A review on key factors influencing the electrical conductivity of proton exchange membrane fuel cell composite bipolar plates
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Fuel cells are gaining increasing importance as a promising alternative to traditional energy sources, primarily due to their exceptional efficiency and environmental advantages. The electrical performance of proton exchange membrane fuel cells (PEMFCs) largely depends on the effectiveness of proton and electron transport within the cell components. A critical factor impacting this efficiency is the electrical conductivity of polymer‐based bipolar plates (BPPs), which play a fundamental role as current collectors. BPPs in PEMFCs can be made from various materials including coated metallic materials, graphitic materials, and polymer composites. This review exclusively concentrates on polymer composite BPPs. Enhancing the overall cell performance is achievable through the integration of electrically conductive additives into the polymer matrix of these plates. Graphite (GR), carbon black (CB), carbon fibers (CF), carbon nanotubes (CNT), and graphene (Gr) all emerge as highly promising functional materials capable of substantially elevating BPPs performance. This study, among its various objectives, delves into the synergistic effects of these electrically conductive additives and their capacity to enhance the electrical conductivity within polymeric matrices. Furthermore, this review article thoroughly explores the influence of the polymeric matrix, encompassing co‐continuous morphology and processing conditions. In essence, it focuses on the improvement of BPPs electrical conductivity through innovative designs of their polymer‐based composites and nanocomposites and the particular selection of the electrically conductive fillers. The insights derived from this study significantly contribute to a more profound understanding of how to effectively harness the potential of this vital PEMFC component.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it