Dissecting complex nanoparticle heterostructures via multimodal data fusion with aberration-corrected STEM...

Area of Science:

• Materials Science
• Nanotechnology
• Catalysis

Background:

• Nanostructured materials are crucial for energy applications like water splitting and energy harvesting.
• Characterizing the local chemical and electronic properties of buried interfaces in these materials is challenging.
• Existing methods lack the ability to simultaneously analyze electronic structure and full composition at buried interfaces.

Purpose of the Study:

• To develop a statistical methodology for joint analysis of STEM EELS and EDX data.
• To enable comprehensive characterization of buried metal/oxide interfaces in nanostructured materials.
• To estimate transition metal valency and full chemical composition in encapsulated morphologies.

Main Methods:

• Jointly analyzing simultaneously-acquired STEM EELS (Electron Energy Loss Spectroscopy) and EDX (Energy-Dispersive X-ray Spectroscopy) datasets.
• Fusing datasets along shared spatial factors using a statistical approach.
• Applying the methodology to a heterogeneous Co-P thin film catalyst.

Main Results:

• A statistical methodology was successfully developed for joint analysis of STEM EELS and EDX data.
• The method allows for estimation of transition metal valency and full chemical composition.
• Demonstrated on a Co-P catalyst, revealing a multi-shell phosphide structure with a P-doped metallic Co core.

Conclusions:

• The developed statistical methodology provides a powerful tool for characterizing complex nanostructured materials.
• Accurate analysis of buried interfaces is achievable, advancing the design of catalytic heterostructures.
• The Co-P catalyst exhibits a unique multi-shell phosphide structure, offering insights for catalyst design.