Facile Preparation of Pt <sub>3</sub> Co@Pt Core@Shell Nanoparticles as Cathode Catalysts for Fuel Cell Applications
Bibliographic record
Abstract
Proton exchange membrane fuel cells (PEMFCs) offer an efficient way to generate electricity from sustainable fuels. However, the preparation of catalysts with reduced Pt and improved activity for the oxygen reduction reaction (ORR) continues to be a challenge. In this study, Pt 3 Co@Pt core@shell nanoparticles supported on hybrid carbon materials are prepared through a one-pot, two-step procedure: Pt and Co are first coreduced to form the Pt 3 Co core, followed by the overgrowth of a Pt shell. This approach provides simplified synthesis with fewer steps, low-cost equipment, mild conditions, high yield, and potential for large-scale production. The Pt 3 Co@Pt nanoparticles have an average size of 3.99 nm, with an average Pt shell thickness of 0.71 nm. The Pt 3 Co@Pt/C catalyst exhibits a 2.31-fold higher electrochemical surface area (ECSA, 109 m 2 /g) and a 1.86-fold higher mass activity (MA, 446 mA/mg pt ) compared to the commercial Pt/C in half-cell testing. Single-cell test results indicate that the membrane electrode assembly (MEA) made with Pt 3 Co@Pt/C achieves a peak power density of 0.80 W/cm 2 at a backpressure of 100 kPag, compared to 0.71 W/cm 2 for the MEA using commercial Pt/C. This activity enhancement is attributed to the optimized particle size and synergetic effect between Pt and Co within the core@shell configuration. The Pt shell serves as a protective layer, leading to less than 15% loss of ECSA and MA after the durability test, compared with over 20% loss for commercial Pt/C. Such results demonstrate the feasibility of improving ORR performance by constructing core@shell catalysts through a simple, scalable method, supporting their practical applications in PEMFCs.
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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 | 0.001 |
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".