Innovative home-compostable modified starch coatings with enhanced heat sealability for paper packaging
Bibliographic record
Abstract
The rapid rise in packaged food consumption has intensified plastic waste generation, with packaging materials forming a major fraction of municipal solid waste. This study presents the development of home-compostable starch-based coatings as a sustainable alternative for paper-based flexible food packaging. Two modified starches sodium starch octenyl succinate (SSOS) and maltodextrin (MAL) were combined with plasticizers (sorbitol and glycerol) to optimize sealing performance. The coated papers were characterized using thermal (DSC), structural (ATR-FTIR), rheological, and surface analyses to assess their functional properties. Optimized formulations significantly reduced the seal initiation temperature (SIT) and improved fiber tear temperature (FTT), ensuring stronger adhesive bonding. Enhanced surface roughness and abrasion resistance further contributed to better sealability. FTIR and DSC confirmed increased polymer chain mobility and favorable intermolecular interactions consistent with improved sealing. All selected coatings demonstrated strong blocking resistance and stability under ambient storage. These findings highlight the potential of starch-based coatings to provide both functional performance and environmental sustainability in food packaging. The proposed materials offer a viable alternative to petroleum-based films, supporting the transition toward compostable, waste-reducing solutions in the food manufacturing sector.
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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".