Effect of fiber length on processing and properties of extruded wood‐fiber/HDPE composites
Bibliographic record
Abstract
Abstract Fiber length and distribution play important roles in the processing and mechanical performance of fiber‐based products such as paper and fiberboard. In the case of wood–plastic composites (WPC), the production of WPC with long fibers has been neglected, because they are difficult to handle with current production equipment. This study provides a better understanding of the effect of fiber length on WPC processing and properties. The objectives of this study were therefore to determine the role of fiber length in the formation process and property development of WPC. Three chemithermomechanical pulps (CTMP) with different lengths, distributions, and length‐to‐diameter ratios ( L / D ) were obtained by mechanical refining. Length, shape, and distribution were characterized using a fiber quality analyzer (FQA). The rheometer torque properties of high‐density polyethylene (HDPE) filled with the pulps at different loads were studied. Variations in fiber load and length distribution resulted in significant variations in melting properties and torque characteristics. Composites from the three length distributions were successfully processed using extrusion. Physical and mechanical properties of the obtained composites varied with both length distribution and additive type. Mechanical properties increased with increasing fiber length, whereas performance in water immersion tests decreased. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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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 | 0.001 |
| 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".