Sustainable recycling of polypropylene waste: Characterization and statistical analysis of wood husk-PP composite
Bibliographic record
Abstract
The growing emphasis on sustainability and circular economy has driven extensive efforts in recycling and reusing polymer materials, widely used across various industries. While significant progress has been made in recycling polypropylene (PP), the development of polypropylene-ortho (PP-ortho) composite for prosthetics and orthotics remains underexplored. This study focuses on recycling PP-ortho waste by adding wood husk (WH) as a filler material for making PP-ortho composites for manufacturing assistive parts. Statistical analyses were performed considering PP, wood husk and particle size (PS) as input parameters to investigate the mechanical, thermal, and structural properties of the wood husk-PP composite. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the thermal stability of the composite. The results showed that mechanical properties improved with reduced wood husk content and smaller particle sizes, with optimal conditions achieved at 90 wt% PP, 10 wt% wood husks, and a particle size of 125 μm. Thermal analysis indicated a slight reduction in thermal stability with increased wood husk content. The scanning electron microscopy (SEM) further confirmed that higher wood husk content and larger particle sizes led to weaker interfacial adhesion, fiber pullout, and reduced mechanical performance. This research advances sustainable recycling of polypropylene by developing composites for assistive parts in physical rehabilitation, bridging material science with impactful healthcare solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".