Development and characterization of chitosan and beeswax coated biodegradable corn husk and sugarcane bagasse-based cellulose paper.
Bibliographic record
Abstract
Abstract This research focuses on the development of paper from lignocellulosic agricultural wastes, viz., corn husk (CH), which is an underexplored material and sugarcane baggase (SB), in varying proportions, through soda pulping and enhancement of their functionalities through chitosan and chitosan-beeswax emulsion coatings. Fiber digestion conditions were as follows: 100°C (30 min); 100–162°C (90 min) and 162°C (90 min); followed by blowing, quenching and then refining of both treated CH and SB pulp to a Canadian Standard Freeness (CSF) of 400–450 mL. The handsheets of 80 GSM (grammage) were prepared as per the standard ISO-5269/1 and were tested for their mechanical and barrier properties as per standard methods of ISO. Handsheets developed from the blend of SB and CH (50:50) and SB fibers (100%) were found to have better mechanical strength (in terms of burst, tensile and tear strength) in comparison to CH fibers (100%). The effect of coatings on mechanical, water resistance, micro-structural, and biodegradable properties of the cellulose papers were also assessed. The chitosan coating significantly improved (p < 0.05) the mechanical properties of papers, the barrier properties against water vapor, moisture and air were also enhanced (up to 85%). Papers coated with beeswax–chitosan emulsion had the longest absorbency time, followed by chitosan-coated and uncoated papers. The results advocated for beeswax–chitosan emulsion as the best among the coatings tested, for aforementioned cellulose papers to enhance their barrier properties.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".