PGM-Free Fe/N/C and Ultralow Loading Pt/C Hybrid Cathode Catalysts with Enhanced Stability and Activity in PEM Fuel Cells
Bibliographic record
Abstract
In this work, the stability behaviors of the state-of-the-art Fe/N/C and Pt/C catalysts (as well as the activation time of the latter) were first systematically investigated, under different cathode catalyst loadings, in the membrane electrode assemblies (MEA) in PEM fuel cells. Based on that, two types of cathode electrodes with the combination of Fe/N/C and Pt/C catalysts were developed (type I: layered hybrid catalysts with Pt/C next to the membrane and type II: uniformly mixed catalysts). In this way, the shortcomings of the Fe/N/C catalyst (the fast decay) and the Pt/C catalyst (the long activation time) can be compensated at the same time. The hybrid catalysts also showed a very short activation time (a few hours vs over 10 h for Pt/C with the same Pt loading). Comparing the two types of hybrid catalysts, type I shows a much higher current density. The loadings of the Fe/N/C and Pt/C catalysts in the hybrid electrode were systematically studied, with optimal values of 1.0 mg cm–2 for Fe/N/C and 0.035 mgPt cm–2 for Pt/C. The Pt loading of this hybrid catalyst (type I) at the cathode only takes ca. 30% of the U.S. Department of Energy (DOE) target of Pt usage (0.100 mgPt cm–2), while its mass activity of Pt (in H2/O2 PEMFC) is 0.22 A mgPt–1 at 0.9iR-free V, reaching half of the DOE activity target (0.44 A mgPt–1), which is among the best performances reported so far. Via both half-cell and single-cell electrochemical evaluations together with other characterizations, the origin of the improved activity and stability is believed to be the synergistic effect between Pt/C and Fe/N/C catalysts to ORR. This work provides an effective strategy for engineering highly performing MEA for the industrialization of PEM fuel cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".