Hybrid nanocellulose/carbon nanotube/natural rubber nanocomposites with a continuous three‐dimensional conductive network
Bibliographic record
Abstract
Abstract In this study, the hybrid effect of nanocellulose/carbon nanotube (NCC/CNT) reinforcement on natural rubber (NR) nanocomposites was investigated. To this end, three series of NR nanocomposites were prepared: NCC/NR, CNT/NR and NCC/CNT/NR. First, the nanocomposites morphology and the filler–rubber interactions were studied using scanning electron microscopy (SEM) and the swelling behavior in toluene, respectively. The results showed that the presence of NCC improved the NCC/CNT hybrid filler dispersion forming a 3D network, while the presence of CNT increased the filler–matrix interaction. The curing results also confirmed that the degree of crosslinking increased when hybrid fillers were used, but the curing time was not modified. In addition, it was observed that using a NCC/CNT hybrid system led to superior mechanical properties, dynamic mechanical properties and thermal conductivity than each material used separately. When 10 phr hybrid filler (with a filler ratio of 1) was added to NR, the tensile strength, modulus at 300% elongation (M300), storage modulus at 10% strain and thermal conductivity were all increased by 57%, 137%, 120%, and 30%, respectively. The results also showed that the NR nanocomposites properties can be controlled by tuning the NCC/CNT filler ratio.
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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".