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We employ photothermally driven self-assembly of colloidal particles to design microscopic structures with programmable size and tunable order. The experimental system is based on a binary mixture of “plasmonic heater” gold nanoparticles and “assembly building block” microparticles. Photothermal heating of the gold nanoparticles under visible light causes a natural convection flow that efficiently assembles the microscale building block particles (diameter 1–10 μm) into a monolayer. We identify the onset of active Brownian motion of colloidal particles under this convective flow by varying the conditions of light intensity, gold nanoparticle concentration, and sample height. We realize a crowded assembly of microparticles around the center of illumination and show that the size of the particle crowd can be programmed using patterned light illumination. In a binary mixture of gold nanoparticles and polystyrene microparticles, we demonstrate the formation of rapid and large-scale crystalline monolayers, covering an area of 0.88 mm2 within 10 min. We find that the structural order of the assembly can be tuned by varying the surface charge of the nanoparticles and the size of the microparticles, giving rise to the formation of different phases–colloidal crystals, crowds, and gels. Using Monte Carlo simulations, we explain how the phases emerge from the interplay between hydrodynamic and electrostatic interactions, as well as the assembly kinetics. Our study demonstrates the promise of self-assembly with programmable shapes and structural order under nonequilibrium conditions using an accessible setup comprising only binary mixtures and LED light. 

Abstract Auxetic materials have a negative Poisson’s ratio and are of significant interest in applications that include impact mitigation, membrane separations and biomedical engineering. While there are numerous examples of structured materials that exhibit auxetic behavior, the examples of engineered auxetic structures is largely limited to periodic lattice structures that are limited to directional or anisotropic auxetic response. Structures that exhibit a three-dimensionally isotropic auxetic response have been, unfortunately, slow to evolve. Here we introduce an inverse design algorithm based on global node optimization to design three-dimensional auxetic metamaterial structures from disordered networks. After specifying the target Poisson’s ratio for a structure, an inverse design algorithm is used to adjust the positions of all nodes in a disordered network structure until the desired mechanical response is achieved. The proposed algorithm allows independent control of shear and bulk moduli, while preserving the density and connectivity of the networks. When the angle bending stiffness in the network is kept low, it is possible to realize optimized structures with a Poisson’s ratios as low as −0.6. During the optimization, the bulk modulus of these networks decreases by almost two orders of magnitude, but the shear modulus remains largely unaltered. The materials designed in this manner are fabricated by dual-material 3D-printing, and are found to exhibit the mechanical responses that were originally encoded in the computational design engine. The approach proposed here provides a materials-by-design platform that could be extended for engineering of optical, acoustic, and electrical properties, beyond the design of auxetic metamaterials.