Physical Hybrid of Nanographene/Carbon Nanotubes as Reinforcing Agents of NR-Based Rubber Foam
Bibliographic record
Abstract
Natural rubber (NR) foams reinforced by a physical hybrid of nanographene/carbon nanotubes were fabricated using a two-roll mill and compression molding process. The effects of nanographene (GNS) and carbon nanotubes (CNT) were investigated on the curing behavior, foam morphology, and mechanical and thermal properties of the NR nanocomposite foams. Microscope investigations showed that the GNS/CNT hybrid fillers acted as nucleation agents and increased the cell density and decreased the cell size and wall thickness. Simultaneously, the cell size distribution became narrower, containing more uniform multiple closed-cell pores. The rheometric results showed that the GNS/CNT hybrids accelerated the curing process and decreased the scorch time from 6.81 to 5.08 min and the curing time from 14.3 to 11.12 min. Other results showed that the GNS/CNT hybrid improved the foam’s curing behavior. The degradation temperature of the nanocomposites at 5 wt.% and 50 wt.% weight loss increased from 407 °C to 414 °C and from 339 °C to 346 °C, respectively, and the residual ash increased from 5.7 wt.% to 12.23 wt.% with increasing hybrid nanofiller content. As the amount of the GNS/CNT hybrids increased in the rubber matrix, the modulus also increased, and the Tg increased slightly from −45.77 °C to −38.69 °C. The mechanical properties of the NR nanocomposite foams, including the hardness, resilience, and compression, were also improved by incorporating GNS/CNT hybrid fillers. Overall, the incorporation of the nano hybrid fillers elevated the desirable properties of the rubber foam.
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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".