Effect of fiber and polymer variability on the rheological properties of wood polymer composites during processing
Bibliographic record
Abstract
We investigated the effects of fiber variability, size and content on the fusion characteristics of wood particle‐reinforced high‐density polyethylene (HDPE). Five types of wood sawdust were used: eastern white cedar, with sapwood, and heartwood treated separately; jack pine divided into wood and bark; and black spruce. Three different fiber length classes were also used. Composite pellets containing wood particles at 25, 35, and 45% by weight with HDPE were made using a co‐rotating twin‐screw extruder. The pellets were melted and mixed during 7 min in a torque rheometer at 180°C. We also investigated the polymer variation using HDPE, polypropylen (PP), polyvinyl chloride (PVC), and a blend of (80% HDPE+ 20% PP) where wood fiber proportion and length were kept constant. We varied the mixing temperature to reach the melting temperature range of each polymer. Mixing and melting times, maximum torque and stabilized torque were obtained. Adding wood fibers to the HDPE increased processing time and torque energy. At constant fiber length and proportion, torque properties varied with fiber origin. Higher fiber length and proportion increased torque energy and time of stabilization. Thus, using wood fibers with different properties will lead to significant variations in processing, such as in extrusion or injection. The fusion characteristics of wood polymer composite vary among polymers. The PVC showed the highest steady‐state torque. The formulation containing a polymer blend of HDPE and PP showed the highest torque energy. This higher torque energy is explained by the interaction of the incompatible polymeric chains of the two thermoplastic polymers. POLYM. COMPOS., 40:E609–E616, 2019. © 2018 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".