Cold-plasma surface engineering induces multifunctional enhancements in biodegradable packaging films
Bibliographic record
Abstract
Background Biodegradable packaging materials made from polysaccharides, proteins, and other biobased polymers offer environmental benefits over synthetic plastics but often suffer from poor mechanical strength, water sensitivity, and weak barrier properties. These limitations hinder their broader adoption in food packaging applications. Cold plasma (CP), a non-thermal and solvent-free surface engineering technique, has emerged as a promising solution to enhance the functionality of biodegradable films without affecting their bulk characteristics. Scope and approach This review summarizes recent developments in CP technology for modifying biodegradable packaging materials. Key focus areas include plasma-induced changes in surface chemistry, wettability, and interfacial properties, as well as CP-assisted coating strategies and nanoparticle integration. Key findings and conclusions CP treatment introduces reactive species that etch and functionalize polymer surfaces, increasing surface roughness and incorporating polar groups. These modifications can improve wettability, coating adhesion, and compatibility with active agents. CP also enhances tensile strength, thermal stability, and barrier performance under optimized conditions. Moreover, it enables the synthesis and functionalization of nanomaterials, such as nanocellulose, starch, and protein-based nanoparticles, providing additional reinforcement and functionality to packaging films. Plasma-treated films have shown improved antimicrobial performance, particularly when combined with essential oils or metallic nanoparticles. While CP offers a green, tunable platform for developing multifunctional packaging systems, challenges related to treatment uniformity, scalability, and material-specific responses remain. Addressing these limitations through process optimization and standardization will be essential to support industrial translation and regulatory approval.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".