Morphological, mechanical, and physical properties of composites made with wood flour‐reinforced polypropylene/recycled poly(ethylene terephthalate) blends
Bibliographic record
Abstract
Wood flour (WF)‐filled composites based on a polypropylene (PP)/recycled polyethylene terephthalate (r‐PET) matrix were prepared using two‐step extrusion. Maleic anhydride grafted polypropylene (MAPP) was added to improve the compatibility between polymer matrices and WF. The effects of filler and MAPP compatibilization on the water absorption, mechanical properties, and morphological features of PP/r‐PET/WF composites were investigated. The addition of MAPP significantly improved mechanical properties such as tensile strength, flexural strength, tensile modulus, and flexural modulus compared with uncompatibilized composites, but decreased elongation at break. Scanning electron microscopic images of fracture surface specimens revealed better interfacial interaction between WF and polymer matrix for MAPP‐compatibilized PP/r‐PET/WF composites. MAPP‐compatibilized PP/r‐PET/WF composites also showed reduced water absorption due to improved interfacial bonding, which limited the amount of absorbable water molecules. These results indicated that MAPP acts as an effective compatibilizer in PP/r‐PET/WF composites. POLYM. COMPOS., 38:1749–1755, 2017. © 2015 Society of Plastics Engineers
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".