Biocomposites from Thermoplastic Postindustrial Waste Starches Filled with Mineral Fillers for Single‐Use Flexible Packaging
Bibliographic record
Abstract
Abstract Plasticized starch (PS) materials hold a fundamental role in the design of novel biodegradable and compostable plastics aimed to reduce global single‐use plastic wastes. This work exemplifies the plasticization of postindustrial waste wheat starch (pWWS) and postindustrial corn starch (pWCS) with a comparison to food‐grade corn starch (pCS) by introducing technical waste glycerol (25%) along with urea (5%) and water. The PS (30%) is blended with 70% poly(butylene adipate‐ co ‐terephthalate) (PBAT) to design a matrix with optimized mechanical properties. As a result, the inclusion of pWCS and pCS shows similar impact strength and melt flow index (MFI) values. Further, the mineral fillers, i.e., talc and calcium carbonate (CaCO 3 ) (25%), are introduced into the PBAT/PS (70%/30%) matrix to explore the effect on MFI, mechanical, thermal, and morphological properties. Scanning electron microscopy analysis reveals good dispersion of PS particles and mineral fillers into the PBAT. Adding talc into the PBAT/pWWS and PBAT/pCS blends shows significantly higher modulus (≈134% and ≈105%, respectively) and heat deflection temperature (HDT) (7.8% and 9.3%, respectively). The addition of 25% CaCO 3 results in the impact strength of PBAT/pWWS being improved by ≈20%. Due to talc's lamellar platelet structure and better dispersion, the inclusion of talc into the PBAT/PS blends shows improved rheological properties compared to that of CaCO 3 .
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".