The addition effect of hollow glass microsphere on the dispersion behavior and physical properties of polypropylene/clay nanocomposites
Bibliographic record
Abstract
ABSTRACT Polypropylene (PP) nanocomposites have been widely researched for last decade due to its high mechanical property and multiple usages in various industrial fields from automobile to consumer packaging. Dispersion of nanoclay in polyethylene and PP is the key factor due to their hydrophobic property. Adding surfactant or introducing a polymer with a functional group, and modifying a natural clay with organic ions have been tried to achieve the better dispersion of clay in polymer matrix. In this study, the PP/clay nanocomposite with maleic anhydride grafted PP and hollow glass microspheres (HGMs) at various compositions were prepared and characterized. The addition of HGMs to PP/clay nanocomposites significantly improved the tensile stress at yield point except 7 wt % of HGMs. It is assumed that this could be due to not only the reinforcing effect of HGMs in PP, but the crushing effect of HGMs on the clay resulting in interlayer increase of clay as well. The barrier properties was also found to be improved about 32% max resulting from the better dispersion and presence of HGMs. It could be assumed that the HGMs could act as a ball creating a ball milling crush force to achieve the better dispersion of clay in the polymer and improve nanocomposite performance. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47476.
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) | 0.001 | 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".