BioBased and compostable bioplastics for paper lamination
Bibliographic record
Abstract
Plastic packaging is used in very large volumes and is essential for food preservation. Each year, approximately 1.5 million tonnes of plastic packaging waste is generated, representing 47% of plastic waste in Canada, with the majority ending up in landfills. There is an urgent need for a sustainable alternative. Paper-based packaging could offer a potential solution, owing to its advantages of renewability, recyclability, and biodegradability. However, paper-based packaging cannot withstand high humidity and water exposure unless it is protected by water-resistant coatings. Current water-resistant coatings are based on per- and polyfluorinated alkyl substances (PFAS), also known as "forever chemicals," which are potentially toxic to human health and harmful to the environment. Laminating hydrophobic polymers onto paper is an effective alternative. Currently, the most commonly used plastic for lamination coating is LDPE, rendering the package neither recyclable nor compostable. Some bioplastics have been developed for paper lamination coatings; however, they are expensive and not readily accepted at composting facilities. In this study, the NRC has developed cost-effective starch-based bioplastics for paper lamination with improved oxygen barrier properties and accelerated composting capabilities. This development will promote packaging innovations in both the Canadian forestry and manufacturing industries, potentially creating new business opportunities and supporting the Government of Canada's Zero Plastic Waste and Circular Economy Agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".