Effects of Lignin Content on Mechanical and Thermal Properties of Polypropylene Composites Reinforced with Micro Particles of Spray Dried Cellulose Nanofibrils
Bibliographic record
Abstract
In this work, the use of microsized cellulosic particles obtained from spray dried cellulose nanofibrils with high lignin content (>20 wt %) were explored for the first time as reinforcement in polypropylene (PP) composites. Their effect was compared with the results from PP composites reinforced by cellulosic particles of spray dried cellulose nanofibrils with a low lignin content (<5 wt %). Cellulose nanofibrils with diameters less than 100 nm were obtained by mechanically fibrillating unbleached and bleached cellulosic fibers obtained from tree bark after alkaline extraction for removal of extractive. These cellulose nanofibrils were then spray dried to microsized high lignin content cellulose particles (HLCP) and low lignin content cellulose particles (LLCP), respectively. The presence of a large amount of lignin in the nanofibrils alleviated the degree of aggregation during the spray drying process. Both HLCP and LLCP were melt compounded with polypropylene (PP) to make composites films with different cellulosic particle loading levels. Compared to LLCP, HLCP significantly improved water repellency, thermal stability, and tensile properties of the composites films. With an addition of 5 wt % HLCP in PP, the tensile strength and modulus of the composites increased by 25.3% and 41.5% compared to neat PP, respectively. However, composites containing 5 wt % LLCP experienced a decrease in tensile strength by nearly 23.0% instead. Moreover, compatibilizing and stabilizing effects of lignin were also observed during the processing of the composites. This study demonstrated strong potential of HLCP as biobased reinforcement filler in plastic composites.
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".