Effect of Crystallinity on Water Vapor Sorption, Diffusion, and Permeation of PLA-Based Nanocomposites
Bibliographic record
Abstract
The effects of crystalline morphology and presence of nanoparticles such as cellulose nanofibers (CNFs), organically modified nanoclay (C30B), or a combination of both on water vapor sorption and diffusion in polylactide (PLA) were evaluated by a quartz spring microbalance (QSM). It was found that the large spherulite size induced by high-temperature processing leads to an increase in water sorption and a substantial reduction of diffusion with increasing crystallinity. Contrarily, small-sized spherulites, arising after low-temperature processing during solvent-casting, showed a different behavior with a slight decrease in both water vapor sorption and diffusion with increasing crystallinity. These observations suggest that solvent-casting at low temperatures should not be used to predict the properties a material will show after industrial-scale processing. From the analysis of the nanocomposite materials, it was concluded that nanoparticles affected the material's properties not only by themselves but also by modifying the crystalline morphology. Interestingly, this led to CNF showing similar performance to C30B, decreasing water diffusivity (21 vs 27%) on isothermally crystallized materials despite its less favorable geometry. Additionally, the incorporation of 1 wt % CNF and C30B decreased water vapor transmission rate (WVTR) by 24% under an amorphous state but by 44% in a crystallized state, which makes hybrid CNF/C30B composites a promising food packaging material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".