Synergistic effect between graphene nanoplatelets and carbon black to improve the thermal and mechanical properties of natural rubber nanocomposites
Bibliographic record
Abstract
In this study, we focused on the synergistic effect between carbon black (CB) and graphene nanoplatelets (GNPs) of various aspect ratios and specific surface areas as hybrid fillers in natural rubber (NR) nanocomposites. Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and scanning electron microscopy (SEM) were carried out to characterize the GNPs properties, while dynamic mechanical analysis (DMA), tensile properties, hardness, thermal conductivity, swelling behavior in toluene and SEM were performed on the NR nanocomposites. The results showed the positive effect of GNPs on the thermal and mechanical properties, which was attributed to the high surface area and aspect ratio of the GNPs playing a vital role in producing a conductive GNPs/CB hybrid fillers’ network. Among the three GNPs investigated, the sample having the highest lateral dimension (25 µm) led to a denser and more thermally conductive network. On the other hand, the GNPs/CB hybrid fillers’ synergy increased with increasing concentration inside the NR nanocomposites up to 5 phr due to their good dispersion as confirmed via SEM.
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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) | 0.001 | 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".