Improving paper-based packaging with home compostable modified starch coatings: a focus on heat seal optimization
Bibliographic record
Abstract
In the quest for sustainable packaging solutions, this study pioneers the development of home-compostable coatings that transform the heat sealability of paper-based flexible food packaging—a longstanding challenge in the industry. Leveraging sodium starch octenyl succinate (SSOS) and maltodextrin (MAL), compostable starch derivatives, combined with sorbitol (SOR) and glycerol (GLY) as plasticizers, we optimized critical sealing parameters: seal initiation temperature (SIT) and fiber tear temperature (FTT). Using an innovative central composite design (CCD) and response surface methodology (RSM), the research reveals the interplay between material composition and sealing performance, uncovering unprecedented efficiency in achieving low SIT and FTT values. Remarkably, the optimal formulations achieved SIT and FTT values as low as 120 °C and 147 °C for SSOS (10 % SOR, 20 % GLY) and 112 °C and 125 °C for MAL (20 % SOR, 5 % GLY), outperforming traditional alternatives while maintaining full compostability. The statistical framework demonstrated exceptional predictive accuracy (R 2 > 0.85) and precision (CV < 7.4 %), underscoring the reliability and scalability of these formulations. This groundbreaking approach bridges the gap between sustainability and functionality, setting a new standard for home compostable packaging materials. By providing a scalable pathway to reduce environmental impact without compromising performance, this study offers transformative insights for the packaging industry and positions itself as a cornerstone for future innovations in sustainable food packaging.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".