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Discovery of two-dimensional binary nanoparticle superlattices using global Monte Carlo optimization

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  • Yilong Zhou

    (Duke University
    Lawrence Livermore National Laboratory)

  • Gaurav Arya

    (Duke University)

Abstract

Binary nanoparticle (NP) superlattices exhibit distinct collective plasmonic, magnetic, optical, and electronic properties. Here, we computationally demonstrate how fluid-fluid interfaces could be used to self-assemble binary systems of NPs into 2D superlattices when the NP species exhibit different miscibility with the fluids forming the interface. We develop a basin-hopping Monte Carlo (BHMC) algorithm tailored for interface-trapped structures to rapidly determine the ground-state configuration of NPs, allowing us to explore the repertoire of binary NP architectures formed at the interface. By varying the NP size ratio, interparticle interaction strength, and difference in NP miscibility with the two fluids, we demonstrate the assembly of an array of exquisite 2D periodic architectures, including AB-, AB2-, and AB3-type monolayer superlattices as well as AB-, AB2-, A3B5-, and A4B6-type bilayer superlattices. Our results suggest that the interfacial assembly approach could be a versatile platform for fabricating 2D colloidal superlattices with tunable structure and properties.

Suggested Citation

  • Yilong Zhou & Gaurav Arya, 2022. "Discovery of two-dimensional binary nanoparticle superlattices using global Monte Carlo optimization," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35690-8
    DOI: 10.1038/s41467-022-35690-8
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