High-Performance Multiscale LNPs from Black Liquor as Repulpable Paper Coatings with Enhanced Water, Oil, and Vapor Resistance
Bibliographic record
Abstract
Technical lignin in alkaline pulping black liquor is usually combusted for energy in a soda recovery unit. To improve resource utilization, multiscale lignin nanoparticles (LNPs) were acid precipitated from black liquor and combined with gelatinized starch (SS) to produce a series of uniformly mixed surface coating agents to enhance the barrier properties of paper materials including water, oil, and water vapor resistance. The coating with 20%SS and 50%LNPs (based on oven-dried SS) with a diameter of 276 nm could significantly improve the barrier properties of the coated paper. Cobb 60 decreased by 79.6% from 67.98 to 13.86 g/m 2, Kit rating improved from 0 to 10, and water vapor transmission reduced from ≥3000 to 441.69 g/m 2 ·day. The coated paper presented superior mechanical properties, thermal stability, repulpability, and biodegradability. This work provides an effective strategy for the high-value application of pulping black liquor and holds promise for paper-based packaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".