Isolation and characterization of microcrystalline cellulose from roselle fibers
Bibliographic record
Abstract
In this study, microcrystalline cellulose (MCC) was extracted from roselle fiber through acid hydrolysis treatment and its properties were compared with those of commercially available MCC. The physicochemical and morphological characteristics, elemental composition, size distribution, crystallinity and thermal properties of the obtained MCC were analyzed in this work. Fourier transform infrared spectroscopy (FTIR) analysis provided clear evidence that the characteristic peak of lignin was absent in the spectrum of the MCC prepared from roselle fiber. Rough surface and slight aggregation of MCC were observed by scanning electron microscopy (SEM). Energy dispersive X-ray (EDX) analysis showed that pure MCC with small quantities of residues and impurities was obtained, with a similar elemental composition to that of commercial MCC. A mean diameter of approximately 44.28μm was measured for MCC by using a particle size analyzer (PSA). X-ray diffraction (XRD) showed the crystallinity increased from 63% in roselle pulp to 78% in roselle MCC, the latter having a slightly higher crystallinity than that of commercial MCC (74%). TGA and DSC results indicated that the roselle MCC had better thermal stability than the roselle pulp, whereas it had poorer thermal stability in comparison with commercial MCC. Thus, the isolated MCC from roselle fibers will be going to use as reinforcing element in green composites and may be a precursor for future roselle derived nanocellulose, and thus a promising subject in nanocomposite research.
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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".