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• W4378953755 abstract "Stimuli-responsive surfaces with reversible surface topography and controllable physical and mechanical properties are highly desirable for various engineering applications: e.g., information encoding, anticounterfeiting, micromanipulations, displays, etc. Here we present a digital type of stimuli-responsive surface composed of discrete silicon scales supported on individual core-shell magnetic micropillars (MMP) and realize precise control over the local topography of the surfaces. The individual MMP can be reversibly modulated between two contrasting bending states (state 0, hard to bend; state 1, easy to bend) by controlling the spatial distribution of the magnetic nanoparticles inside the pillar shells. These two different states of the micropillars induce a completely different topography of the supporting scales that can be utilized as mechanical pixels upon applying actuation magnetic fields. We further build a three-dimensional (3D) microcontrolling platform for digital modulation of the micropillar states. With this platform, a large array of 50 × 50 MMP surfaces can be programmed and reprogrammed into any combination of the local states by simply reading a matrix of binary digits. Such a digital modulation process facilitates the practical application of the MMP surfaces for fast and reprogrammable display of various structural patterns, as demonstrated for microscale letters, millimeter-scale QR code, and complex Chinese characters. The digital modulation and on-demand reprogrammability of the MMP surfaces reported here are expected to advance the development of various other forms of digital mechanical metasurfaces that can easily transform digital information into encoded mechanical responses." @default.
• W4378953755 created "2023-06-02" @default.
• W4378953755 creator A5032028053 @default.
• W4378953755 date "2023-06-01" @default.
• W4378953755 modified "2023-10-15" @default.
• W4378953755 title "Digital Mechanical Metasurfaces for Reprogrammable Structural Display" @default.
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• W4378953755 doi "https://doi.org/10.1021/acsnano.2c12679" @default.
• W4378953755 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37260374" @default.
• W4378953755 hasPublicationYear "2023" @default.
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