Pineapple-Derived Nanocellulose for Nanocomposites: Extraction, Processing, and Properties
Bibliographic record
Abstract
Pineapple waste is an underexplored source for producing nanocomposites, from which nanocellulose, namely cellulose nanocrystals (CNCs) or cellulose nanofibers (CNFs), can be produced. This review summarizes extraction methods from different pineapple residues (leaves, crown leaves, stem, peel, pulp, and pomace), covering top-down processes (hydrolysis, oxidation, carboxymethylation, and mechanical fibrillation) and bottom-up strategies (ionic liquids and deep eutectic solvents). The review examines the influence of the morphology and crystallinity of nanocellulose on the functional performance of the nanocomposites. Strategies for processing pineapple-derived nanocellulose composites are analyzed by technique (solution casting, film stacking, and melt blending/extrusion) and polymer matrices (starch, PVA, chitosan, PLA, PHBV, PBAT, proteins, and polysaccharides), including typical loading levels for most polymer-reinforced systems (0.5–5 wt.%), while higher levels (15–50 wt.%) are used in particular cases such as PVA, CMC, and cellulosic matrices. The impact on mechanical strength, barrier behavior, UV shielding, and optical properties is summarized, along with reports of self-reinforced and hybrid cellulose-derived matrices. A benchmarking section was prepared to show nanocellulose loading ranges, trends in properties, and processing-relevant information categorized by type of matrix. Finally, the review describes the potential roles of pineapple waste within a bioeconomy context and identifies some extraction by-products that could be incorporated into diverse value chains.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".