Development of enhanced co-continuous PVDF/PET nanocomposites via synergistic effects of graphite particle size, hybrid systems, and reduced graphene oxide
Bibliographic record
Abstract
Abstract This study explores the development of electrically conductive co-continuous polyvinylidene fluoride/polyethylene terephthalate (PVDF/PET) nanocomposites incorporating graphite (GR) and reduced graphene oxide (rGO) for potential use in proton exchange membrane fuel cell (PEMFC) bipolar plates. The influence of GR particle size, concentration (20–60 wt%), and hybrid GR/GR and GR/GR/rGO combinations on electrical, thermal, mechanical, and water absorption properties was systematically investigated. Scanning electron microscopy revealed GR localization within the PET phase and the formation of a dense conductive network. The optimal composition, a hybrid G2/G3 (45/15 wt%) system, achieved low through-plane (0.93 Ω cm) and in-plane (0.71 Ω cm) resistivities, further reduced with 2 wt% rGO (0.89 Ω cm through-plane and 0.62 Ω cm in-plane). This formulation also exhibited superior thermal stability (onset degradation at ∼490 °C) and mechanical properties, with a flexural strength of 44.4 MPa and modulus of 16.4 GPa. Additionally, water absorption decreased significantly to 0.05 %. These findings demonstrate the potential of hybrid GR/rGO nanocomposites for enhanced durability and performance in PEMFC applications, offering a balance between electrical conductivity, mechanical strength, and environmental resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".