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In nature, snowflake ice crystals arrange themselves into diverse symmetrical six-sided structures. We show an analogy of this when zinc (Zn) dissolves and crystallizes in liquid gallium (Ga). The lowmelting-temperature Ga is used as a “metallic solvent” to synthesize a range of flake-like Zn crystals. We extract these metallic crystals from the liquid metal solvent by reducing its surface tension using a combination of electrocapillary modulation and vacuum filtration. The liquid metal–grown crystals feature high morphological diversity and persistent symmetry. The concept is expanded to other single and binary metal solutes and Ga-based solvents, with the growth mechanisms elucidated through ab initio simulation of interfacial stability. This strategy offers general routes for creating highly crystalline, shape-controlled metallic or multimetallic fine structures from liquid metal solvents. [ABSTRACT FROM AUTHOR]

Multi-stimulus responsive soft materials with integrated functionalities are elementary blocks for building soft intelligent systems, but their rational design remains challenging. Here, we demonstrate an intelligent soft architecture sensitized by magnetized liquid metal droplets that are dispersed in a highly stretchable elastomer network. The supercooled liquid metal droplets serve as microscopic latent heat reservoirs, and their controllable solidification releases localized thermal energy/information flows for enabling programmable visualization and display. This allows the perception of a variety of information-encoded contact (mechanical pressing, stretching, and torsion) and noncontact (magnetic field) stimuli as well as the visualization of dynamic phase transition and stress evolution processes, via thermal and/or thermochromic imaging. The liquid metal-elastomer architecture offers a generic platform for designing soft intelligent sensing, display, and information encryption systems.

We introduce dynamic light scattering imaging (DLSI) to enable the wide-field measurement of the speckle temporal intensity autocorrelation function. DLSI uses the full temporal sampling of speckle fluctuations and a comprehensive model to identify the dynamic scattering regime and obtain a quantitative image of the scatterer dynamics. It reveals errors in the traditional theory of laser Doppler flowmetry and laser speckle contrast imaging and provides guidance on the best model to use in cerebral blood flow imaging., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)