Assessing Extraction Methods and Mechanical and Physicochemical Properties of Algerian Yucca Fibers for Sustainable Composite Reinforcement
Bibliographic record
Abstract
Abstract The utilization of biofiber in recent years has significantly increased due to its advantages like being environmentally friendly, availability, and low costs. This paper investigates the physicochemical, mechanical, and morphological properties of the yucca fiber extracted by three methods such as water‐retting, traditional, and chemical methods. These analyses are designed to evaluate the extraction methodology and the hypothesis of the influence of harvesting location and growth conditions of the fiber. Various technologies are used, such as SEM, FTIR, XRD, and tensile tests. The fiber extracted by water retting is the strongest in the mechanical analysis with a strength of 690.48 MPa, followed by fiber extracted with the traditional method with 685.48 MPa, also 673.06, 657.94, 373.68 MPa for the fiber extracted by the chemical method using 3%, 5%, 10%NaOH respectively. The fiber obtained by the water retting method also has a higher chemical composition with 80.25% cellulose, 10.45% lignin, and 13.75% hemicellulose. The morphological characteristics are examined using Scanning Electron Microscopy. The crystallinity index ranged from 61.75% to 70.77%, and crystallite size from 1.73 to 2.04 nm is calculated from the XRD analysis. All these results confirm that yucca fiber can be a good sustainable choice for composite reinforcement.
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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) | 0.000 | 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 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".