Innovative Poly(vinyl alcohol) (PVA)-Based Nanolayered Films: Balancing Mechanical and Gas Barrier Properties
Bibliographic record
Abstract
Poly(vinyl alcohol) (PVA), while offering exceptional gas barrier performance, faces significant challenges due to its extensive hydrogen bonding network. This structure limits its mechanical flexibility and creates processing difficulties, particularly during thermal melt processing, as the temperature window between melting and decomposition is narrow. To address these limitations, this study explores the multifunctional properties of nanostructured multilayer films composed of PVA and ethylene vinyl alcohol copolymer (EVOH). By engineering nanometric layers within the multilayer structure, we preserved the outstanding oxygen and water vapor barrier capabilities of the materials while enhancing the flexibility of the films. The findings reveal that reducing individual layer thicknesses to the nanoscale improves EVOH macromolecular mobility, leading to notable changes in thermal behavior. The formation of more regular crystalline structures and the complex interplay at the interfaces between PVA and EVOH layers significantly impedes the diffusion of small molecules across the film. Furthermore, mechanical testing demonstrates that increasing the number of layers enhances the ductility of the films, an effect attributed to the expanded interfacial area and a lower degree of crystallinity. These advancements highlight the potential for optimizing multilayer film structures to balance the barrier and mechanical performance.
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) | 0.000 | 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 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".