Tailoring Carbon Additives Synergy in Co‐Continuous <scp>PVDF</scp> / <scp>PET</scp> Nanocomposites for Enhanced <scp>PEM</scp> Fuel Cell Bipolar Plates
Bibliographic record
Abstract
ABSTRACT The advancement of proton exchange membrane fuel cells (PEMFCs) requires the development of bipolar plates (BPPs) that are lightweight, mechanically and thermally performant, and possess adequate electrical conductivity. This study investigates co‐continuous nanocomposites based on a blend of polyvinylidene fluoride (PVDF) and polyethylene terephthalate (PET), reinforced with hybrid electrically conductive fillers. A total filler loading of 60 wt.% was employed, comprising combinations of graphite (GR), carbon black (CB), carbon fiber (CF), and multi‐walled carbon nanotubes (MWCNTs). Morphological analysis revealed that, beyond the co‐continuous structure of the PVDF/PET matrix and the preferential localization of the conductive fillers within the PET phase, the strategic combination of micro‐ and nanoscale fillers promoted the formation of continuous conductive networks. Thermogravimetric analysis indicated excellent thermal stability, with a maximum decomposition temperature of 438°C for graphite‐rich formulations, accompanied by an ash content of ~80%. Electrical resistivity was markedly reduced through hybrid filler incorporation, with the best‐performing formulation (2.5/7.5/3.0 wt.% CF/CB/MWCNTs) achieving 0.087 Ω·cm (through‐plane) and 0.042 Ω·cm (in‐plane). Mechanical characterization showed that CB enhanced flexural strength, CF increased stiffness, and MWCNTs improved overall mechanical integrity, underscoring the synergistic effect of the filler system. Water uptake remained low across all samples, with a minimum of 0.023% observed for 1 wt.% MWCNTs after 168 h, and only 0.068% at 3 wt.%. Polarization curve analysis demonstrated that the inclusion of 2.5 wt.% CF and 7.5 wt.% CB significantly enhanced the electrochemical performance of PEMFCs, validating the potential of these hybrid‐filled PVDF/PET nanocomposites for next‐generation BPP applications.
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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 | 0.001 |
| 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 | 0.001 | 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".