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Conforming nanoparticle sheets to surfaces with Gaussian curvature


Nanoparticle monolayer sheets are ultrathin inorganic-organic hybrid materials that combine highly controllable optical and electrical properties with mechanical flexibility and remarkable strength. Like other thin sheets, their low bending rigidity allows them to easily roll into or conform to cylindrical geometries. Nanoparticle monolayers not only can bend, but also stretch and compress. This means that, unlike nearly inextensible sheets such as paper or graphene, nanoparticle sheets can conform to surfaces with more complex topography that are characterized by non-zero Gaussian curvature, like spherical caps or saddles. Here, we investigate the limits of nanoparticle monolayers' ability to conform to substrates with Gaussian curvature by stamping nanoparticle sheets onto lattices of larger polystyrene spheres. Tuning the local Gaussian curvature by increasing the size of the substrate spheres, we find that the stamped sheet morphology evolves through three characteristic stages: from full substrate coverage, where the sheet extends over the interstices in the lattice, to coverage in the form of caps that conform tightly to the top portion of each sphere and fracture at larger polar angles, to caps that exhibit radial folds. Through analysis of the nanoparticle positions, obtained from scanning electron micrographs, we extract the local strain tensor and track the onset of strain-induced dislocations in the particle arrangement. By considering the interplay of energies for elastic and plastic deformations and adhesion, we construct arguments that capture the observed changes in sheet morphology as Gaussian curvature is tuned over two orders of magnitude.

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Publication details

The article was received on 10 Aug 2018, accepted on 10 Oct 2018 and first published on 11 Oct 2018

Article type: Paper
DOI: 10.1039/C8SM01640B
Citation: Soft Matter, 2018, Accepted Manuscript
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    Conforming nanoparticle sheets to surfaces with Gaussian curvature

    N. P. Mitchell, R. Carey, J. Hannah, Y. Wang, M. C. Ruiz, S. McBride, X. Lin and H. Jaeger, Soft Matter, 2018, Accepted Manuscript , DOI: 10.1039/C8SM01640B

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