Mineral-filled biopolyester coatings for paperboard packaging materials: barrier, sealability, convertability and biodegradability properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Changing trends in packaging materials has been driven by increasing environmental awareness as well as legislation. In this context, paperboard-based packaging have become increasingly popular due to its bio-based origin, potential biodegradability and physical properties. However, conventional systems lack behind in barrier performance and heat sealability. Hence, the addition of functional layers have been considered as alternative solutions to this challenge. Here we propose a biodegradable PLA-based polymer coating filled with minerals (0–10 wt% loading), namely, talc, kaolin and calcium carbonate, all of which were used in surface application on uncoated folding box board. For this purpose, we used a pilot-scale extrusion unit that produced materials that were tested for key properties. We found that the presence of filler in the PLA layer improved both water vapor (up to 16 %) and oxygen (up to 56 %) barrier properties. Moreover, the main effect of the fillers was observed in heat sealability, which was improved via adhesion at low temperatures, resulting in full fiber tear. Cup forming was less effected by filler loading in the PLA coating while repulping tests indicated the presence of large flakes of the polymer coating in 1 %-consistency slurries. Finally, biodegradability was slightly delayed in filler-containing samples (biodegradation within 10–60 days), most likely due to the nucleating effect of the fillers. Nevertheless, all the samples can be classified as biodegradable. Overall, our results represent a step forward in scale-up adoption of PLA-based coatings most useful in the development of packaging materials.
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.
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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it