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Updated: Apr 2, 2026

$Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene$

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3D Atomic Arrangement at Functional Interfaces Inside Nanoparticles by Resonant High-Energy X-ray Diffraction.

Valeri Petkov¹, Binay Prasai¹, Sarvjit Shastri²

¹Department of Physics, Central Michigan University , Mt. Pleasant, Michigan 48859, United States.

ACS Applied Materials & Interfaces

|September 29, 2015

Summary

Understanding nanoparticle interfaces is key to new functionalities. This study uses advanced X-ray diffraction to precisely map atomic structures at core-shell nanoparticle interfaces, revealing their impact on catalytic properties.

Keywords:

3D atomic structure composite metallic nanoparticles for catalytic applications element specific atomic pair distribution functions resonant high-energy X-ray diffraction reverse Monte Carlo simulations

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Area of Science:

Materials Science
Nanotechnology
Surface Chemistry

Background:

Nanoparticles (NPs) are increasingly important in science and technology.
Composite NPs offer enhanced properties through synergistic interfaces.
Current methods struggle to precisely characterize NP interfaces at the atomic level.

Purpose of the Study:

To demonstrate a method for precise atomic-level characterization of NP interfaces.
To investigate the influence of core-shell interfaces on NP catalytic functionality.
To validate a novel technique for NP interface analysis.

Main Methods:

Utilized resonant high-energy X-ray diffraction (XRD).
Applied element-specific atomic pair distribution function (PDF) analysis.
Studied 10 nm Ruthenium (Ru) core-Platinum (Pt) shell NPs.

Main Results:

Achieved precise 3D atomic arrangement knowledge at the Ru-Pt core-shell interface.
Quantified the influence of the interface on catalytic activity.
Demonstrated the effectiveness of the combined XRD-PDF technique.

Conclusions:

Precise atomic-level interface knowledge is attainable using resonant XRD and PDF analysis.
This knowledge is crucial for understanding and optimizing NP functionality.
The demonstrated technique offers a powerful tool for NP design and application.