Thermal Response of Biocarbon-Filled Hemp Fiber-Reinforced Bioepoxy Composites
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We investigated the thermal conductivity of materials based on pyrolysis temperature, filler loading, filler size, and type of biomass feedstock. Hemp stalk and switchgrass were pyrolyzed at 450, 550, and 650 °C and crushed into 50, 75, and 100 μm particle sizes. Biocarbon fillers (10, 15, and 20 wt %) were added to the bioepoxy polymer matrix. The study showed increased filler loading and particle size increased thermal conductivity─the biocomposite samples with 20 wt % filler loading of 100 μm particle size of the biocarbon obtained at 650 °C showed the maximum thermal conductivity in both hemp biocarbon-filled composites (0.59 W·m –1 ·K –1 ) and switchgrass-filled composites (0.58 W·m –1 ·K –1 ) with the highest flame time. Biocarbon in biofiber-reinforced polymer composites can improve thermal conductivity and extend the flame time. These findings significantly contribute to developing hemp-based bioepoxy composite materials for thermal applications in various fields. These include insulating materials for buildings and thermal management systems, energy-efficient applications, and help in material selection and product design with a positive environmental impact.
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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".