Morphological, rheological, and mechanical properties of hybrid elastomeric foams based on natural rubber, nanoclay, and nanocarbon black
Bibliographic record
Abstract
Abstract In this study, compression molding was used to produce elastomeric nanocomposite foams based on natural rubber (NR) with a hybrid reinforcing system containing organo‐modified nanoscale (NC) and nanocarbon black (NCB). The effect of NC content (0‐10 part per hundred rubber, phr) on the curing behavior, as well as the morphological and mechanical properties of elastomeric foams containing 10 phr of NCB was determined. Transmission electron microscopy and X‐ray diffraction results showed that NC exfoliation occurred at low NC concentration (less than 5 phr), while increasing NC content up to 5 phr led to aggregation. Rheological data revealed that increasing the NC content up to 10 phr gradually changed the curing parameters such as 50% shorter scorch and curing time, two times faster curing rate, as well as higher initial (35%) and final (35%) torque. Scanning electron microscopy analysis also showed that increasing NC content from 0 to 5 phr produced foams with more uniform small cells, while 7 phr of NC changed the foam structure into two areas composed of different cell sizes and different cell densities. Higher NC content (10 phr) led to broken cell walls. With nanoparticles, higher foam modulus (83%) and hardness (104%) were observed. Finally, NC addition was found to improve the NR's thermal and thermo‐oxidative resistance while the sound absorption coefficient was constant.
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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) | 0.000 | 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".