Poly Lactic Acid (PLA) Nanocomposites: Effect of Inorganic Nanoparticles Reinforcement on Its Performance and Food Packaging Applications
Bibliographic record
Abstract
Poly lactic acid (PLA) is a compostable, as well as recyclable, sustainable, versatile and environmentally friendly alternative, because the monomer of PLA-lactide (LA) is extracted from natural sources. PLA's techno-functional properties are fairly similar to fossil-based polymers; however, in pristine state, its brittleness and delicacy during processing pose challenges to its potential exploitation in diverse food packaging applications. PLA is, therefore, re-engineered to improve its thermal, rheological, barrier and mechanical properties through nanoparticle (NP) reinforcement. This review summarises the studies on PLA-based nanocomposites (PLA NCs) developed by reinforcing inorganic metal/metallic oxide, graphite and silica-based nanoparticles (NPs) that exhibit remarkable improvement in terms of storage modulus, tensile strength, crystallinity, glass transition temperature (Tg) value, antimicrobial property and a decrease in water vapour and oxygen permeability when compared with the pristine PLA films. This review has also discussed the regulations around the use of metal oxide-based NPs in food packaging, PLA NC biodegradability and their applications in food systems. The industrial acceptance of NCs shows highly promising perspectives for the replacement of traditional petrochemical-based polymers currently being used for food packaging.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".