Mechanical and Structural Characterization of Pineapple Leaf Fiber
Bibliographic record
Abstract
Evidence-based research had shown that elevated alkali treatment of pineapple leaf fiber (PALF) compromised the mechanical properties of the fiber. In this work, PALF was subjected to differential alkali concentrations: 1, 3, 6, and 9% wt/wt to study the influence on the mechanical and crystal properties of the fiber. The crystalline and mechanical properties of untreated and alkali-treated PALF samples were investigated by X-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR), and tensile testing analysis. The XRD results indicated that crystal properties of the fibers were modified with 6% wt/wt alkali-treated PALF recording the highest crystallinity and crystallite size of 76% and 24 nm, respectively. The FTIR spectra suggested that all alkali-treated PALF samples underwent lignin and hemicellulose removal to varying degrees. An increase in the crystalline properties improved the mechanical properties of the PALF treated with alkali at 6% wt/wt, which has the highest tensile strength (1620 MPa). Although the elevated alkali treatment resulted in decreased mechanical properties of PALF, crystallinity generally increased. The findings revealed that the mechanical properties of PALF not only improve with increasing crystallinity and crystallite size, but are also dependent on the intermediate bond between adjacent cellulose chains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".