Hemp Waste Stream Valorization Through Pyrolytic Carbonization for Epoxy Composite Strengthening
Bibliographic record
Abstract
This research addresses a gap in the literature by exploring the combined use of hemp and hemp hurds in composites, presenting a novel approach to bio-composite development. We report on the mechanical properties of epoxy resin composites reinforced with hemp fibers and hemp hurds, selected for their sustainability, biodegradability, and environmental benefits. These natural fibers offer a renewable alternative to synthetic fibers, aligning with the growing demand for eco-friendly materials in various industries. The primary objective was to evaluate how different filler contents and hemp hurd-to-hemp fiber ratios affect the composite’s performance. Composites with 1:1 and 3:1 ratios were prepared at filler concentrations ranging from 1 wt.% to 10 wt.%. Tensile tests revealed that the 3:1 ratio composites exhibited better stiffness and tensile strength, with a notable UTS of 19.8 ± 0.4 MPa at 10 wt.%, which represents a 160% increase over neat epoxy. The 1:1 ratio composites showed significant reductions in mechanical properties at higher filler contents due to filler agglomeration. The study concludes that a 3:1 hemp hurd-to-hemp fiber ratio optimizes mechanical properties, offering a sustainable solution for enhancing composite materials’ performance in industrial 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) | 0.002 | 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".