Polypropylene Composites for Polymer Electrolyte Membrane Fuel Cell Bipolar Plates
Bibliographic record
Abstract
Abstract The polymer electrolyte membrane fuel cell (PEMFC) holds tremendous promise for a variety of mobile and stationary power generation applications and is the cornerstone of the anticipated hydrogen economy. One of the major factors limiting fuel cell commercialization is the development of bipolar plates since bipolar plates account for approximately 70% of the PEMFC weight, and 60% of the stack manufacturing and materials cost. The objective of this research is to investigate a feasibility of a conductive composite family to be used as bipolar plates in a PEMFC, in order to get the highly conductive, light weight, and low cost bipolar plates. This work utilized a combination of a polypropylene and low cost conductive filler materials: graphite, conductive carbon black, and carbon fibers. The components were combined in a batch mixer and injection molded into samples for testing with loadings up to 65%wt of fillers. The novel blends were tested for electrical conductivity, hydrophobicity, rheology, and actual plates (16 cm2) were tested in fuel cell testing trials. The impact of different types of fillers on the composite properties was evaluated, as well as the synergetic effect of mixtures of fill types within a polypropylene matrix. From the results, the highest conductivity, 1900 S/m (in‐plane) and 156 S/m (through plane), was obtained with the 65% composite. Moreover, the effects of additives such as coupling agents, and intrinsically conductive polymer (polypyrrole) were observed in this work. The electrical conductivity was influenced by polypyrrole added to the polypropylene composite.
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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) | 0.001 | 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) | 0.001 | 0.001 |
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".