Exploration of Multifunctional Properties of Piper Betel Leaves Extract Incorporated Polyvinyl Alcohol-Oxidized Maize Starch Blend Films for Active Packaging Application
Bibliographic record
Abstract
Abstract This study explains the development of eco-friendly polyvinyl alcohol (PVA)/oxidized maize starch (OMS)/Betel leaves extract (BLE) blend films by employing a cost effective technique. The influence of BLE on structural, Thermal, Mechanical, Morphological, Optical, Antibacterial, and Antioxidant properties of PVA/OMS blend were investigated successfully by applying FTIR, TGA, DSC, UTM, SEM, UV spectroscopy, In vitro antibacterial and DPPH scavenging activity. The hydrogen bonding interaction among the components of the blend films led to enhance thermal stability, miscibility, mechanical properties, smooth surface morphology and UV blocking properties of the blend films. The addition of BLE enormously improved the antibacterial and antioxidant properties of PVA/OMS blend films. Moreover, the water contact angle, solubility, biodegradability, Water vapour transmission rate, oxygen permeability properties of blend films were analyzed, which illustrates that the obtained films were hydrophilic, water soluble and biodegradable in nature. With regard to barrier properties the WVTR and oxygen permeability of the blend films were enhanced after doping of BLE. These experimental outcomes suggest that the BLE containing PVA/OMS films can be used for the expansion of active packaging material.
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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) | 0.001 | 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".