Carbon‐Encapsulated PtCo Intermetallic/Co‐N‐C Hybrid Catalyst for Ultralow‐Pt‐Loading Fuel‐Cell Catalysis
Bibliographic record
Abstract
ABSTRACT Developing high‐performance, low‐Pt catalysts for oxygen reduction reaction (ORR) is crucial for advancing proton exchange membrane fuel cells (PEMFCs), yet it remains an ongoing challenge. Herein, we report a structurally integrated catalyst featuring PtCo intermetallic nanoparticles encapsulated within a nitrogen‐doped carbon (NC) shell and supported on Co‐N‐C substrate. This catalyst achieves a high mass activity of 1.56 A mg Pt −1 at 0.9 V (iR‐free) and retains excellent stability, with only a 4 mV voltage decay at 0.8 A cm −2 after 60,000 cycles of accelerated durability testing in PEMFCs, even at an ultralow cathode Pt loading of 0.04 mg cm −2 . Through in situ spectroscopy and density functional theory calculations, we identify the mechanisms behind this performance. The NC shell not only acts as a physical barrier to improve stability but also actively promotes reaction kinetics by forming a hydrogen bond (N···*OOH) that breaks the scaling relationship of *OOH/*OH adsorption. Simultaneously, the Co‐N‐C support weakens the *OH adsorption for the optimized ORR kinetics and stabilizes the PtCo intermetallic nanoparticles through reinforcing metal‐support interactions. These synergic effects establish our catalyst as a leading candidate for low‐Pt PEMFCs and demonstrate that the structurally integrated design is a powerful paradigm for creating high‐performance Pt‐based ORR catalysts.
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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) | 0.000 | 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 | 0.000 |
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 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".