Development and Characterisation of Electrically Conductive Polymeric‐Based Blends for Proton Exchange Membrane Fuel Cell Bipolar Plates
Bibliographic record
Abstract
Abstract The main objective of this work was to develop films with controlled dimensions for proton exchange membrane fuel cell (PEMFC) bipolar plates (BPPs) using the twin‐screw extrusion process. These films consisted of a low‐viscosity polyethylene terephthalate (PET) in which a mixture of high specific surface area carbon black (CB) and synthetic flake graphite (GR) were dispersed. A third conductive additive, consisting of silver‐coated glass particles (SCG) or multi‐walled carbon nanotubes (MWCNT), was also added at a low concentration (5 wt.‐%) in order to study its synergistic effect on the PET‐based blend electrical conductivity. As the developed blends had to meet properties suitable for PEMFC bipolar plate applications, they were characterised for their electrical through‐plane resistivity, mechanical properties and oxygen permeability. Through‐plane electrical resistivity of about 0.3 Ω·cm and oxygen permeation rate of 3.5 × 10–8 cc cm–2 s–1 were obtained for only 30 wt.‐% of a 60:40 mixture of CB/GR conductive additives. Although the substitution of 5 wt.‐% of CB/GR by the same amount of MWCNT had no significant effect on BPPs' electrical resistivity, it helped to improve their mechanical properties and especially their oxygen permeation, which was decreased from 3.5 × 10–8 cc cm–2 s–1 to around 0.6 × 10–8 cc cm–2 s–1.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".