Impact of preparation methods on dielectric and mechanical properties of graphene-epoxy nanocomposites
Bibliographic record
Abstract
Proper dispersion of graphene in epoxy is essential for enhancing nanocomposite properties and requires control of processing methods. Here, epoxy/graphene composites (E/G, 1% wt) were prepared via solution mixing. To evaluate the effect of additional dispersion treatments, the graphene/acetone suspensions before mixing with epoxy, or uncured E/G samples, were further mixed using an ultrasonic bath (UB) or ultrasonic probe (UP). The rheological properties of the uncured materials were analyzed performing small amplitude oscillatory shear (SAOS). The morphology of the cured samples was observed by optical microscopy and their electrical and mechanical properties by broadband dielectric spectroscopy, and dynamic mechanical analysis (DMA) respectively.The modulus of the composites varied depending on the treatment applied. Untreated E/G exhibited a lower storage modulus (G') at low frequencies compared to the neat epoxy. This is attributed to the slippage of aggregates within the composite, a phenomenon known as ""lubrication effect."" UB treatment of the E/G composite did not significantly impact G'. Conversely, applying UB (1 h) to the graphene/acetone suspension increased G’, indicating an improved graphene distribution, favored by processing at lower viscosity that may facilitates the ultrasonic wave propagation. Microscopy images confirmed reduced agglomerates, though average particle size remained unchanged.UP treatment was more effective, leading to a higher G' after 10 minutes at 25% amplitude. However, excessive UP treatment (75% amplitude for 30 minutes) reduced G'. The lower G' is associated with graphene sheet fragmentation, as observed in microscopy images showing reduced sheet size, potentially compromising reinforcement. Direct UP application to the E/G composite led to polymer degradation.The dielectric properties of neat epoxy and E/G composites were analyzed using a combination of Havriliak-Negami function and a conductivity term to take into account charge fluctuations. The relaxation time of the dipolar processes was further modeled with the Vogel-Fulcher-Tammann equation, providing the activation energy of the ?-relaxation mechanisms, reflecting molecular mobility restrictions. While untreated E/G showed activation energy similar to that of neat epoxy, UB treatment increased activation energy, suggesting improved graphene dispersion and greater restriction of molecular motion of the epoxy chains. This was confirmed by DMA, which showed increased storage modulus, loss modulus, and glass transition temperature, indicating a more rigid polymer with enhanced filler-matrix interactions.In conclusion, this study highlights the importance of optimizing processing parameters. The UP(25%-10min) applied to the graphene suspension improved the dispersion without fragmenting the sheets, resulting in higher activation energy and improved dynamic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".