Optimization of PVA/TiO2/MMT mixed matrix membrane for food packaging
Bibliographic record
Abstract
Abstract Nanocomposite films performance parameters, including barrier and properties for packaging films, can be affected by variables such as the type and concentration of nanoparticles. In this investigation, Polyvinyl alcohol (PVA) nanocomposite films were prepared by solution casting method with different combinations of Montmorillonite (MMT) platelets and Titanium Oxide (TiO2) spherical nanoparticles.A support vector machine (SVM) was implemented to study the thin nanocomposite films' behavior to changes in the independent variables. The SVM model predicted oxygen transmission rate (OTR), water vapor permeability (WVP|), Young ̓s Modulus (YM), ΔE, opacity, tensile strength (TS), and elongation at the breakpoint (EB) with an error of less than 6.43%. A Genetic Algorithm (GA) was applied to find the optimal nanoparticle concentration to achieve optimum film performance. Therefore, the results exhibited that the optimum film performance depends on the type and concentration of nanoparticles. Results show that the optimum loading of nanoparticles in this research should be between 0.5-1 wt% for TiO2 and 2.5–3.5 wt% for MMT.
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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) | 0.000 | 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".