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Atomic Nuclei: Nuclear Spin State Population Distribution

Atomic Nuclei: Nuclear Spin State Population Distribution

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Related Experiment Video

Updated: Feb 1, 2026

Synthesis of Bimetallic Pt/Sn-based Nanoparticles in Ionic Liquids

Synthesis of Bimetallic Pt/Sn-based Nanoparticles in Ionic Liquids

Published on: August 23, 2018

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning.

Janis Timoshenko¹, Cody J Wrasman², Mathilde Luneau

¹Department of Materials Science and Chemical Engineering , Stony Brook University , Stony Brook , New York 11794 , United States.

|December 4, 2018

Summary

This study combines X-ray spectroscopy, simulations, and machine learning to determine atomic arrangements in metal nanoparticles (NPs). The method reveals how particle size affects structure and composition in platinum and palladium-gold NPs.

Keywords:

EXAFS Nanocatalysts bond length distribution machine learning molecular dynamics neural network

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Area of Science:

Materials Science
Nanotechnology
Computational Chemistry

Background:

Nanoparticle properties depend on composition, structure, and dynamics.
Finite size effects and unique alloying in bimetallic nanoparticles present challenges.
Partial radial distribution functions (PRDFs) describe these attributes but are hard to determine.

Purpose of the Study:

To develop a method for directly extracting PRDFs from experimental data.
To investigate finite size effects on mono- and bimetallic nanoparticles.
To analyze nearest neighbor distributions, bond dynamics, and alloying in Pt and PdAu NPs.

Main Methods:

Extended X-ray absorption fine structure (EXAFS) spectroscopy.
Molecular dynamics (MD) simulations.
Supervised machine learning (artificial neural-network) approach.

Main Results:

Successfully extracted PRDFs from experimental data for Pt and PdAu NPs.
Demonstrated the influence of finite size effects on interatomic distances and crystalline order.
Revealed distinct alloying motifs and bond dynamics in bimetallic nanoparticles.

Conclusions:

The combined EXAFS, MD, and machine learning approach is effective for determining NP structure.
Finite size effects significantly impact nanoparticle properties.
This method provides general insights into mono- and bimetallic nanoparticle systems.