<i>Aloe vera</i> rind cellulose nanofibers‐reinforced films
Bibliographic record
Abstract
ABSTRACT Aloe vera (AV) gel has been widely used in various medical, cosmetic, and nutraceutical applications. However, AV rind, the tougher outer layer of AV leaves where the cell wall components exists, is currently treated as a fertilizer or waste. This study aimed to investigate the potential of the AV rind as a resource for the production of cellulose nanofibers. Since a detailed analysis of the AV rind has been lacking, chemical composition of rind was analyzed before processing it into nanofibers. The results showed that AV rind has a high proportion of α‐cellulose (57.72% ± 2.18%). AV rind nanofibers (AVRNF) were prepared using chemi‐mechanical process. The morphological analyses showed that most of the isolated fibers were individual fibers under 20 nm. Crystallinity and degree of polymerization of the obtained AVRNF, and mechanical properties of the nanofibrous film were evaluated and compared with the wood nanofibers. Tensile strength of AVRNF film (102 MPa) was comparatively lower than the wood fibers (132 MPa), which was consistent with the lower crystallinity of AVRNF [crystallinity index (CI) = 0.66] as well as the lower degree of polymerization (DP = 396), compared with wood fibers (CI = 0.90, DP = 1297). © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40592.
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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".