Effect of coupling agents on rice‐husk‐filled HDPE extruded profiles
Bibliographic record
Abstract
Abstract Lignocellulosic composites are diversifying their applications into various fields as they can meet the requirements of the respective applications by changing the matrix, fiber resource and processing ingredients. In this research work we explored the potential of extruded rice‐husk‐filled high density polyethylene (HDPE) composite profiles for structural applications. The structure and the properties of the interface in fiber‐reinforced composites play a crucial role in determining the performance properties of the composites. An optimum degree of adhesion between the fiber and the matrix is required for efficient stress transfer from the matrix to the fiber. Generally, coupling agents are used to improve the adhesion between lignocellulosic filler and the polymer matrix in structural composite materials. In this study, four different coupling agents based on ethylene‐(acrylic ester)‐(maleic anhydride) terpolymers and ethylene‐(acrylic ester)‐(glycidyl methacrylate) terpolymers were used to enhance the performance properties of the composites. The results indicated that these coupling agents enhanced the tensile and flexural strength of the composites significantly, and the extent of the coupling effect depends on the nature of the interface formed. Incorporation of coupling agents enhanced the resistance to thermal deformation and the water absorption properties of the composite, whereas it reduced the extrusion rate significantly. Among the four coupling agents used, EGMA1—the one with a glycidyl methacrylate functional group and without any methyl acrylate pendant group on the polymer backbone—was found to be the best coupling agent for the rice‐husk‐filled HDPE composites. Copyright © 2004 Society of Chemical Industry
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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".