Cellulose Nanofibers as Functional Biomaterial from Pineapple Stubbles via TEMPO Oxidation and Mechanical Process
Bibliographic record
Abstract
Abstract The pineapple fruit when harvested generates a large amount of residual biomass; this biomass can be used to generate value-added products such as cellulose nanofibers. This study was focused on the isolation of CNF from pineapple leaves after oxidation pretreatment with 2,2,6,6-tetramethylpireridine-1-oxyl, followed by mechanical deconstruction of the fibers via combination of grinding and microfluidization process. One and two microfluidization passes were applied to bleached and unbleached fibers, respectively. The implications of these findings are that during the production process it is possible to reduce the amount of chemicals needed for bleaching and the energy involved in the mechanical microfluidization process. Such process yielded corresponding fibril lengths and widths in the range of 481–746 nm and 16–48 nm. The respective electrostatic charges, as measured by zeta potentials, were −41 mV and −31 mV. As expected, the CNF crystallinity was higher than that of the starting material, especially for the cellulose. However, the thermal stability was reduced, showing two degradative processes due to the chemical modification of the fibers. The CNF produced from pineapple leaves has a potential to be used like biomaterial in diverse applications while representing a viable alternative to producers, which face serious environmental and health challenges given the large volume of biomass that is otherwise left in the fields as waste. Graphic Abstract
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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) | 0.001 | 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".