Effect of Wood Fiber Surface Treatment on the Properties of Recycled HDPE/Maple Fiber Composites
Bibliographic record
Abstract
This work reports on the production and characterization of recycled high density polyethylene (R-HDPE) composites reinforced with maple fibers. The composites were produced by a simple dry-blending technique followed by compression molding. Furthermore, a fiber surface treatment was performed using a coupling agent (maleated polyethylene, MAPE) in solution. FTIR, TGA/DTG, and density analyses were performed to confirm any changes in the functional groups on the fiber surface, which was confirmed by SEM-EDS. As expected, the composites based on treated fiber (TC) showed improved properties compared to composites based on untreated fiber (UC). In particular, MAPE was shown to substantially improve the polymer–fiber interface quality, thus leading to better mechanical properties in terms of tensile modulus (23%), flexural modulus (54%), tensile strength (26%), and flexural strength (46%) as compared to the neat matrix. The impact resistance also increased by up to 87% for TC as compared to UC. In addition, the maximum fiber content to produce good parts increased from 15 to 75 wt% when treated fiber was used. These composites can be seen as sustainable materials and possible alternatives for the development of low-cost building/construction/furniture applications.
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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".