Progress in bio-based plastics and plasticizing modifications
Bibliographic record
Abstract
Over the coming few decades bioplastic materials are expected to complement and gradually replace some of the fossil oil based materials. Multidisciplinary research efforts have generated a significant level of technical and commercial success towards these bio-based materials. However, extensive application of these bio-based plastics is still challenged by one or more of their possible inherent limitations, such as poor processability, brittleness, hydrophilicity, poor moisture and gas barrier, inferior compatibility, poor electrical, thermal and physical properties. The incorporation of additives such as plasticizers into the biopolymers is a common practice to improve these inherent limitations. Generally, plasticizers are added to both synthetic and bio-based polymeric materials to impart flexibility, improve toughness, and lower the glass transition temperature. This review introduces the most common bio-based plastics and provides an overview of recent advances in the selection and use of plasticizers, and their effect on the performance of these materials. In addition to plasticizers, we also present a perspective of other emerging techniques of improving the overall performance of bio-based plastics. Although a wide variety of bio-based plastics are under development, this review focuses on plasticizers utilized for the most extensively studied bioplastics including poly(lactic acid), polyhydroxyalkanoates, thermoplastic starch, proteinaceous plastics and cellulose acetates. The ongoing challenge and future potentials of plasticizers for bio-based plastics are also discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".