Poly(lactic acid) mass transfer properties
Bibliographic record
Abstract
Poly(lactic acid) (PLA), a biodegradable and compostable polymer, is gaining market acceptance and has been extensively investigated. The versatility of PLA has led to its broad and different applications in medical, agriculture, and food packaging fields. Similar to other polymers, PLA is permeable to gases, vapors and organic compounds. Thus, the mass transfer properties of PLA can influence its suitability for end-use applications. Here, we present a comprehensive, systematic, and critical review of more than 300 papers published since 1990 reporting the mass transfer properties of PLA, which include permeability, diffusion and solubility to gases, water vapor and organic vapors, along with migration of chemical compounds from PLA. Overall, PLA provides moderate barrier to gases, water vapor, and organic compounds. Barrier enhancement can be achieved through modifications such as blending with other polymers and formation of composite structures. Most of the mass transfer parameters reported in the literature are based on two-phase mobile amorphous and crystalline fractions, omitting the role of the restricted amorphous fraction, which can lead to unclear comprehension of PLA barrier properties as well as what affects those properties. Additional research is needed to address this shortcoming. This review provides an in-depth analysis of PLA mass transfer and a foundation for future research and commercial development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".