Enhancing bio-based polysaccharide/protein film properties with Natural Deep Eutectic Solvents (NADESs) and NADES-based bioactive extracts – A review
Bibliographic record
Abstract
• Biopolymer films can be formulated with Natural Deep Eutectic Solvents (NADESs). • Hydrophilic NADESs can improve film plasticity and mechanical stability. • NADES-based extracts can act as plasticizers and improve film functionality. • Hydrophobic NADESs may improve the water vapor permeability of biopolymer films. • Studies incorporating hydrophobic NADESs and NADES-extracts into films are limited. Biodegradable films show promise as eco-friendly alternatives to petroleum-based plastic films. However, single substrate-based biopolymer films have limitations like poor water-barrier qualities due to their hydrophilicity and low water resistance. Biopolymer films can be prepared by mixing proteins or polysaccharides to improve their physicochemical properties and may include additives such as bioactive compounds to confer added antioxidant and antibacterial characteristics. Recently, Natural Deep Eutectic Solvents (NADESs) have been a popular research topic. NADESs are green solvents with high extraction capabilities and are comprised of common metabolites that can act as film additives. However, few studies have explored the incorporation of NADESs into bio-based polysaccharide/protein film formulations and the effect on film properties for applications such as active food packaging. In this review, we discuss the limited research currently available on NADESs and NADES-based extracts as film additives. While most of the current studies have focused on hydrophilic NADESs and chitosan-based films, more research is needed to explore the incorporation of hydrophobic NADES extracts into different bio-based polysaccharide/protein films and the effect on film properties, such as water vapor permeability. Further consideration is given to the sustainability of this novel approach to film formulation and future prospects.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".