Fabrication and Performance of PBAT Composite Films Reinforced with High-Molecular-Weight Lignin Nanoparticles for Sustainable Packaging
Bibliographic record
Abstract
This study focuses on the incorporation of lignin nanoparticles (LNPs), prepared via antisolvent precipitation from fractionated lignin, into poly(butylene adipate- co -terephthalate) (PBAT) matrices and evaluates the effects of LNP particle size on the properties of PBAT composite films. The results demonstrate that, compared to the original lignin, LNPs acted as functional fillers, exhibiting excellent dispersion within the PBAT matrix. Strong hydrogen bonding interactions between LNPs and PBAT significantly improved the mechanical properties of the composite films, with an 8 wt % addition of LNPs leading to a 62.1% increase in tensile strength. High-molecular-weight LNPs (25 ∼ 116 nm), characterized by smaller particle sizes, achieved superior dispersion and compatibility within the PBAT matrix compared to low-molecular-weight LNPs. This improved dispersion contributed to enhanced tensile strength, UV-shielding capability, and barrier properties of the films. Additionally, PBAT films containing high-molecular-weight LNPs with a lower hydrophilic group content displayed higher antioxidant and antimicrobial activities. These findings highlight the potential of high-molecular-weight LNPs-reinforced PBAT composite films as sustainable food packaging materials. They also provide innovative strategies for the high-value utilization of lignin and the development of degradable high-performance packaging solutions.
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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".