Effect of specific surface area on the rheological properties of graphene nanoplatelet/poly(ethylene oxide) composites
Bibliographic record
Abstract
The rheological properties of poly(ethylene oxide) containing graphene nanoplatelets (GNPs) having different specific surface areas (SSAs) are studied using steady shear and small amplitude oscillatory shear experiments. A series of GNPs having SSAs ranging from 175 ± 5 to 430 ± 13 m2/g was prepared using a thermomechanical exfoliation process. The complex viscosity, moduli, and yield stress of the composites increase with SSA, whereas electrical and rheological percolation threshold concentrations decrease, suggesting that higher SSAs promote filler network formation. Modeling of small amplitude oscillatory shear data using a two-phase model confirms that hydrodynamic effects dominate at low concentrations below 8 wt. %, where the particles are noninteracting. At higher concentrations, the response is dominated by filler-phase contributions. We demonstrate that the two-phase model parameters can be used to track the exfoliation of graphite into GNPs. Fitting of rheological percolation curves using Utracki and Lyngaae–Jørgensen models at low concentrations (noninteracting regime) resulted in aspect ratios between 19 and 76. At high concentrations (interacting particles), the aspect ratios determined by the Krieger–Daugherty model ranged between 5 and 24 due to aggregation. The highest aspect ratios (defined as the ratio of major dimension to minor dimension) were associated with GNPs that had the highest SSA of 430 m2/g. Strain sweeps revealed that the critical strain for the onset of nonlinear viscoelasticity scaled with SSA above the percolation threshold. The scaling relationships of the critical strain and storage modulus with volume fraction were used to infer the fractal dimensions of filler networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| 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".