High temperature proton exchange membrane fuel cells: progress in advanced materials and key technologies
Bibliographic record
Abstract
High temperature proton exchange membrane fuel cells (HT-PEMFCs) are one type of promising energy device with the advantages of fast reaction kinetics (high energy efficiency), high tolerance to fuel/air impurities, simple plate design, and better heat and water management. They have been expected to be the next generation of PEMFCs specifically for application in hydrogen-fueled automobile vehicles and combined heat and power (CHP) systems. However, their high-cost and low durability interposed by the insufficient performance of key materials such as electrocatalysts and membranes at high temperature operation are still the challenges hindering the technology's practical applications. To develop high performance HT-PEMFCs, worldwide researchers have been focusing on exploring new materials and the related technologies by developing novel synthesis methods and innovative assembly techniques, understanding degradation mechanisms, and creating mitigation strategies with special emphasis on catalysts for oxygen reduction reaction, proton exchange membranes and bipolar plates. In this paper, the state-of-the-art development of HT-PEMFC key materials, components and device assembly along with degradation mechanisms, mitigation strategies, and HT-PEMFC based CHP systems is comprehensively reviewed. In order to facilitate further research and development of HT-PEMFCs toward practical applications, the existing challenges are also discussed and several future research directions are proposed in this paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".