Perspective on Polylactic Acid (PLA) based Sustainable Materials for Durable Applications: Focus on Toughness and Heat Resistance
Bibliographic record
Abstract
Evolution of the bioplastics industry has changed directions dramatically since the early 1990s. The latest generation is moving toward durable bioplastics having high biobased content. The main objective is to replace "fossil carbon" with "renewable carbon", a holistic strategy to mitigate climate change by minimizing the environmental impact of a product throughout its life cycle. Durable bioplastics is desired for multiuse long-term application in automotive, electronics and other industries. One necessary requirement for them is to be both tough and strong, yet the two attributes are often mutually exclusive. Does this mean a biobased and biodegradable polymer as polylactic acid (PLA) with its high strength but low toughness cannot be adopted for durable applications? Well, not exactly; this is where the concept of tailoring the properties of PLA to achieve stiffness–toughness balance along with acceptable heat resistance comes into play. In this perspective, we summarize the recent research progress in addressing the toughness vs strength and heat resistance conflict inherent in PLA. Blends having super toughness and composites based on the toughened PLA blends formulated to obtain desired material properties are covered. Morphology and crystallinity that individually contribute to toughness and heat resistance have also been elucidated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".