Determination of optimal alkaline treatment conditions for fique fiber bundles as reinforcement of composites materials
Bibliographic record
Abstract
"For the last decades, natural fibers have been used as reinforcement of friendly environmental polymeric composites, due to their technical, economical and environmental advantages that include: moderate mechanical and thermal properties. However, the OH groups in some of their chemical structures like cellulose, reduce the compatibility with hydrophobic polymeric matrices such as polyolefines. Natural fiber, usually are exposed to chemical and physical treatment to reduce their hydrophilic tendency and to enhance fiber/matrix adhesion. Alkalinization, alkali treatment or mercerization, is one of the most common procedures applied on natural fibers. This process introduces important changes on its mechanical properties, physical and morphological characteristics, and chemical composition. In spite of other studies, it is necessary define surface treatment conditions in accordance with industrial processing and environmental considerations. In this study, the influence of different alkali treatment conditions on the fique fiber tensile behavior has been evaluated. Treatment parameters as solution concentration, exposure time and dry conditions have been analyzed. Fourier transformation infrared spectrophotometry (FTIR) analysis, atomic force (AFM) and optical microscopies have been used to evaluate alteration on chemical and morphological characteristics. Treatment conditions that include low solution concentration bring a good enough quality in the mechanical behavior required by fique fiber bundles as polymeric reinforcement."
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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 | 0.001 | 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) | 0.001 | 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 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".