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Related Concept Videos

Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

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Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
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Metallic Solids02:37

Metallic Solids

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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
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Updated: Dec 31, 2025

High Resolution Physical Characterization of Single Metallic Nanoparticles
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Cluster-mining: an approach for determining core structures of metallic nanoparticles from atomic pair distribution

Soham Banerjee1, Chia Hao Liu1, Kirsten M Ø Jensen2

  • 1Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, USA.

Acta Crystallographica. Section A, Foundations and Advances
|January 8, 2020
PubMed
Summary

This study introduces cluster-mining, a new automated method for determining metallic nanoparticle structures. It generates more realistic models that better match experimental data compared to traditional methods.

Keywords:
clustersdata miningnanoparticlespair distribution functionsscreeningstructural models

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Chemistry

Background:

  • Determining the atomic structure of small metallic nanoparticles is crucial for understanding their properties.
  • Existing methods often rely on simplified models that may not accurately represent complex nanoparticle structures.

Purpose of the Study:

  • To present a novel, automated computational approach for identifying and evaluating structural models of small metallic nanoparticles.
  • To improve the physical realism and data agreement of nanoparticle structural models.

Main Methods:

  • Development of the 'cluster-mining' approach, which algorithmically builds libraries of clusters from diverse structural motifs.
  • Individual refinement of each cluster model against experimental pair distribution functions using highly constrained fits.
  • Evaluation of candidate models based on goodness-of-fit criteria.

Main Results:

  • The cluster-mining approach successfully identifies multiple candidate structure models consistent with experimental data.
  • Models generated by cluster-mining demonstrate improved physical realism and better agreement with pair distribution functions.
  • Comparison shows superior performance over traditional methods using cubic close-packed cores.

Conclusions:

  • Cluster-mining offers a robust, automated, and user-friendly alternative for nanoparticle structure determination.
  • This method enhances the accuracy and reliability of structural models for small metallic nanoparticles.
  • The findings challenge the prevalent use of simplified crystallographic cores in nanoparticle modeling.