Conductive Materials for Proton Exchange Membrane Fuel Cell Bipolar Plates Made from PVDF, PET and Co‐continuous PVDF/PET Filled with Carbon Additives
Bibliographic record
Abstract
Abstract The aim of this work was to develop and characterise electrically conductive materials for proton exchange membrane fuel cells and bipolar plates (BPPs). These BPPs were made from highly conductive blends of polyethylene terephthalate (PET) and polyvinylidene fluoride (PVDF), as matrix phase. The conductive materials were developed from carefully formulated blends composed of conductive carbon black (CB) powder and, in some cases, graphite synthetic flakes mixed with pure PET, PVDF or with PVDF/PET systems. They were first developed by twin‐screw extrusion process then compression‐molded to give BPP final shape. As the developed blends have to meet properties suitable for BPP applications, they were characterised for their rheological properties, electrical through‐plane resistivity (the inverse of conductivity), oxygen permeability, flexural and impact properties. Results showed that lower resistivity was obtained with PVDF/CB blends due to the higher interfacial energy between the PVDF matrix and CB and also the higher density and crystallinity of PVDF, compared to those of PET. It was also observed that the lowest resistivity values were obtained with mixing PVDF and PET at controlled compositions to ensure PVDF/PET co‐continuous morphology. Also, slow cooling rates helped to attain the lowest values of through‐plane resistivity for all studied blends. This behaviour was related to the higher crystallinity obtained with low cooling rates leading to smaller amorphous regions in which carbon particles are much more concentrated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".