Cellulose filaments as sustainable packaging materials: Enhancing barrier properties with cationic starch and alkylketene dimer
Bibliographic record
Abstract
Single-use plastic packaging has become a serious problem for many countries, including Canada, with millions of tons consumed annually without considering end-of-life impact. Researchers are developing sustainable alternatives to non-biodegradable plastic. While cellulose microfibers (MFC) and nanocrystalline cellulose (NFC) are common raw materials for film production, their preparation requires costly chemical treatments that cause environmental pollution. Kruger Inc. developed a method to produce cellulose filaments (CFs) with high fibrillation using only mechanical treatment. This study uses CFs to produce packaging films through handsheet forming, with cationic starch (CS) and Alkylketene Dimer (AKD) as additives for enhanced water, water vapor, and oxygen resistance. We analyzed morphological, barrier, mechanical, and optical properties using various techniques, including SEM, WVTR, WVP, OTR, contact angle, Cobb 60, burst index, tensile index, tear index, and transmittance. The films showed promising properties, with WVTR decreasing to 49.42 g/m 2 ·day, WVP to 2.82 × 10 −6 g·m/m 2 ·day·Pa, OTR to 3.3 cc/m 2 ·day, and Cobb value to 32.2 g/m 2 . After adding 3 % CS, films demonstrated good mechanical performance with a tensile index of 80.49 N·m/g, burst index of 10.93 kPa·m 2 /g, and tear index of 0.66 mN·m 2 /g. These results prove CFs' effectiveness with CS and AKD in forming films with excellent properties. CFs show potential compared to other biopolymers due to their eco-friendliness and cost-effectiveness. The production method is simple, scalable, and yields biodegradable films composed of biomass-derived materials.
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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) | 0.000 | 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 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".