Optimizing Conductive Polymer Composites: The Role of Graphite Particle Size and Concentration in PVDF, PP, and PET Matrices
Bibliographic record
Abstract
This study investigates the impact of graphite (GR) concentration and particle size on the performance of conductive polymer composites (CPCs) using polyvinylidene fluoride (PVDF), polypropylene (PP), and polyethylene terephthalate (PET) as matrix materials. Composites were prepared with GR concentrations ranging from 20 to 60 wt. % and particle sizes categorized as G1 (5.9 µm), G2 (17.8 µm), and G3 (561 µm), and evaluated for their electrical, thermal, and mechanical properties. The investigation of the effect of graphite particle size on composite properties represents the main originality of this work. Among all composites, PVDF containing 60 wt. % of medium-sized G2 particles exhibited the lowest electrical resistivity (0.77 ohm·cm through-plane and 0.69 ohm·cm in-plane), along with the highest residual ash content (72%). In PP and PET matrices, incorporating 60 wt. % G2 particles resulted in through-plane resistivities of 11.3 ohm·cm and 1.6 ohm·cm, and in-plane resistivities of 5 ohm·cm and 1.2 ohm·cm, respectively, with thermal decomposition temperatures of 374 °C and 401 °C. Regarding mechanical performance and thermal stability, composites with small-sized G1 particles demonstrated superior performance due to their larger surface area and stronger matrix interactions. The PVDF/G1 (40/60 wt. %) composite achieved the highest flexural modulus (6.8 GPa), flexural strength (38.6 MPa), compressive modulus (0.28 GPa), and decomposition temperature (445 °C), highlighting its exceptional properties. These CPCs show significant promise for energy and electronic applications, particularly in the fabrication of bipolar plates for proton exchange membrane fuel cells, as well as in shielding materials and thermoelectric devices.
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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 | 0.000 |
| 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".