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Updated: Jan 14, 2026

Molten-Salt Synthesis of Complex Metal Oxide Nanoparticles
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Non-Equilibrium Synthesis Methods to Create Metastable and High-Entropy Nanomaterials.

Shuo Liu1,2, Chaochao Dun1, Jeffrey J Urban1

  • 1The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 13, 2026
PubMed
Summary

Non-equilibrium synthesis enables mixing immiscible elements into advanced materials. These metastable, high-entropy nanostructures overcome thermodynamic limits for novel applications in catalysis and energy.

Keywords:
high‐entropy materialsmetastablenanomaterialsnon‐equilibrium synthesis

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Area of Science:

  • Materials Science
  • Inorganic Chemistry
  • Nanotechnology

Background:

  • Stabilizing multiple elements in a single phase creates advanced materials with unique properties.
  • Hume-Rothery rules limit solid-state miscibility under equilibrium conditions, restricting material composition.
  • This limitation narrows the scope for discovering new inorganic materials.

Purpose of the Study:

  • To review how non-equilibrium synthesis methods overcome thermodynamic barriers.
  • To highlight the integration of immiscible elements into metastable and high-entropy nanostructures.
  • To discuss the potential of these materials in catalysis, energy storage, thermoelectrics, and sensing.

Main Methods:

  • Ultrafast heating and quenching techniques are employed.
  • Non-equilibrium synthesis enables kinetic trapping and stabilization via high configurational entropy.
  • These methods generate unconventional compositions and nanostructures.

Main Results:

  • Metastable and high-entropy nanostructures are formed from immiscible elements.
  • Enhanced phase stability is achieved through kinetic trapping and entropy stabilization.
  • Unique structural and functional properties arise from these complex compositions.

Conclusions:

  • Non-equilibrium synthesis dramatically expands the accessible material design space.
  • These methods offer rich datasets for AI-guided materials discovery.
  • The high-throughput and scalable nature of these approaches accelerate materials development and industrial production.