Crosslinking networks in epoxidized natural rubber/hemp hurd bio‐composites for the enhancement of thermal, mechanical, and water repellence properties
Bibliographic record
Abstract
Abstract Epoxidized natural rubber (ENR) bio‐composites filled with hemp hurd were fabricated via reactive batch‐mixing and vulcanization. The effect of the ENR‐hemp hurd grafting, silane modification, sulfur vulcanization, and the built‐up crosslinking networks on the thermomechanical and physical properties were analyzed. It was observed that the reactive mixing of ENR and hemp hurd caused epoxy ring opening followed by ether bonding of ENR onto the hemp hurd. There was evidence showing that the hemp fillers were also involved in the sulfur vulcanization. The sulfur vulcanized ENR bio‐composites displayed a low swelling index, homogeneous filler dispersion, and superior thermomechanical properties. The vulcanized bio‐composites with silane‐modified hemp hurd showed slightly less grafting but substantially reduced water absorption. The hardness, tensile strength, and modulus of the optimized formulation displayed a 300%, 1030%, and 960% enhancement as compared to the ENR baseline, respectively. Highlights ENR bio‐composites with hemp hurd were fabricated. Crosslinking networks were built‐up within the ENR/hemp bio‐composites. SiHemp show similar reinforcement but more water repellence. The synthesized composites had enhanced thermal and mechanical properties.
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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".