Thermal Properties of Hemp Fiber‐Based Hybrid Composites
Bibliographic record
Abstract
Thermal properties play important roles in determining the materials ability to perform in high-temperature exposure and harsh service conditions. Hemp fiber is one of the most commonly used natural fiber reinforcements in composite materials. This is due to the fact that hemp fiber is renewable and natural and offers high stiffness and strength in comparison with other natural fibers such as jute, kenaf, sisal, etc. Despite many attractive attributes, hemp fiber also suffers from disadvantages such as high moisture absorption, poor compatibility with hydrophobic polymers, lower thermal stability limits, high-temperature processing requirements, and their use in harsh serve conditions. Natural fibers such as hemp generally start degradation at around 230–240 °C. To reduce some of the drawbacks and enhance the thermal stability of hemp fiber-reinforced composites, different chemical treatments and surface modification methodologies have been employed. Among these improvement techniques, a hybrid approach has recently attracted much attention. Existing literature suggests that the adaptation of various improvement initiatives has resulted in improved performances of hemp fiber-reinforced composites. This chapter investigates and presents the benefits of various techniques including a hybrid approach in terms of improving thermal stability of these composites.
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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".