Properties of High-Density Polyethylene-Polypropylene Wood Composites
Bibliographic record
Abstract
We investigated the effects of polymer blend variation on the physical, mechanical, and thermal properties of wood-polymer composites (WPC). We used high-density polyethylene (HDPE) and polypropylene (PP) and a combination of 80% PP, 20% HDPE, and 80% HDPE, 20% PP as polymer blends for WPC formulations to simulate recycled plastics. We used black spruce (Picea mariana Mill.) hammer milled fibers (75–250 μm) at 35 wt% as a filler for all the formulations. A two-step process was used for WPC manufacturing; pellet extrusion followed by test samples injection. Tensile and three bending tests characterized the WPC mechanical properties. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) characterized the WPCs’ thermal properties. Water absorption and contact angle measurements assessed the composite dimensional stability. Infrared spectroscopy (FTIR) and electron scanning microscopy (SEM) investigated the WPCs’ surface chemistry and microstructure. Mechanical properties and dimensional stability varied according to polymer composition, with better performance for WPC containing higher PP proportions. Thermal properties varied with the polymer composition in the WPC, with better thermal stability for the formulation containing higher HDPE proportions. Surface chemistry analysis did not reveal any chemical changes on the WPCs surface. Scanning electron microscopy analysis revealed distinct phases in all WPCs without evidence of interfacial adhesion.
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".