Influence of <scp>SOFTWOOD</scp> ‐fillers content on the biodegradability and morphological properties of <scp>WOOD</scp> –polyethylene composites
Bibliographic record
Abstract
In the present work, wood flour reinforced thermoplastic polymer composites, with and without coupling agents, were prepared by melt processing, and their mechanical and thermal behaviors were analyzed. For preparation of polymer composites, six different formulations were used. On the other side, the degradation of wood fillers up to 97 days and water uptake in composites up to 10 weeks was evaluated, respectively, using fungi specie ( Gloephyllyllum trabeum ) and water absorption tests. To study the morphological changes resulting from microorganism activity, scanning electron microscopy was used. The obtained results indicate that the addition of wood fillers to the polyethylene matrix increases the degree of crystallinity, and tensile strength. On the contrary, resistance to fungi decay and water absorption decrease as a function of the wood fillers content thus the composite becomes more vulnerable to moisture uptake and micro‐organism attack, which can change the morphological and mechanical strength of composite. Based on the obtained results, microorganisms mainly affect the surface of composite and the adhesion of wood and polymer matrix. An optimized amount of filler content can reinforce the polymeric matrix more efficiently while decrease the rate of degradation of wood fillers as well. POLYM. COMPOS., 39:29–37, 2018. © 2016 Society of Plastics Engineers
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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".