Physics-aware differentiable design of magnetically actuated kirigami for shape morphing.

Shape morphing that transforms morphologies in response to stimuli is crucial for future multifunctional systems. While kirigami holds great promise in enhancing shape-morphing, existing designs primarily focus on kinematics and overlook the underlying physics. This study introduces a differentiable inverse design framework that considers the physical interplay between geometry, materials, and stimuli of active kirigami, made by soft material embedded with magnetic particles, to realize target shape-morphing upon magnetic excitation. We achieve this by combining differentiable kinematics and energy models into a constrained optimization, simultaneously designing the cuts and magnetization orientations to ensure kinematic and physical feasibility. Complex kirigami designs are obtained automatically with unparalleled efficiency, which can be remotely controlled to morph into intricate target shapes and even multiple states. The proposed framework can be extended to accommodate various active systems, bridging geometry and physics to push the frontiers in shape-morphing applications, like flexible electronics and minimally invasive surgery.

Liquid Crystal Elastomer-Liquid Metal Composite: Ultrafast, Untethered, and Programmable Actuation by Induction Heating.

Liquid crystal elastomers (LCEs) are a class of stimuli-responsive materials that have been intensively studied for applications including artificial muscles, shape morphing structures, and soft robotics due to their capability of large, programmable, and fully reversible actuation strains. To fully take advantage of LCEs, rapid, untethered, and programmable actuation methods are highly desirable. Here, a liquid crystal elastomer-liquid metal (LCE-LM) composite is reported, which enables ultrafast and programmable actuations by eddy current induction heating. The composite consists of LM sandwiched between two LCE layers printed via direct ink writing (DIW). When subjected to a high-frequency alternating magnetic field, the composite is actuated in milliseconds. By moving the magnetic field, the eddy current is spatially controlled for selective actuation. Additionally, sequential actuation is achievable by programming the LM thickness distribution in a sample. With these capabilities, the LCE-LM composite is further exploited for multimodal deformation of a pop-up structure, on-ground omnidirectional robotic motion, and in-water targeted object manipulation and crawling.

Concentration-Driven Self-Assembly of PS-b-PLA Bottlebrush Diblock Copolymers in Solution.

Bottlebrush polymers are a class of semiflexible, hierarchical macromolecules with unique potential for shape-, architecture-, and composition-based structure-property design. It is now well-established that in dilute to semidilute solution, bottlebrush homopolymers adopt a wormlike conformation, which decreases in extension (persistence length) as the concentration and molecular overlap increase. By comparison, the solution phase self-assembly of bottlebrush diblock copolymers (BBCP) in a good solvent remains poorly understood, despite critical relevance for solution processing of ordered phases and photonic crystals. In this work, we combine small-angle X-ray scattering, coarse-grained simulation, and polymer synthesis to map the equilibrium phase behavior and conformation of a set of large, nearly symmetric PS-b-PLA bottlebrush diblock copolymers in toluene. Three BBCP are synthesized, with side chains of number-averaged molecular weights of 4500 (PS) and 4200 g/mol (PLA) and total backbone degrees of polymerization of 100, 255, and 400 repeat units. The grafting density is one side chain per backbone repeat unit. With increasing concentration in solution, all three polymers progress through a similar structural transition: from dispersed, wormlike chains with concentration-dependent (decreasing) extension, through the onset of disordered PS/PLA compositional fluctuations, to the formation of a long-range ordered lamellar phase. With increasing concentration in the microphase-separated regimes, the domain spacing increases as individual chains partially re-extend due to block immiscibility. Increases in the backbone degree of polymerization lead to changes in the scattering profiles which are consistent with the increased segregation strength. Coarse-grained simulations using an implicit side-chain model are performed, and concentration-dependent self-assembly behavior is qualitatively matched to experiments. Finally, using the polymer with the largest backbone length, we demonstrate that lamellar phases develop a well-defined photonic band gap in solution, which can be tuned across the visible spectrum by varying polymer concentration.

Deep Learning-Accelerated Designs of Tunable Magneto-Mechanical Metamaterials.

Metamaterials are artificially structured materials with unusual properties, such as negative Poisson's ratio, acoustic band gap, and energy absorption. However, metamaterials made of conventional materials lack tunability after fabrication. Thus, active metamaterials using magneto-mechanical actuation for untethered, fast, and reversible shape configurations are developed to tune the mechanical response and property of metamaterials. Although the magneto-mechanical metamaterials have shown promising capabilities in tunable mechanical stiffness, acoustic band gaps, and electromagnetic behaviors, the existing demonstrations rely on the forward design methods based on experience or simulations, by which the metamaterial properties are revealed only after the design. Considering the massive design space due to the material and structural programmability, a robust inverse design strategy is desired to create the magneto-mechanical metamaterials with preferred tunable properties. In this work, we develop an inverse design framework where a deep residual network replaces the conventional finite-element analysis for acceleration, realizing metamaterials with predetermined global strains under magnetic actuations. For validation, a direct-ink-writing printing method of the magnetic soft materials is adopted to fabricate the designed complex metamaterials. The deep learning-accelerated design framework opens avenues for the designs of magneto-mechanical metamaterials and other active metamaterials with target mechanical, acoustic, thermal, and electromagnetic properties.

Three-Dimensional Printable Nanoporous Polymer Matrix Composites for Daytime Radiative Cooling.

Daytime radiative cooling presents an exciting new strategy for combating global warming, because it can passively cool buildings by reflecting sunlight and utilizing the infrared atmospheric window to eject heat into outer space. Recent progress with novel material designs showed promising subambient cooling performance under direct sunlight. However, large-scale implementation of radiative cooling technologies is still limited by the high-cost and complex fabrication. Here, we develop a nanoporous polymer matrix composite (PMC) to enable rapid production and cost reduction using commercially available polymer processing techniques, such as molding, extrusion, and 3D printing. With a high solar reflectance of 96.2% and infrared emissivity > 90%, the nanoporous PMC achieved a subambient temperature drop of 6.1 °C and cooling power of 85 W/m2 under direct sunlight, which are comparable to the state-of-the-art. This work offers great promise to make radiative cooling technologies more viable for saving energy and reducing emissions in building cooling applications.