The rubber–filler interaction and reinforcement in styrene butadiene rubber/devulcanize natural rubber composites with silica–graphene oxide
Bibliographic record
Abstract
Ethoxy functionalized devulcanize natural rubber (DeVulcNR) is used as compatibilizer for silica/graphene oxide (SiO2@GO) hybrid fillers in the styrene butadiene rubber (SBR) to fabricate SBR composites. The dispersion behavior of SiO2@GO hybrid filler was investigated through scanning electron microscopy (SEM) analysis of the tensile fracture surface along with the broken rubber surface developed by plunging into liquid nitrogen. The rubber–filler interfacial interactions were evaluated through the measurement of equilibrium swelling experiment, fraction of immobilized polymer chain by DSC study, FTIR analysis, and molecular dynamics simulation. The results reveal that in the presence of DeVulcNR, the rubber–filler interaction is enhanced compared with that of the control formulations containing only SBR. SiO2@GO hybrid‐filler shows synergistic effect on the mechanical properties of the composites in the presence of DeVulcNR. The improved mechanical properties of the SiO2@GO hybrid filler rubber composites may be due to chemical interaction among the functional groups of SiO2 and GO with the DeVulcNR. Further, XRD study indicates that there is no significant layer‐by‐layer restack of GO in the SBR/DeVulcNR composites. The higher storage modulus and lower tan δ of the SiO2@GO hybrid filler rubber composites show superior interfacial interaction between rubber and filler compared with that of the control formulations. POLYM. COMPOS., 40:E1559–E1572, 2019. © 2018 Society of Plastics Engineers
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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".