Wood plastic composites by melt impregnation: Polymer retention and hardness
Bibliographic record
Abstract
Abstract Wood plastic composites were prepared through impregnation of solid wood with polyethylene. The effects of impregnation parameters on polymer retention and hardness were investigated. A screening strategy of 16‐run resolution IV design for seven factors at two levels was adopted. The seven factors were: ratio of maleated polyethylene in formulations, ratio of polyethylenes with different molecular weights, four process factors (vacuum, pressure, time, and temperature), and wood species (red maple and aspen). Polymer retention (PR) and Brinell hardness (HB) were investigated and discussed on the basis of the impregnation parameters. The present work showed that process parameters (pressure and temperature), polymer impregnants (different molecular weight polyethylenes), and wood species contributed significantly to PR and HB. Increasing pressure and temperature resulted in a higher PR and HB, whereas increasing the molecular weight of polyethylene and switching wood species from aspen to red maple gave a lower PR and HB. This study was aimed at understanding how impregnation parameters affect the final properties of wood plastic composites and developing an optimal fabrication process for wood plastic composites. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 1672–1680, 2006
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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) | 0.001 | 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".