Natural rubber biocomposites reinforced with cellulose nanocrystals/lignin hybrid fillers
Bibliographic record
Abstract
Abstract In this study, a novel hybrid system containing lignin and cellulose nanocrystals (CNC) is developed to reinforce natural rubber (NR) and produce high‐performance biocomposites. Firstly, the effect of lignin content in lignin/NR biocomposites is investigated. Despite lignin's advantages as an inexpensive biopolymer, its addition to NR results in longer cure time, reduced tensile strength and increased loss factor (tan δ); that is, lignin addition has a negligible reinforcing effect compared to conventional fillers, such as carbon black (CB). On the other hand, adding only 7.5 parts per hundred rubber (phr) of CNC to lignin/NR compounds decreased the curing time (14%) and loss factor (55% at 10% strain), while increasing the bound rubber content (37%), modulus at 100% strain (101%) and tensile strength (36%). CNC/lignin/NR bionanocomposites exhibited comparable mechanical properties and even better dynamical mechanical properties (53% lower loss factor at 10% strain) than conventional composites reinforced with CB. The optimum lignin content in the NR composite was 40 phr, while the percolation threshold for CNC was around 7 phr.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".