Improved thermomechanical and rheological properties of polypropylene composites with thermomechanical pulp for injection molding
Bibliographic record
Abstract
Abstract Material extrusion and injection molding are prevalent in polymer processing, but wood fiber‐reinforced polymer composites offer eco‐friendly alternatives for industries like automotive, and aviation. Our study explores biocomposites using bleached chemi‐thermomechanical pulp (BCTMP) and polypropylene (PP). BCTMP is rich in cellulose and hemicellulose and quite hydrophilic, while PP's hydrophobic structure creates a disconnect to creating a composite of the two. Traditional methods add costly coupling agents like maleic anhydride polypropylene (MAPP) in an attempt to enhance the adhesion properties of wood‐plastic composites. However, it is worth noting that even in the presence of MAPP, PP maintains its high hydrophobicity and low surface energy, despite exhibiting considerable heterogeneity. Further complexity arises from the thermal degradation characteristics of BCTMP during the melting processing of PP. Our proposed method involves premixing via cryo and planetary ball milling. This boosts PP and BCTMP adhesion, enhancing dispersion quality and mechanical properties without needing coupling agents. Moreover, the premixing of BCTMP and PP forms a thermal buffer layer around BCTMP, minimizing its thermal degradation during processing. This process also ensures even distribution of BCTMP into PP, resulting in a 200% rise in Young's modulus with 30 wt% BCTMP without compromising ultimate tensile strength. Highlights Exploration of biocomposites using bleached chemi‐thermomechanical pulp (BCTMP) and polypropylene (PP) thorough injection molding Implementation of premixing to enhance PP/BCTMP adhesion without coupling agents Premixing reduces thermal degradation of BCTMP, enhances dispersion, and improves mechanical properties Achieving a 200% increase in Young's modulus with 30% BCTMP incorporation, while maintaining ultimate tensile strength
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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".