Processing of Pineapple Leaf Fibers for the Production of Oxidized Micro-/Nanofibrillated Cellulose
Bibliographic record
Abstract
Pineapple leaf fibers (PALFs), obtained from abundant yet underutilized pineapple leaf residues, represent a promising feedstock for producing fibrillated cellulose. In this work, cellulosic fibers were isolated and characterized by Fiber Quality Analysis (FQA), showing lengths between 0.33 and 0.47 mm and widths of 12.2 µm after organosolv pulping using ethanol and acetic acid as a catalyst, followed by hydrogen peroxide bleaching with diethylenetriaminepentaacetic acid as a chelating agent. The cellulosic fibers were then subjected to TEMPO-mediated oxidation and subsequently disintegrated by microfluidization to produce micro-/nanofibrillated cellulose (MNFC) with a carboxylate content of 0.85 and 1.00 mmol COO−/g, zeta potential of −41 and −53 mV, and average widths of 15 and 12 nm for unbleached and bleached nanofibrils, respectively. The nanofibrillation yields were 73% and 68% for the bleached and unbleached MNFC samples, indicating the presence of some non-fibrillated or partially fibrillated fractions. X-ray diffraction analysis confirmed preservation of cellulose type I crystalline structure, with increased crystallinity, reaching 85% in the bleached MNFC. These findings demonstrate the feasibility of a sequential process, combining organosolv pulping, hydrogen peroxide bleaching, TEMPO-mediated oxidation, and microfluidization, for preparing MNFC from pineapple leaf fibers. Overall, this study highlights pineapple leaf residues as a sustainable source of MNFC, supporting strategies to transform agricultural waste into valuable bio-based materials.
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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".