Preparation and characterization of silicone rubber/graphene nanosheets nanocomposites by in-situ loading of the coupling agent
Bibliographic record
Abstract
Inexpensive approach to fully disperse graphene nanosheet (GNS) in silicone rubber (SR) by the addition of (3-Aminopropyl) triethoxysilane (APTES) as the coupling agent is presented in this study. The effects of GNS loading and presence of APTES on the cure characteristics, dynamic-mechanical, rheological and mechanical properties of the resulting SR compounds were systematically studied by rheometry, DMTA and tensile testing, respectively. The obtained results were correlated with the microstructure of the samples investigated by SEM and TEM analyses. Vulcanization curves revealed that the GNS and the coupling agent had an accelerating effect on the cure kinetics of the SR compounds leading to a steady decrease in scorch time and optimum cure time along with a gradual increase in the effective torque value. Morphological results showed that the GNSs could disperse more homogeneously within SR matrix using a simple solution mixing approach by in-situ loading of APTES. DMTA results showed restricted relaxation processes in GNS-reinforced SR systems in comparison with the pure SR, with more pronounced effect for the system containing APTES owing to improved interactions between graphene and SR which prevented the molecular mobility of neighboring chains of SR matrix. The tensile data demonstrated about 20% rise of modulus in the GNS-filled rubber nanocomposites in the presence of APTES. Low-frequency rheological properties including the storage modulus (G′), the loss modulus (G″), and complex shear viscosity (η*) showed a significant increase of about 10-fold, 75% and 20%, respectively, with the incorporation of APTES and GNS. Thus it could be expected that APTES had a substantial potential to be applied in-situ as the coupling agent to fabricate SR/GNS nanocomposites with exfoliated GNS morphology and increased the rheological and mechanical properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".