Surface Nanostructures of Pt-Compositionally Complex Alloy Single-Crystal Model Catalyst Surfaces for Improved Oxygen...

Area of Science:

• Materials Science
• Catalysis
• Surface Science

Background:

• Developing efficient catalysts for the oxygen reduction reaction (ORR) is crucial for clean energy technologies.
• Platinum-based catalysts are effective but expensive, driving research into alternative compositions.
• Compositionally complex alloys (CCAs) offer tunable properties for catalytic applications.

Purpose of the Study:

• To optimize the synthesis conditions for platinum-containing compositionally complex alloy (Pt-CCA) single-crystal model catalyst surfaces.
• To investigate the relationship between alloy composition, synthesis temperature, and ORR performance.
• To leverage machine learning for efficient navigation of synthesis parameter space.

Main Methods:

• Utilized vacuum deposition to create Pt/CCA/Pt(111) model catalyst surfaces on a Pt(111) substrate.
• Synthesized CCAs with varying compositions of less-noble elements (Cr-Mn-Fe-Co-Ni or Mn-Fe-Co-Ni).
• Employed a machine-learning approach to predict optimal synthesis (annealing) temperatures and alloy compositions.

Main Results:

• Pt-CCA model catalysts synthesized under optimized conditions exhibited superior ORR durability compared to benchmark surfaces.
• Performance was dependent on specific alloy compositions and annealing temperatures.
• Machine learning successfully identified synthesis conditions linked to favorable atomic-level surface microstructures.

Conclusions:

• Optimized Pt-CCA synthesis, guided by machine learning, leads to enhanced ORR activity and durability.
• The formation of a "pseudo-core-shell-like structure" with specific elemental distribution is key to improved performance.
• Precise control over CCA composition and synthesis temperature is critical for advancing Pt-CCA catalyst systems.