength of laminates made by consolidation of the PNC fibers were higher than those of consolidated pure PP fibers as well as those of bulk PP or PNC.
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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) | 0.001 | 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".