Silanization-Modified Lignin Nanoparticles for Paper Coating with Enhanced Liquid and Vapor Barriers, Frication Resistance, and Self-Cleaning Properties
Bibliographic record
Abstract
Paper’s inherent hydrophilicity and porosity cause inadequate barrier properties, failing under high humidity/temperature. This study successfully developed a hydrophobic nanocoating agent (xLNPs-OTS) through silanization modification using D276 (lignin nanoparticles with a diameter of 276 nm) as the substrate and OTS (octadecyltrichlorosilane) as the functionalizing agent. By applying the coating to paper surfaces followed by a hot-pressing process, the paper achieved comprehensive performance enhancements, including superior water, oil, and vapor barrier properties, thermal stability, mechanical strength, frictional resistance, and self-cleaning capabilities. The Cobb 60 value of LOTSC3.5T120t30 (the coating made from the OTS silanized lignin with the coating amount of 3.5 g/m2 and a hot-pressing at 120 °C for 30 min) coated paper is as low as 3.75 g/m2, and can withstand hot water at 100 °C for 60 min. The Cobb 60 value of the LOTSC20T120t30 (the coating made from the OTS silanized lignin with the coating amount of 20 g/m2 and a hot-pressing at 120 °C for 30 min) coated paper is reduced to 0.9 g/m2, the Kit grade is 6, and all coated papers are endowed with self-cleaning features. This study advances lignin’s high-value utilization, driving sustainable packaging and supporting eco-friendly paper material development.
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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".