Influence of Melt-Mixing Process Conditions on Mechanical Performance of Organoclay/Fluoroelastomer Nanocomposites
Bibliographic record
Abstract
Abstract In this study, Cloisite 20A, an organically modified Montmorillonite (Mnt), has been incorporated into Fluoroelastomer (FKM) through melt intercalation technique. Since the nanocomposite preparation method and conditions, and consequently, the resulting morphology play a critical role in the final properties, the effect of different process conditions such as time, temperature, and shear rate on the vulcanization, thermal and mechanical properties have been investigated. The morphology of nanocomposites, prepared at different melt-mixing conditions, was studied using X-ray diffraction (XRD). Rheological, thermal and mechanical behaviors were investigated by moving die rheometer (MDR), thermal gravimetric analysis (TGA), and tensile strength test respectively. Also, the crosslinking density has been measured for the nanocomposites. The best mechanical performance of clay/FKM nanocomposites was attained by optimization of the melt-mixing conditions. We achieved the following enhancements for FKM by clay incorporation: enhancement of tensile strength up to 70 %; elongation up to 94 %; and modulus up to 405 %. Process temperature was found to have a critical role in the final properties of the nanocomposites, while mixing residual time and shear rate had a moderate effect. The most desirable properties and curing behaviors, including highest maximum torques, cure rates, crosslinking densities, fast crosslinking kinetics, high intercalation and best improved tensile strengths, resulted with specific combination of melt-mixing parameters.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".