Self‐Hybrid Biocomposites Based on <i>Luffa cylindrica</i> and Polyethylene
Bibliographic record
Abstract
ABSTRACT In this study, self‐hybrid biocomposites based on Luffa cylindrica fiber and linear medium density polyethylene in a powder form were prepared by thermal compression at 190°C. This fiber was utilized to take advantage of its natural structure and was used directly and in a pulverized form. Five types of biocomposites were prepared and characterized: (1) natural structure of Luffa fiber (longitudinal, and transversal), (2) untreated Luffa fiber powder of different sizes (mesh 40, 50, 70 and 100), (3) Luffa fiber treated with 2% sodium hydroxide, (4) hybridization of the natural structure of Luffa fiber with 10% of 100 mesh fiber powder (longitudinal, and transversal) and (5) self‐hybridization of 10% of Luffa fiber 1:1 mesh 50:100. A morphological characterization was performed, followed by mechanical characterization in tension, flexion, and impact. Thermogravimetric analysis, differential scanning calorimetry, scanning electron microscopy, and Fourier transform infrared spectroscopy analyses were performed on selected samples to determine the relation between structural and macroscopic properties. The outstanding mechanical properties observed are obtained from a combination of the different structures combined together in terms of fiber size, content, and treatment, as well as the concept of self‐hybridization by using both the mat natural structure with smaller fibers. In general, the best mechanical properties are obtained for hybrid biocomposites, leading to a 50% increase in flexural modulus and a 100% increase in tensile modulus compared to the matrix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".