Processing Characterization of Sisal/Epoxy Prepregs
Bibliographic record
Abstract
Quality control to obtain composite laminates is frequently applied to synthetic fibers/epoxy prepregs. The gel time test, resin, volatiles and fiber content, drape measurement and tack tests together with water absorption capacity are methods currently employed. However, for natural fibers prepregs there is a gap in the literature, which makes their application difficult. Thus this work will investigate sisal fibers, which have low cost, high biodegradability and low specific weight, following the common methods to manufacture composites from natural fibers/epoxy prepregs. First, the prepregs were prepared by hand lay-up, aligning the fibers with epoxy, keeping 15% by weight content of fiber. After the quality control characterization, 3 mm thickness composite was prepared by using a press, and tensile tests and scanning electron microscopy (SEM) were applied. As a result, the resin fraction values and the solid content of the matrix showed little variation between the different samples. The natural fibers prepregs absorbed water quickly in the initial stage until reaching the saturation level. The NaOH-treated sisal/epoxy prepreg had a tension of 71.06 ± 8.28 kPa for the tack test and tensile strength of 69.24 ± 11.69 MPa. Finally, the NaOH-treated sisal 15 wt%/epoxy resulted in composites with a better performance than the neat epoxy resin. There was good adhesion between the fibers and matrix, as confirmed by SEM and mechanical tests.
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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 | 0.000 | 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".