Mechanical Properties of PP Composites Reinforced with BCTMP Aspen Fiber
Bibliographic record
Abstract
In this work we investigated the effect of incorporating wood fibers (bleached chemi-thermo-mechanical pulp of aspen, BCTMP) on the properties of a hybrid composite material made from maleated polypropylene (MAPP) and Nanoclay (NC) or not. The effects of morphological structure of polypropylene (PP) and the molecular weight (Mw) and maleic anhydride graft level (MA%) of MAPP were also performed to evaluate the improvements of each independent factor (MAPP, Dicumyl peroxide-DCP and NC). In our case, the size of wood fiber had little effect on the impact and tensile strength. High Mw and low MA% MAPP contributes homo-PP hybrids with better performance, either impact or tensile. In addition, wood fiber gives the hybrids, based on both homo-polypropylene (homo-PP) and co-polypropylene (co-PP), without the presence of coupling agent, similar behaviors of impact and tensile strengths. However, the impact and tensile strength of the hybrids mentioned above are improved in the presence of coupling agent. NC weakens the impact strength of composites with/without the reinforcement of wood fiber, coupling agent, and DCP. It shows different behaviors of tensile with improvement of tensile for hybrids with wood fiber, coupling agent, and DCP but impartation for nanocomposites without additives. Moreover, the behavior of tensile is in concordance to the changes of elongation as well as toughness. Furthermore, DCP leads to negative effects on the impact and tensile, as well as toughness of the hybrids when fiber is present, but exhibits better resistance deformation extension which is in concordance with the increase of modulus. In addition, there is a little increase in plasticity.
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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".