Durable Polylactic Acid (PLA)-Based Sustainable Engineered Blends and Biocomposites: Recent Developments, Challenges, and Opportunities
Bibliographic record
Abstract
The paper comprehensively reviews durable polylactic acid (PLA)-based engineered blends and biocomposites supporting a low carbon economy. The traditional fossil fuel derived nonrenewable durable plastics that cannot be circumvented have spawned increased environmental concerns because of the continuous rise of their carbon footprint during processing and disposal. It is anticipated that the production of biodegradable and nonbiodegradable (durable) plastics from the year 2020 to 2025 will rise ∼47% and ∼21%, respectively. The carbon footprint can be reduced in durable (nonrenewable) plastics by decreasing or replacing the "fossil carbon" content with "renewable carbon" content. The replacement will enable us to attain a sustainable environment, a low carbon footprint, energy security, and effective resource management. Thus, PLA-based durable products need to be developed with an enhanced service life that strikes a balance between environment-friendliness and product performance for engineering high-performance applications. The recent progress for enhancing the durability of PLA-based products consisting of hybrid nonrenewable and renewable carbon has been attained by incorporating synthetic plastics, synthetic fibers (glass and carbon), natural fibers, and other biofillers (biocarbon). Further, the effects of additives such as initiators, nucleating agents, chain extenders, compatibilizers, impact modifiers, and toughening agents to prepare such blends and composites have been discussed. This Review further critically examines the advances centering on processability, heat resistance, flame retardancy, strength, and toughness. In addition to that, current and prospective applications such as automotive, electronic, medical, textile, and housing of PLA-based products are discussed. However, the challenges for tailoring durable PLA-based products that still need to be addressed, such as improved processability, striking stiffness–toughness balance, enhanced heat resistance, and improved interfacial adhesion between the polymer–polymer, polymer–filler, and hybrid polymer–filler in respective polymer blends, composites, and hybrid composites, are summarized and analyzed in this Review. Hence, the opportunities for improvement to overcome the challenges lie ahead.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".