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Functional composites by programming entropy-driven nanosheet growth

Author

Listed:
  • Emma Vargo

    (University of California, Berkeley
    Lawrence Berkeley National Laboratory)

  • Le Ma

    (University of California, Berkeley
    Lawrence Berkeley National Laboratory)

  • He Li

    (Lawrence Berkeley National Laboratory
    Lawrence Berkeley National Laboratory)

  • Qingteng Zhang

    (Argonne National Laboratory)

  • Junpyo Kwon

    (Lawrence Berkeley National Laboratory
    University of California, Berkeley)

  • Katherine M. Evans

    (University of California, Berkeley)

  • Xiaochen Tang

    (Lawrence Berkeley National Laboratory)

  • Victoria L. Tovmasyan

    (University of California, Berkeley)

  • Jasmine Jan

    (University of California, Berkeley)

  • Ana C. Arias

    (University of California, Berkeley)

  • Hugo Destaillats

    (Lawrence Berkeley National Laboratory)

  • Ivan Kuzmenko

    (Argonne National Laboratory)

  • Jan Ilavsky

    (Argonne National Laboratory)

  • Wei-Ren Chen

    (Oak Ridge National Laboratory)

  • William Heller

    (Oak Ridge National Laboratory)

  • Robert O. Ritchie

    (University of California, Berkeley
    Lawrence Berkeley National Laboratory
    University of California, Berkeley)

  • Yi Liu

    (Lawrence Berkeley National Laboratory
    Lawrence Berkeley National Laboratory)

  • Ting Xu

    (University of California, Berkeley
    Lawrence Berkeley National Laboratory
    University of California, Berkeley
    Kavli Energy NanoScience Institute)

Abstract

Nanomaterials must be systematically designed to be technologically viable1–5. Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration6,7. Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible8–13. To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing14–17. Here, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm−2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (

Suggested Citation

  • Emma Vargo & Le Ma & He Li & Qingteng Zhang & Junpyo Kwon & Katherine M. Evans & Xiaochen Tang & Victoria L. Tovmasyan & Jasmine Jan & Ana C. Arias & Hugo Destaillats & Ivan Kuzmenko & Jan Ilavsky & W, 2023. "Functional composites by programming entropy-driven nanosheet growth," Nature, Nature, vol. 623(7988), pages 724-731, November.
  • Handle: RePEc:nat:nature:v:623:y:2023:i:7988:d:10.1038_s41586-023-06660-x
    DOI: 10.1038/s41586-023-06660-x
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