Effect of Crystallinity on Water Vapor Sorption, Diffusion, and Permeation of PLA-Based Nanocomposites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it