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Abstract Flow cell electrodes are typically composed of porous carbon materials, such as papers, felts, and cloths. However, their random architecture hinders the fundamental characterization of electrode structure‐performance relationships during in situ operation of porous electrochemical flow systems. This work describes a “print‐and‐plate” method that combines direct ink writing of micro‐periodic lattices with a two‐step metal plating process that converts them into highly conductive (sheet resistance 40 mΩ sq⁻¹) electrodes. Theiroperandoperformance is assessed in an anthraquinone disulfonic acid half‐cell using widefield electrochemical fluorescence microscopy, where output current and fluorescence intensity are in excellent agreement. The pressure drop associated with flow through three electrode designs is determined via simulations from which the most efficient design is identified and manufactured via print‐and‐plate. Confocal fluorescence microscopy is then used to create a 3D map of the state of charge (SOC) inside this print‐and‐plate electrode. The experimental state of the charge map is in good agreement with computational predictions. The rapid design, simulation, and fabrication of print‐and‐plate electrodes enable fundamental investigations of how architected porosity affects electrochemical performance under flow. 

Light- and ink-based 3D printing methods have vastly expanded the design space and geometric complexity of architected ceramics. However, light-based methods are typically confined to a relatively narrow range of preceramic and particle-laden resins, while ink-based methods are limited in geometric complexity due to layerwise assembly. Here, embedded 3D printing is combined with microwave-activated curing to generate architected ceramics with spatially controlled composition in freeform shapes. Aqueous colloidal inks are printed within a support matrix, rapidly cured via microwave-activated polymerization, and subsequently dried and sintered into dense architectures composed of one or more oxide materials. This integrated manufacturing method opens new avenues for the design and fabrication of complex ceramic architectures with programmed composition, density, and form for myriad applications. 

Abstract Recent advances in computational design and 3D printing enable the fabrication of polymer lattices with high strength‐to‐weight ratio and tailored mechanics. To date, 3D lattices composed of monolithic materials have primarily been constructed due to limitations associated with most commercial 3D printing platforms. Here, freeform fabrication of multi‐material polymer lattices via embedded three‐dimensional (EMB3D) printing is demonstrated. An algorithm is developed first that generates print paths for each target lattice based on graph theory. The effects of ink rheology on filamentary printing and the effects of the print path on resultant mechanical properties are then investigated. By co‐printing multiple materials with different mechanical properties, a broad range of periodic and stochastic lattices with tailored mechanical responses can be realized opening new avenues for constructing architected matter. 

Soft machines will require soft materials that exhibit a rich diversity of functionality, including shape morphing and photoresponsivity. The combination of these functionalities enables useful behaviors in soft machines that can be further developed by synthesizing materials that exhibit localized responsivity. Localized responsivity of liquid crystal elastomers (LCEs), which are soft materials that exhibit shape morphing, can be enabled by formulating composite inks for direct ink writing (DIW). Gold nanorods (AuNRs) can be added to LCEs to enable photothermal shape change upon absorption of light through a localized surface plasmon resonance. We compared LCE formulations, focusing on their amenability for printing by DIW and the photoresponsivity of AuNRs. The local responsivity of different three-dimensional architectures enabled soft machines that could oscillate, crawl, roll, transport mass, and display other unique modes of actuation and motion in response to light, making these promising functional materials for advanced applications. 

Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. How- ever, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability pro- files. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials. 

Dielectric elastomer actuators (DEAs) are among the fastest and most energy-efficient, shape-morphing materials. To date, their shapes have been controlled using patterned electrodes or stiffening elements. While their actuated shapes can be analyzed for prescribed configurations of electrodes or stiffening elements (the forward problem), the design of DEAs that morph into target shapes (the inverse problem) has not been fully addressed. Here, we report a simple analytical solution for the inverse design and fabrication of programmable shape-morphing DEAs. To realize the target shape, two mechanisms are combined to locally control the actuation magnitude and direction by patterning the number of local active layers and stiff rings of varying shapes, respectively. Our combined design and fabrication strategy enables the creation of complex DEA architectures that shape-morph into simple target shapes, for instance, those with zero, positive, and negative Gaussian curvatures as well as complex shapes, such as a face.