Lignin and Cellulose Nanofibers from Bambusa vulgaris Schrad (Bamboo): An Extraction, Preparation and Characterization Study
Bibliographic record
Abstract
In this paper, lignin and cellulose nanofibers were extracted and prepared from Bambusa vulgaris schrad (B. vulgaris: bamboo) before being subjected to characterization investigations. These extractions and preparations of the lignin and cellulose nanofibers were carried out chemically using alkali combined with bleaching treatments together with acid hydrolysis and sonication. The cellulose nanofibers were then subjected to morphological and dimensional characterization of the Zetasizer, Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) instruments. The functional groups investigation, using Fourier Transform infrared spectroscopy (FTIR), and thermal degradation via the Thermogravimetric analysis (TGA), of the bamboo lignin and of the cellulose nanofibers were also carried out. Results revealed that the percentage yields of the bamboo lignin and bamboo nanofiber were 21.91 wt% and 33.6 wt% respectively. The SEM and TEM investigations indicated the prepared nanofibers were rod-like in morphology, having sizes ranging from 20 to 100 nm. FTIR showed that the lignin extracted from bamboo typified G-S type lignin while the nanofibers are completely devoid of lignin. TGA revealed that the lignin was more thermally stable than the nanofiber under the test conditions. The obtained lignin and cellulose nanofibers showed promise for possible application as reinforcement agents in biodegradable nanocomposite film preparation.
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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".