Functionalized lignin nanoparticles prepared by high shear homogenization for all green and barrier-enhanced paper packaging
Bibliographic record
Abstract
Paper-based materials made from cellulose have been sought after as a sustainable and inexpensive packaging option. However, the porous structure and high hydrophilicity of paper-based materials result in inadequate water and oil repellency, as well as a limited water vapor barrier. In this work, lignin nanoparticles (LNPs) were prepared using a high-speed homogenizer, and subsequently coated on base paper along with cationic starch to enhance its multi-barrier performance to facilitate the packaging application. The LNPs obtained through such a facile process formed stable colloidal dispersion in water, which exhibited excellent interfacial compatibility with cationic starch. During the coating process, a highly adhesive emulsion consisting of cationic starch and LNPs were coated on the surface of base paper, imparting good hydrophobic properties to the paper. The resulting paper material exhibited good water resistance (Cobb value of 37.5 g m−2), high oil resistance (Kit rating 9) and tensile strength (48.93 MPa). The reduction in water vapor transmission rate (WVTR) exceeds sixfold. This study provides a new avenue for the application of lignin in high-barrier, fluorine-free, water-and oil-resistant packaging materials.
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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".