Production of chitosan-based composite film reinforced with lignin-rich lignocellulose nanofibers from rice husk
Bibliographic record
Abstract
Lignocellulosic nanofibers (LCNFs), implying lignin-containing cellulose fibers, maintain the properties of both lignin and cellulose, which are hydrophobic and hydrophilic, respectively. The presence of hydrophobic lignin in LCNFs is expected to be an economical and attractive option that can improve the thermal and mechanical properties of polymers. Thus, this study was conducted to produce lignin-rich LCNFs from sugar-rich waste obtained from rice husks after acidic pretreatment. The LCNFs were produced from the lignin-rich solid fractions obtained after pretreatment and enzymatic hydrolysis, which were then incorporated as an additive into a chitosan-based film. The variations in lignin content in the range of approximately 50.6%–66.8% in differently obtained LCNFs gave significantly different optical strengths and mechanical properties. These controllable processes may allow for customized film formation. Additionally, the glucose-rich liquid fractions obtained after pretreatment and enzymatic hydrolysis were used as a substrate for ethanol fermentation to achieve total utilization of rice husk biomass waste. In conclusion, the lignin-rich biomass fraction holds promise as a suitable material for chitosan-LCNF film and has the potential to increase the economic feasibility of the biomaterial industry.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".