Surface-acylated lignin-rich fines as strength and functional fillers for micro/nano fibrillated cellulose
Bibliographic record
Abstract
Surface acylation of softwood thermomechanical pulp (TMP) fines (<76 μm) using succinic anhydride (SA) and imidazole effectively introduced carboxyl groups to both lignin and carbohydrate components, increasing the total carboxyl content to 0.6 mmol/g and up to 1.2 mmol/g in lignin. This chemical modification facilitated fibrillation during high-pressure homogenization, producing a fine fraction with uniform microscale morphology and cellulose nanofibrils containing 12 wt% lignin (LCNF). Films produced from these SA-modified fines exhibited enhanced mechanical properties, including a tensile strength exceeding 31 MPa and a Young's modulus over 3.9 GPa. When incorporated into micro/nanofibrillated cellulose (M/NFC) matrices at 30-40 % loadings, the modified fines significantly improved the mechanical performance of M/NFC-derived films, nearly doubling tensile strength (from 36 MPa to over 65 MPa) and modulus (from 1.8 to ∼3.6 GPa), while concurrently enhancing UV-blocking performance without compromising water vapor transmission. Additionally, incorporation of SA-modified fines in M/NFC-based cryogels (from 1 wt% suspensions) increased compressive strength (from 19 to 29 kPa) and modulus (from 0.18 to 0.35 kPa), demonstrating their value as reinforcing agents in lightweight, bio-based materials. Importantly, the inclusion of these fines preserved the ultralow density and exceptionally low thermal conductivity (0.0307 W/m·K) of the cryogels, underscoring their potential in thermal insulation applications.
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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".