Characterization of rheological and thermophysical properties of HDPE–wood composite
Bibliographic record
Abstract
ABSTRACT The objective of this study is to develop a new biocomposite material with high deformation ability. In this regard, the thermal, rheological, and thermophysical properties of this new composite were characterized as a function of temperature and filler concentration. High density polyethylene (HDPE) was the matrix of this new composite which was reinforced with six sawdust concentrations 0%, 20%, 30%, 40%, 50%, and 60%. Maleic anhydride grafted polyethylene (PE‐g‐MA) was used as coupling agent. Addition of sawdust with PE‐g‐MA increased significantly the complex viscosity, the storage modulus (G′), and loss modulus (G″) of the matrix. The superposition of the complex viscosity curves using temperature dependent shift factor, allowed the construction of a viscosity master curve covering a wide range of temperatures. Arrhenius law was used for the relationship of the shift factor to temperature. Furthermore, method of Van Gurp and Palmen (tan delta vs. G*) is also used to control the time–temperature superposition. The experimental results can be well fitted with the cross rheological model which allowed the prediction of the thermorheological properties of the composites over a broad frequency range. By increasing wood concentration, both the activation energy and relaxation time for the biocomposites determined using, respectively, the Arrhenius law and the cole–cole rule increased. By contrast, specific heat of the matrix decreased with sawdust addition while its dimensional stability improved. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40495.
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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".