Progress and perspective on thermoplastic composites for hydrogen fuel cells
Bibliographic record
Abstract
Proton Exchange Membrane Fuel Cells (PEMFCs) have emerged as a promising and efficient technology for clean energy conversion. However, the progression of this technology has stagnated due to various material issues, specifically concerning the bipolar plate components. Due to the corrosion issues related to metallic bipolar plates, and the poor mechanical properties associated with graphite bipolar plates, more attention has been dedicated to the application of Polymer Matrix Composites (PMC) within this field. This paper first explores the key aspects driven by the desire to overcome the complexities and challenges associated with graphite and metallic bipolar plates and further demonstrates the advancements made within thermoplastics composite bipolar plate design. Since the bipolar plate’s properties are partly dictated by the binding material and the content of functional additives, this review provides an integration of different types of binding materials used so far. In addition, the employed manufacturing approach, and the functional additive behavior (i.e., dispersion, orientation, interfacial adhesion) of the manufacturing approach have also been discussed in detail. Lastly, this review presents the prospects and suggests areas for future research in the field. It highlights potential avenues for further studies and provides recommendations for future work that can contribute to advancements in the design and development of bipolar plates using thermoplastic composite materials
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".