The effects of clay dispersion on the mechanical, physical, and flame‐retarding properties of wood fiber/polyethylene/clay nanocomposites
Bibliographic record
Abstract
Abstract In this study, nanosized clay particles were introduced into wood fiber/plastic composites (WPCs) to improve their mechanical properties and flame retardancy, which are especially important in various automotive and construction applications. A high degree of exfoliation for nanoclay in the wood fiber/high density polyethylene (HDPE) composites was successfully achieved with the aid of maleated HDPE (PE‐g‐MAn), through a melt blending masterbatch process. The structures and morphologies of the composites were determined using X‐ray diffraction (XRD) and transmission electron microscopy (TEM), respectively. This article presents the effects of clay content and degree of clay dispersion on the mechanical and physical properties and flame retardancy of wood fiber/HDPE composites that contained a small amount of clay, in the range of 3–5 wt %. We concluded that achieving a higher degree of dispersion for the nanosized clay particles is critical to enhance the mechanical properties and the flame retardancy of WPCs when small amounts of clay are used. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
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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) | 0.000 | 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".