Sustainable Chitosan-Dialdehyde Cellulose Nanocrystal Film
Bibliographic record
Abstract
In this study, we incorporated 2,3-dialdehyde nanocrystalline cellulose (DANC) into chitosan as a reinforcing agent and manufactured biodegradable films with enhanced gas barrier properties. DANC generated via periodate oxidation of cellulose nanocrystal (CNC) was blended at various concentrations with chitosan, and bionanocomposite films were prepared via casting and characterized systematically. The results showed that DANC developed Schiff based bond with chitosan that improved its properties significantly. The addition of DANC dramatically improved the gas barrier performance of the composite film, with water vapor permeability (WVP) value decreasing from 62.94 g·mm·m−2·atm−1·day−1 to 27.97 g·mm·m−2·atm−1·day−1 and oxygen permeability (OP) value decreasing from 0.14 cm3·mm·m−2·day−1·atm−1 to 0.026 cm3·mm·m−2·day−1·atm−1. Meanwhile, the maximum decomposition temperature (Tdmax) of the film increased from 286 °C to 354 °C, and the tensile strength of the film was increased from 23.60 MPa to 41.12 MPa when incorporating 25 wt.% of DANC. In addition, the chitosan/DANC (75/25, wt/wt) films exhibited superior thermal stability, gas barrier, and mechanical strength compared to the chitosan/CNC (75/25, wt/wt) film. These results confirm that the DANC and chitosan induced films with improved gas barrier, mechanical, and thermal properties for possible use in film packaging.
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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".