Effect of filler additions on pilot-scale extrusion coating of paperboard with PLA-based blends
Bibliographic record
Abstract
Abstract We study the incorporation of minerals (talc, kaolin and surface-treated calcium carbonate) in paperboard coatings based on PLA to improve their performance, often limited by the low crystallinity and moderate gas barrier of the polymer. Masterbatches of PLA-based blends mixed with the mineral fillers were melt-blended in a twin-screw extruder and applied as a coating on paperboard in a pilot-scale unit operating at velocities up to 140 m/min. Thermal imaging was used during the extrusion coating and the effect of the fillers was investigated as far as processability and their effect on the mechanical performance. A reduction of neck-in and improved adhesion between the coating and the substrate were achieved at intermediate mineral loadings. Excess filler and low coating weight generated pinholes, leading to a reduction of the integrity and mechanical properties of the coatings. Overall, we define the performance window for continuous, pilot-scale coating of paperboard with a biopolyester filled with mineral particles, opening the opportunity to realize operations in industrial settings.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".