Development of multilayer PVA-MMT and PVA-CS film structures by spin coating-assisted layer-by-layer technique: Effect of PVA, CS and MMT nanoclay orientation on oxygen barrier properties
Bibliographic record
Abstract
The orientation of biopolymer macromolecules and nanoclay, specifically Montmorillonite (MMT) nanoplatelets, plays a crucial role in controlling the properties of multi-layer film structures. Understanding the impact of macromolecule and nanoclay platelets’ orientation on barrier properties of packaging films is essential. To investigate the influence of hydrogen bonding in multilayer films structures, two polymers, namely polyvinyl alcohol (PVA) and chitosan (CS), were selected and laminated nanocomposite films were fabricated using the spin coating-assisted layer-by-layer (Spin-LbL) assembly technique. This technique facilitates the production of highly oriented nanocomposite films, where polymer chains and nanoclay particles align parallel to the film surface. Multi-directional 2-D wide-angle X-ray diffraction (2D-WADX) was successfully used to accurately assess the orientation and distribution of MMT nanoplatelets. Additionally, the films underwent characterization using X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The 2D-WADX analysis revealed a parallel alignment of both the PVA chains and MMT clay nanoplatelets parallel to film surface. The XRD results confirmed the formation of intercalated nanolaminate structures, hydrogen-bonding interactions, and adjustments in the crystalline structure of PVA matrix. Through contact angle and oxygen permeability measurements, we observed that all quadri-layer film structures exhibited hydrophobic properties and reduced oxygen permeability compared to neat PVA films. Furthermore, the integration of MMT nanoclay, even at low concentrations, contributed to the development of nanocomposite films with improved oxygen barrier properties. Consequently, the quadri-layer films demonstrate great potential for food packaging applications.
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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 | 0.000 | 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".