Evaluating the elastic properties of ensete fiber as a sustainable alternative to bast fibers: A micromechanical and numerical study
Bibliographic record
Abstract
Bast fibers are promising natural materials known for their biodegradability, affordability, and eco-friendliness, making them an alternative to synthetic options. Extensive research has been conducted to examine the effects of integrating various bast fiber reinforcements into epoxy and polystyrene matrices to boost the properties of the composite materials. However, there is limited research on ensete fiber and its utilization as a reinforcement that needs more in-depth research to be used as an alternative bast fiber. This paper aimed to predict and compare the performance of ensete fiber composites with six other bast fiber-reinforced polystyrene and epoxy composites. In this study, flax, hemp, jute, ramie, banana and kenaf were selected bast fibers for comparison purposes. This article employed various micromechanics models and finite element method (FEM), varying the fiber volume fraction. Our findings revealed that hemp fiber-reinforced composites exhibited the best predicted elastic properties, while banana fiber-reinforced composites showed the weakest performance. Notably, composites made with ensete fibers outperformed those made with jute and banana fibers in both epoxy and polystyrene matrices. Comparisons were made between results from the micromechanics models and FEM for all bast fiber-reinforced epoxy and polystyrene composites and there was an agreement between the effective elastic properties and fiber volume fraction (FVF). Further, bast-fiber reinforced epoxy composites showed higher values than polystyrene for strain analysis while for stress analysis, polystyrene composites showed higher stress loads than epoxy 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".