Dielectrorheology of Aspect-Ratio-Tailored Carbon Nanotube/Polyethylene Composites under Large Deformations: Implications for High-Temperature Dielectrics
Why this work is in the frame
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Bibliographic record
Abstract
In this work, advanced custom-synthesized carbon nanotube (CNT)-based polymer nanocomposites were designed and prepared for electrical applications, with an innovative strategy in terms of catalyst preparation. The physical structures of the CNTs were fine-tuned by varying synthesis time to investigate the effect of aspect ratio on the electrical and rheological properties of the nanocomposites with state-of-the-art dielectro-nonlinear-rheological spectroscopy. The network structures of the nanotubes within the polymer matrix were studied utilizing a wide range of characterization techniques (e.g., linear rheology, electrical conductivity, dielectric properties, and electromagnetic interference shielding effectiveness). The results showed distinct differences in the microstructural features of the samples upon changing the aspect ratio of the CNTs. Then, for the first time, a correlation was developed between rheological and electrical properties with respect to deformation, under small, medium, and large amplitude oscillatory shear (SAOS, MAOS, and LAOS, respectively) flows. Dielectrorheological results depicted that the electrical properties of the nanocomposites containing longer CNTs are strain invariant while their rheological behaviors are highly sensitive to deformation. However, nanocomposites with shorter CNTs followed an opposite trend, which means there is a high dependency of electrical properties and a low sensitivity of rheological behavior to deformation. The output stress waveforms and Lissajous–Bowditch plots confirmed the aforementioned results. Our results disclose the importance of the physical properties of the nanomaterials on the performance of the high-temperature dielectrics and reveal that nanocomposites containing higher aspect ratio nanomaterials provide a more stable dielectric response when subjected to large deformations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it