In situ formation of sub nanometer cobalt particle with platinum nanocrystal for high performance oxygen reduction reaction electrocatalyst
Bibliographic record
Abstract
• Co sub nanometer particles diffused in Pt nanocrystal and carbon support is synthesized. • The as developed catalyst exhibits outstanding halfwave potential surpassing benchmark Pt/C catalysts at 0.9 V RHE . • The as developed catalyst shows excellent performance in single H 2 /air fuel cell having power density of 1.08 W/cm 2 . Hitherto, platinum (Pt) is the most active electrocatalyst for the oxygen reduction reaction (ORR) of the proton exchange membrane fuel cells (PEMFCs). Enhancing the performance and reducing the use of costly Pt is of great significance for the wider adoption of PEMFCs. The present research demonstrates in situ synthesized Pt nanocrystal immobilized with sub nanometer sized cobalt (Co) particles (≤ 0.3 nm) loaded on ketjenblack carbon (KB) support via a simple polyol method as a highly active ORR electrocatalyst. The as synthesized Pt 4.1 Co/KB catalyst featured a more positive halfwave potential of 0.925 V with a resultant of 1.8 times higher mass activity than Co free Pt/KB catalyst at 0.9 V in 0.1 M HClO 4 and insignificant decay in ORR performance after 30,000 potential cycles. The excellent electrocatalytic performance of Pt 4.1 Co/KB has also been proven in a practical H 2 /air fuel cell, demonstrating a maximum peak power density of 1.08 W/cm 2 , comparable to the standard Pt/C-TKK (47 %) catalyst. The improved ORR performance of Pt 4.1 Co/KB is attributed to the incorporation of sub nanometer sized Co particles, which synergistically enhance the activity and stability. Computational studies using periodic density functional theory calculations also suggest that the integration of ultrafine Co nanoparticles shifted the Pt contribution to the density of states towards higher energy levels, thereby facilitating the ORR process for the Pt 4.1 Co/KB catalyst. This work provides a distinctive development of an efficient and robust ORR catalyst for advancing 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 | 0.000 |
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".