Effect of Surface Modification on the Properties of Buckwheat Husk—High-Density Polyethylene Biocomposites
Bibliographic record
Abstract
This study focuses on the production and characterization of biocomposites based on a thermoplastic polymer (high-density polyethylene, HDPE) and a biosourced filler (buckwheat husk, BHS) to develop more sustainable composites. Compounding was performed via twin-screw extrusion with three different types of BHS. In the first series, untreated BHS was directly mixed with the polymer matrix, while the second series used mercerized BHS and the third series used pretreated BHS with a coupling agent (polyethylene grafted with maleic anhydride, MAPE) in solution. The samples were prepared at different concentrations (10, 20, 30, 40 and 50 wt.% of BHS) to compare with the neat matrix (0%). All the samples were finally produced by compression molding and then cut to get the specimens for characterization. The latter included morphological (scanning electron microscopy), physical (density and hardness) and mechanical (tension, flexural and impact strength) properties. Based on the results obtained, it was observed that most of the mechanical and physical properties were improved, especially when the BHS was pretreated in solution before its introduction into the polymer matrix. The results showed that 30 wt.% of BHS in HDPE was the optimum for most of the properties investigated.
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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".