Responsive materials architected in space and time

Self-deployable contracting-cord metamaterials with tunable mechanical properties

Recent advances in active materials and fabrication techniques have enabled the production of cyclically self-deployable metamaterials with an expanded functionality space. However, designing metamaterials that possess continuously tunable mechanical properties after self-deployment remains a challenge, notwithstanding its importance. Inspired by push puppets, we introduce an efficient design strategy to create reversibly self-deployable metamaterials with continuously tunable post-deployment stiffness and damping. Our metamaterial comprises contracting actuators threaded through beads with matching conical concavo-convex interfaces in networked chains. The slack network conforms to arbitrary shapes, but when actuated, it self-assembles into a preprogrammed configuration with beads gathered together. Further contraction of the actuators can dynamically tune the assembly's mechanical properties through the beads' particle jamming, while maintaining the overall structure with minimal change. We show that, after deployment, such metamaterials exhibit pronounced tunability in bending-dominated configurations: they can become more than 35 times stiffer and change their damping capability by over 50%. Through systematic analysis, we find that the beads' conical angle can introduce geometric nonlinearity, which has a major effect on the self-deployability and tunability of the metamaterial. Our work provides routes towards reversibly self-deployable, lightweight, and tunable metamaterials, with potential applications in soft robotics, reconfigurable architectures, and space engineering.

Adaptive hierarchical origami-based metastructures

Shape-morphing capabilities are crucial for enabling multifunctionality in both biological and artificial systems. Various strategies for shape morphing have been proposed for applications in metamaterials and robotics. However, few of these approaches have achieved the ability to seamlessly transform into a multitude of volumetric shapes post-fabrication using a relatively simple actuation and control mechanism. Taking inspiration from thick origami and hierarchies in nature, we present a hierarchical construction method based on polyhedrons to create an extensive library of compact origami metastructures. We show that a single hierarchical origami structure can autonomously adapt to over 103 versatile architectural configurations, achieved with the utilization of fewer than 3 actuation degrees of freedom and employing simple transition kinematics. We uncover the fundamental principles governing theses shape transformation through theoretical models. Furthermore, we also demonstrate the wide-ranging potential applications of these transformable hierarchical structures. These include their uses as untethered and autonomous robotic transformers capable of various gait-shifting and multidirectional locomotion, as well as rapidly self-deployable and self-reconfigurable architecture, exemplifying its scalability up to the meter scale. Lastly, we introduce the concept of multitask reconfigurable and deployable space robots and habitats, showcasing the adaptability and versatility of these metastructures.

Design rules for controlling active topological defects

Topological defects play a central role in the physics of many materials, including magnets, superconductors, and liquid crystals. In active fluids, defects become autonomous particles that spontaneously propel from internal active stresses and drive chaotic flows stirring the fluid. The intimate connection between defect textures and active flow suggests that properties of active materials can be engineered by controlling defects, but design principles for their spatiotemporal control remain elusive. Here, we propose a symmetry-based additive strategy for using elementary activity patterns, as active topological tweezers, to create, move, and braid such defects. By combining theory and simulations, we demonstrate how, at the collective level, spatial activity gradients act like electric fields which, when strong enough, induce an inverted topological polarization of defects, akin to a negative susceptibility dielectric. We harness this feature in a dynamic setting to collectively pattern and transport interacting active defects. Our work establishes an additive framework to sculpt flows and manipulate active defects in both space and time, paving the way to design programmable active and living materials for transport, memory, and logic.

Crystal-Inspired Cellular Metamaterials and Triply Periodic Minimal Surfaces

Triply periodic minimal surfaces (TPMSs) are found in many natural objects including butterfly wings, sea urchins, and biological membranes. They simultaneously have zero mean curvature at every point and a crystallographic group symmetry. A metamaterial can be created from such periodic surfaces or used as a reinforcement of a composite material. While a TPMS as a mathematical object has been known since 1865, only novel additive manufacturing (AM) technology made it possible to fabricate cellular materials with complex TPMS shapes. Cellular TPMS-based metamaterials have remarkable properties related to wetting/liquid penetration, shock absorption, and the absence of stress concentrators. Recent studies showed that TPMSs are also found in natural crystals when electron surfaces are considered. Artificial crystal-inspired metamaterials mimic such crystals including zeolites and schwarzites. These metamaterials are used for shock, acoustic waves, and vibration absorption, and as structural materials, heat exchangers, and for other applications. The choice of the crystalline cell of a material, as well as its microstructure, plays a decisive role in its properties. The new area of crystal-inspired materials has many common features with traditional biomimetics with models being borrowed from nature and adjusted for engineering applications.

Mechanically Adaptive Polymers Constructed from Dynamic Coordination Equilibria

Designing materials capable of adapting their mechanical properties in response to external stimuli is the key to preventing failure and extending their service life. However, existing mechanically adaptive polymers are hindered by limitations such as inadequate load-bearing capacity, difficulty in achieving reversible changes, high cost, and a lack of multiple responsiveness. Herein, we address these challenges using dynamic coordination bonds. A new type of mechanically adaptive material with both rate- and temperature-responsiveness was developed. Owing to the stimuli-responsiveness of the coordination equilibria, the prepared polymers, PBMBD-Fe and PBMBD-Co, exhibit mechanically adaptive properties, including temperature-sensitive strength modulation and rate-dependent impact hardening. Benefitting from the dynamic nature of the coordination bonds, the polymers exhibited impressive energy dissipation, damping capacity (loss factors of 1.15 and 2.09 at 1.0 Hz), self-healing, and 3D printing abilities, offering durable and customizable impact resistance and protective performance. The development of impact-resistant materials with comprehensive properties has potential applications in the sustainable and intelligent protection fields.

Trainable bioinspired magnetic sensitivity adaptation using ferromagnetic colloidal assemblies

Nature has already suggested bioinspired functions. Beyond them, adaptive and trainable functions could be the inspiration for novel responsive soft matter beyond the state-of-the-art classic static bioinspired, stimulus-responsive, and shape-memory materials. Here, we describe magnetic assembly/disassembly of electrically conducting soft ferromagnetic nickel colloidal particles into surface topographical pillars for bistable electrical trainable memories. They allow magnetic sensing with adaptable and rescalable sensitivity ranges, enabled by bistable memories and kinetic concepts inspired by biological sensory adaptations. Based on the soft ferromagnetism of the nanogranular composition and the resulting rough particle surfaces prepared via a solvothermal synthesis, triggerable structural memory is achieved by the magnetic field-driven particle assembly and disassembly, promoted by interparticle jamming. Electrical conversion from current to frequency for electrical spikes facilitates rescalable and trainable frequency-based sensitivity on magnetic fields. This work suggests an avenue for designing trainable and adaptable life-inspired materials, for example, for soft robotics and interactive autonomous devices.

Intelligent electroactive material systems with self-adaptive mechanical memory and sequential logic

By synthesizing the requisite functionalities of intelligence in an integrated material system, it may become possible to animate otherwise inanimate matter. A significant challenge in this vision is to continually sense, process, and memorize information in a decentralized way. Here, we introduce an approach that enables all such functionalities in a soft mechanical material system. By integrating nonvolatile memory with continuous processing, we develop a sequential logic-based material design framework. Soft, conductive networks interconnect with embedded electroactive actuators to enable self-adaptive behavior that facilitates autonomous toggling and counting. The design principles are scaled in processing complexity and memory capacity to develop a model 8-bit mechanical material that can solve linear algebraic equations based on analog mechanical inputs. The resulting material system operates continually to monitor the current mechanical configuration and to autonomously search for solutions within a desired error. The methods created in this work are a foundation for future synthetic general intelligence that can empower materials to autonomously react to diverse stimuli in their environment.

Dynamic Chemistry Toolbox for Advanced Sustainable Materials

Developing dynamic chemistry for polymeric materials offers chemical solutions to solve key problems associated with current plastics. Mechanical performance and dynamic function are equally important in material design because the former determines the application scope and the latter enables chemical recycling and hence sustainability. However, it is a long-term challenge to balance the subtle trade-off between mechanical robustness and dynamic properties in a single material. The rise of dynamic chemistry, including supramolecular and dynamic covalent chemistry, provides many opportunities and versatile molecular tools for designing constitutionally dynamic materials that can adapt, repair, and recycle. Facing the growing social need for developing advanced sustainable materials without compromising properties, recent progress showing how the toolbox of dynamic chemistry can be explored to enable high-performance sustainable materials by molecular engineering strategies is discussed here. The state of the art and recent milestones are summarized and discussed, followed by an outlook toward future opportunities and challenges present in this field.

Adaptive plasticity of auxetic Kirigami hydrogel fabricated from anisotropic swelling of cellulose nanofiber film

Hydrogels are flexible materials that typically accommodate elongation with positive Poisson's ratios. Auxetic property, i.e., the negative Poisson's ratio, of elastic materials can be macroscopically implemented by the structural design of the continuum. We realize it without mold for hydrogel made of cellulose nanofibers (CNFs). The complex structural design of auxetic Kirigami is first implemented on the dry CNF film, i.e., so-called nanopaper, by laser processing, and the CNF hydrogel is formed by dipping the film in liquid water. The CNF films show anisotropic swelling where drastic volumetric change mainly originates from increase in the thickness. This anisotropy makes the design and fabrication of the emergent Kirigami hydrogel straightforward. We characterize the flexibility of this mechanical metamaterial made of hydrogel by cyclic tensile loading starting from the initial end-to-end distance of dry sample. The tensile load at the maximum strain decreases with the increasing number of cycles. Furthermore, the necessary work up to the maximum strain even decreases to the negative value, while the work of restoration to the original end-to-end distance increases from the negative value to the positive. The equilibrium strain where the force changes the sign increases to reach a plateau. This plastic deformation due to the cyclic loading can be regarded as the adaptive response without fracture to the applied dynamic loading input.

Directional actuation and phase transition-like behavior in anisotropic networks of responsive microfibers

Two-dimensional shape-morphing networks are common in biological systems and have garnered attention due to their nontrivial physical properties that emanate from their cellular nature. Here, we present the fabrication and characterization of anisotropic shape-morphing networks composed of thermoresponsive polymeric microfibers. By strategically positioning fibers with varying responses, we construct networks that exhibit directional actuation. The individual segments within the network display either a linear extension or buckling upon swelling, depending on their radius and length, and the transition between these morphing behaviors resembles Landau's second-order phase transition. The microscale variations in morphing behaviors are translated into observable macroscopic effects, wherein regions undergoing linear expansion retain their shape upon swelling, whereas buckled regions demonstrate negative compressibility and shrink. Manipulating the macroscale morphing by adjusting the properties of the fibrous microsegments offers a means to modulate and program morphing with mesoscale precision and unlocks novel opportunities for developing programmable microscale soft robotics and actuators.

Multi-stimuli-responsive luminescence enabled by crown ether anchored chiral antimony halide phosphors

Stimuli-responsive optical materials have provided a powerful impetus for the development of intelligent optoelectronic devices. The family of organic-inorganic hybrid metal halides, distinguished by their structural diversity, presents a prospective platform for the advancement of stimuli-responsive optical materials. Here, we have employed a crown ether to anchor the A-site cation of a chiral antimony halide, enabling convenient control and modulation of its photophysical properties. The chirality-dependent asymmetric lattice distortion of inorganic skeletons assisted by a crown ether promotes the formation of self-trapped excitons (STEs), leading to a high photoluminescence quantum yield of over 85%, concomitant with the effective circularly polarized luminescence. The antimony halide enantiomers showcase highly sensitive stimuli-responsive luminescent behaviours towards excitation wavelength and temperature simultaneously, exhibiting a versatile reversible colour switching capability from blue to white and further to orange. In situ temperature-dependent luminescence spectra, time-resolved luminescence spectra and theoretical calculations reveal that the multi-stimuli-responsive luminescent behaviours stem from distinct STEs within zero-dimensional lattices. By virtue of the inherent flexibility and adaptability, these chiral antimony chlorides have promising prospects for future applications in cutting-edge fields such as multifunctional illumination technologies and intelligent sensing devices.

Engineering Kirigami Frameworks Toward Real-World Applications

The surge in advanced manufacturing techniques has led to a paradigm shift in the realm of material design from developing completely new chemistry to tailoring geometry within existing materials. Kirigami, evolved from a traditional cultural and artistic craft of cutting and folding, has emerged as a powerful framework that endows simple 2D sheets with unique mechanical, thermal, optical, and acoustic properties, as well as shape-shifting capabilities. Given its flexibility, versatility, and ease of fabrication, there are significant efforts in developing kirigami algorithms to create various architectured materials for a wide range of applications. This review summarizes the fundamental mechanisms that govern the transformation of kirigami structures and elucidates how these mechanisms contribute to their distinctive properties, including high stretchability and adaptability, tunable surface topography, programmable shape morphing, and characteristics of bistability and multistability. It then highlights several promising applications enabled by the unique kirigami designs and concludes with an outlook on the future challenges and perspectives of kirigami-inspired metamaterials toward real-world applications.

Electroactive Bi-Functional Liquid Crystal Elastomer Actuators

Liquid crystal elastomers (LCEs) with promising applications in the field of actuators and soft robotics are reported. However, most of them are activated by external heating or light illumination. The examples of electroactive LCEs are still limited; moreover, they are monofunctional with one type of deformation (bending or contraction). Here, the study reports on trilayer electroactive LCE (eLCE) by intimate combination of LCE and ionic electroactive polymer device (i-EAD). This eLCE is bi-functional and can perform either bending or contractile deformations by the control of the low-voltage stimulation. By applying a voltage of ±2 V at 0.1 Hz, the redox behavior and associated ionic motion provide a bending strain difference of 0.80%. Besides, by applying a voltage of ±6 V at 10 Hz, the ionic current-induced Joule heating triggers the muscle-like linear contraction with 20% strain for eLCE without load. With load, eLCE can lift a weight of 270 times of eLCE-actuator weight, while keeping 20% strain and affording 5.38 kJ·m-3 work capacity. This approach of combining two smart polymer technologies (LCE and i-EAD) in a single device is promising for the development of smart materials with multiple degrees of freedom in soft robotics, electronic devices, and sensors.

Decoupling particle-impact dissipation mechanisms in 3D architected materials

Ultralight architected materials enabled by advanced manufacturing processes have achieved density-normalized strength and stiffness properties that are inaccessible to bulk materials. However, the majority of this work has focused on static loading and elastic-wave propagation. Fundamental understanding of the mechanical behavior of architected materials under large-deformation dynamic conditions remains limited, due to the complexity of mechanical responses and shortcomings of characterization methods. Here, we present a microscale suspended-plate impact testing framework for three-dimensional micro-architected materials, where supersonic microparticles to velocities of up to 850 m/s are accelerated against a substrate-decoupled architected material to quantify its energy dissipation characteristics. Using ultra-high-speed imaging, we perform in situ quantification of the impact energetics on two types of architected materials as well as their constituent nonarchitected monolithic polymer, indicating a 47% or greater increase in mass-normalized energy dissipation under a given impact condition through use of architecture. Post-mortem characterization, supported by a series of quasi-static experiments and high-fidelity simulations, shed light on two coupled mechanisms of energy dissipation: material compaction and particle-induced fracture. Together, experiments and simulations indicate that architecture-specific resistance to compaction and fracture can explain a difference in dynamic impact response across architectures. We complement our experimental and numerical efforts with dimensional analysis which provides a predictive framework for kinetic-energy absorption as a function of material parameters and impact conditions. We envision that enhanced understanding of energy dissipation mechanisms in architected materials will serve to define design considerations toward the creation of lightweight impact-mitigating materials for protective applications.

Liquid Crystal Elastomer Artificial Tendrils with Asymmetric Core-Sheath Structure Showing Evolutionary Biomimetic Locomotion

The sophisticated and complex haptonastic movements in response to environmental-stimuli of living organisms have always fascinated scientists. However, how to fundamentally mimic the sophisticated hierarchical architectures of living organisms to provide the artificial counterparts with similar or even beyond-natural functions based on the underlying mechanism remains a major scientific challenge. Here,  liquid crystal elastomer (LCE) artificial tendrils showing evolutionary biomimetic locomotion are developed following the structure-function principle that is used in nature to grow climbing plants. These elaborately designed tendril-like LCE actuators possess an asymmetric core-sheath architecture which shows a higher-to-lower transition in the degree of LC orientation from the sheath-to-core layer across the semi-ellipse cross-section. Upon heating and cooling, the LCE artificial tendril can undergo reversible tendril-like shape-morphing behaviors, such as helical coiling/winding, and perversion. The fundamental mechanism of the helical shape-morphing of the artificial tendril is revealed by using theoretical models and finite element simulations. Besides, the incorporation of metal-ligand coordination into the LCE network provides the artificial tendril with reconfigurable shape-morphing performances such as helical transitions and rotational deformations. Finally, the abilities of helical and rotational deformations are integrated into a new reprogrammed flagellum-like architecture to perform evolutionary locomotion mimicking the haptonastic movements of the natural flagellum.

The bumpy road to friction control

The frictional properties of material interfaces can be rationally designed.

Electrochemically Responsive 3D Nanoarchitectures

Responsive nanomaterials are being developed to create new unique functionalities such as switchable colors and adhesive properties or other programmable features in response to external stimuli. While many existing examples rely on changes in temperature, humidity, or pH, this study aims to explore an alternative approach relying on simple electric input signals. More specifically, 3D electrochromic architected microstructures are developed using carbon nanotube-Tin (Sn) composites that can be reconfigured by lithiating Sn with low power electric input (≈50 nanowatts). These microstructures have a continuous, regulated, and non-volatile actuation determined by the extent of the electrochemical lithiation process. In addition, this proposed fabrication process relies only on batch lithographic techniques, enabling the parallel production of thousands of 3D microstructures. Structures with a 30-97% change in open-end area upon actuation are demonstrated and the importance of geometric factors in the response and structural integrity of 3D architected microstructures during electrochemical actuation is highlighted.

Isogonal 2-periodic polycatenanes: chain mail

For 2-periodic polycatenanes with isogonal (vertex-transitive) embeddings, the basic units linked are torus knots and links including the unknots (untangled polygons). Twenty-four infinite families have been identified, with hexagonal, tetragonal or rectangular symmetry. The simplest members of each family are described and illustrated. A method for determining the catenation number of a ring based on electromagnetic theory is described.

Programming Deformations of 3D Microstructures: Opportunities Enabled by Magnetic Alignment of Liquid Crystalline Elastomers

Synthetic structures that undergo controlled movement are crucial building blocks for developing new technologies applicable to robotics, healthcare, and sustainable self-regulated materials. Yet, programming motion is nontrivial, and particularly at the microscale it remains a fundamental challenge. At the macroscale, movement can be controlled by conventional electric, pneumatic, or combustion-based machinery. At the nanoscale, chemistry has taken strides in enabling molecularly fueled movement. Yet in between, at the microscale, top-down fabrication becomes cumbersome and expensive, while bottom-up chemical self-assembly and amplified molecular motion does not reach the necessary sophistication. Hence, new approaches that converge top-down and bottom-up methods and enable motional complexity at the microscale are urgently needed. Synthetic anisotropic materials (e.g., liquid crystalline elastomers, LCEs) with encoded molecular anisotropy that are shaped into arbitrary geometries by top-down fabrication promise new opportunities to implement controlled actuation at the microscale. In such materials, motional complexity is directly linked to the built-in molecular anisotropy that can be "activated" by external stimuli. So far, encoding the desired patterns of molecular directionality has relied mostly on either mechanical or surface alignment techniques, which do not allow the decoupling of molecular and geometric features, severely restricting achievable material shapes and thus limiting attainable actuation patterns, unless complex multimaterial constructs are fabricated. Electromagnetic fields have recently emerged as possible alternatives to provide 3D control over local anisotropy, independent of the geometry of a given 3D object. The combination of magnetic alignment and soft lithography, in particular, provides a powerful platform for the rapid, practical, and facile production of microscale soft actuators with field-defined local anisotropy. Recent work has established the feasibility of this approach with low magnetic field strengths (in the lower mT range) and comparably simple setups used for the fabrication of the microactuators, in which magnetic fields can be engineered through arrangement of permanent magnets. This workflow gives access to microstructures with unusual spatial patterning of molecular alignment and has enabled a multitude of nontrivial deformation types that would not be possible to program by any other means at the micron scale. A range of "activating" stimuli can be used to put these structures in motion, and the type of the trigger plays a key role too: directional and dynamic stimuli (such as light) make it possible to activate the patterned anisotropic material locally and transiently, which enables one to achieve and further program motional complexity and communication in microactuators. In this Account, we will discuss recent advances in magnetic alignment of molecular anisotropy and its use in soft lithography and related fabrication approaches to create LCE microactuators. We will examine how design choices-from the molecular to the fabrication and the operational levels-control and define the achievable LCE deformations. We then address the role of stimuli in realizing the motional complexity and how one can engineer feedback within and communication between microactuator arrays fabricated by soft lithography. Overall, we outline emerging strategies that make possible a completely new approach to designing for desired sets of motions of active, microscale objects.

Cascade-heterogated biphasic gel iontronics for electronic-to-multi-ionic signal transmission

Currently, electronics and iontronics in abiotic-biotic systems can only use electrons and single-species ions as unitary signal carriers. Thus, a mechanism of gating transmission for multiple biosignals in such devices is needed to match and modulate complex aqueous-phase biological systems. Here we report the use of cascade-heterogated biphasic gel iontronics to achieve diverse electronic-to-multi-ionic signal transmission. The cascade-heterogated property determined the transfer free energy barriers experienced by ions and ionic hydration-dehydration states under an electric potential field, fundamentally enhancing the distinction of cross-interface transmission between different ions by several orders of magnitude. Such heterogated or chemical-heterogated iontronics with programmable features can be coupled with multi-ion cross-interface mobilities for hierarchical and selective cross-stage signal transmission. We expect that such iontronics would be ideal candidates for a variety of biotechnology applications.

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.

Machine learning-enabled constrained multi-objective design of architected materials

Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts' prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.

Multimaterial Additively Manufactured Metamaterials Functionalized with Customizable Thermal Expansion in Multiple Directions

Metamaterials functionalized with customizable multidirectional coefficient of thermal expansion (CTE) are urgently needed for advanced shape control or dimensional stability under temperature variations. The currently reported metamaterials still lack the development of diverse base material systems and exploration of the multimaterial fabrication process. Especially, the reported range of customizable CTEs for metamaterials in multiple directions is limited within [-68.1, 56.4] ppm/°C. Here, this work explicitly proposes a strategy for closely linking base materials, additive manufacturing (AM) process, architecture, and CTE tunability, in order to provide a general guideline for the design or customization of such metamaterials. In detail, first, we systematically identify the key process parameters and related performance for additive manufacturing of polymers and propose various multimaterial systems such as polypropylene-polycarbonate (PP-PC). Then, six types of metamaterials have been fabricated with high quality by the established multimaterial additive manufacturing. By measuring the effective CTEs in multiple directions, the CTE tunability of metamaterials, including large positive values (+523.36 ppm/°C) and large negative values (-230.61 ppm/°C), far beyond the literature-reported CTE range, has been experimentally verified. Further, we have developed a bidirectional requirement-solution strategy here that acts as a bridge between design and fabrication. This work opens advanced avenues for metamaterials with multidirectionally customizable and extensive CTE tunability for a variety of engineering applications such as actuators, thermal stress relief, and improved structural stability.

Overcoming Water Diffusion Limitations in Hydrogels via Microtubular Graphene Networks for Soft Actuators

Hydrogel-based soft actuators can operate in sensitive environments, bridging the gap of rigid machines interacting with soft matter. However, while stimuli-responsive hydrogels can undergo extreme reversible volume changes of up to ≈90%, water transport in hydrogel actuators is in general limited by their poroelastic behavior. For poly(N-isopropylacrylamide) (PNIPAM) the actuation performance is even further compromised by the formation of a dense skin layer. Here it is shown, that incorporating a bioinspired microtube graphene network into a PNIPAM matrix with a total porosity of only 5.4% dramatically enhances actuation dynamics by up to ≈400% and actuation stress by ≈4000% without sacrificing the mechanical stability, overcoming the water transport limitations. The graphene network provides both untethered light-controlled and electrically powered actuation. It is anticipated that the concept provides a versatile platform for enhancing the functionality of soft matter by combining responsive and 2D materials, paving the way toward designing soft intelligent matter.

Ultrastrong colloidal crystal metamaterials engineered with DNA

Lattice-based constructs, often made by additive manufacturing, are attractive for many applications. Typically, such constructs are made from microscale or larger elements; however, smaller nanoscale components can lead to more unusual properties, including greater strength, lighter weight, and unprecedented resiliencies. Here, solid and hollow nanoparticles (nanoframes and nanocages; frame size: ~15 nanometers) were assembled into colloidal crystals using DNA, and their mechanical strengths were studied. Nanosolid, nanocage, and nanoframe lattices with identical crystal symmetries exhibit markedly different specific stiffnesses and strengths. Unexpectedly, the nanoframe lattice is approximately six times stronger than the nanosolid lattice. Nanomechanical experiments, electron microscopy, and finite element analysis show that this property results from the buckling, densification, and size-dependent strain hardening of nanoframe lattices. Last, these unusual open architectures show that lattices with structural elements as small as 15 nanometers can retain a high degree of strength, and as such, they represent target components for making and exploring a variety of miniaturized devices.

Optomechanical computing in liquid crystal elastomers

Embodied decision-making in soft, engineered matter has sparked recent interest towards the development of intelligent materials. Such decision-making capabilities can be realized in soft materials via digital information processing with combinational logic operations. Although previous research has explored soft material actuators and embedded logic in soft materials, achieving a high degree of autonomy in these material systems remains a challenge. Light is an ideal stimulus to trigger information processing in soft materials due to its low thermal effect and remote use. Thus, one approach for developing soft, autonomous materials is to integrate optomechanical computing capabilities in photoresponsive materials. Here, we establish a methodology to embed combinational logic circuitry in a photoresponsive liquid crystal elastomer (LCE) film. These LCEs are designed with embedded switches and integrated circuitry using liquid metal-based conductive traces. The resulting optomechanical computing LCEs can effectively process optical information via light, thermal, and mechanical energy conversion. The methods introduced in this work to fabricate a material capable of optical information processing can facilitate the implementation of a sense of sight in soft robotic systems and other compliant devices.

Fully (Re)configurable Interactive Material through a Switchable Photothermal Charge Transfer Complex Gated by a Supramolecular Liquid Crystal Elastomer Actuator

Charge transfer complexes (CTCs) based on self-assembled donor and acceptor molecules allow light absorption of significantly redshifted wavelengths to either the donor or acceptor. In this work, we demonstrate a CTC embedded in a hydrogen-bonded liquid crystal elastomer (LCE), which in itself is fully reformable and reprocessable. The LCE host acts as a gate, directing the self-assembly of the CTC. When hydrogen bonding is present, the CTC behaves as a near-infrared (NIR) dye allowing photothermal actuation of the LCE. The CTC can be disassembled in specific regions of the LCE film by disrupting the hydrogen bond interactions, allowing selective NIR heating and localized actuation of the films. The metastable non-CTC state may persist for weeks or can be recovered on demand by heat treatment. Besides the CTC variability, the capability of completely reforming the shape, color, and actuation mode of the LCE provides an interactive material with unprecedented application versatility.

Deconvolution volumetric additive manufacturing

Volumetric additive manufacturing techniques are a promising pathway to ultra-rapid light-based 3D fabrication. Their widespread adoption, however, demands significant improvement in print fidelity. Currently, volumetric additive manufacturing prints suffer from systematic undercuring of fine features, making it impossible to print objects containing a wide range of feature sizes, precluding effective adoption in many applications. Here, we uncover the reason for this limitation: light dose spread in the resin due to chemical diffusion and optical blurring, which becomes significant for features ⪅0.5 mm. We develop a model that quantitatively predicts the variation of print time with feature size and demonstrate a deconvolution method to correct for this error. This enables prints previously beyond the capabilities of volumetric additive manufacturing, such as a complex gyroid structure with variable thickness and a fine-toothed gear. These results position volumetric additive manufacturing as a mature 3D printing method, all but eliminating the gap to industry-standard print fidelity.

Bioinspired helical-artificial fibrous muscle structured tubular soft actuators

Biological tubular actuators show diverse deformations, which allow for sophisticated deformations with well-defined degrees of freedom (DOF). Nonetheless, synthetic active tubular soft actuators largely only exhibit few simple deformations with limited and undesignable DOF. Inspired by 3D fibrous architectures of tubular muscular hydrostats, we devised conceptually new helical-artificial fibrous muscle structured tubular soft actuators (HAFMS-TSAs) with locally tunable molecular orientations, materials, mechanics, and actuation via a modular fabrication platform using a programmable filament winding technique. Unprecedentedly, HAFMS-TSAs can be endowed with 11 different morphing modes through programmable regulation of their 3D helical fibrous architectures. We demonstrate a single "living" artificial plant rationally structured by HAFMS-TSAs exhibiting diverse photoresponsive behaviors that enable adaptive omnidirectional reorientation of its hierarchical 3D structures in the response to environmental irradiation, resembling morphing intelligence of living plants in reacting to changing environments. Our methodology would be significantly beneficial for developing sophisticated soft actuators with designable and tunable DOF.

A cold-responsive liquid crystal elastomer provides visual signals for monitoring a critical temperature decrease

Critical temperature indicators have been extensively utilized in various fields, ranging from healthcare to food safety. However, the majority of the temperature indicators are designed for upper critical temperature monitoring, indicating when the temperature rises and exceeds a predefined limit, whereas stringently demanded low critical temperature indicators are scarcely developed. Herein, we develop a new material and system that monitor temperature decrease, e.g., from ambient temperature to the freezing point, or even to an ultra-low temperature of -20 °C. For this purpose, we create a dynamic membrane which can open and close during temperature cycles from high temperature to low temperature. This membrane consists of a gold-liquid crystal elastomer (Au-LCE) bilayer structure. Unlike the commonly used thermo-responsive LCEs which actuate upon temperature rise, our LCE is cold-responsive. This means that geometric deformations occur when the environmental temperature decreases. Specifically, upon temperature decrease the LCE creates stresses at the gold interface by uniaxial deformation due to expansion along the molecular director and shrinkage perpendicular to it. At a critical stress, optimized to occur at the desired temperature, the brittle Au top layer fractures, which allows contact between the LCE and material on top of the gold layer. Material transport via cracks enables the onset of the visible signal for instance caused by a pH indicator substance. We apply the dynamic Au-LCE membrane for cold-chain applications, indicating the loss of the effectiveness of perishable goods. We anticipate that our newly developed low critical temperature/time indicator will be shortly implemented in supply chains to minimize food and medical product waste.

Encoding and Storage of Information in Mechanical Metamaterials

Information processing using material's own properties has gained increasing interest. Mechanical metamaterials, due to their diversity of deformation modes and wide design space, can be used to realize information processing, such as computing and storage. Here a mechanical metamaterial system is demonstrated for material-based encoding and storage of data through programmed reconfigurations of the metamaterial's structured building blocks. Sequential encoding and decoding are achieved in the three-dimensional (3D) printed pixelated mechanical metamaterial via kirigami-based "pixels" with programmable, temperature-dependent bistability. The mechanical metamaterial is demonstrated via a multistep deformation of encoding messages of texts and surfaces with arrays of binary data, and then decoding them by applying a predetermined stretching and heating regimen to sequentially retrieve layers of stored information and display them on its surface. This approach serves as a general framework to enable the encoding and storage of data with mechanical metamaterials.

Linguistic Analysis Identifies Emergent Biomaterial Fabrication Trends for Orthopaedic Applications

The expanse of publications in tissue engineering (TE) and orthopedic TE (OTE) over the past 20 years presents an opportunity to probe emergent trends in the field to better guide future technologies that can make an impact on musculoskeletal therapies. Leveraging this trove of knowledge, a hierarchical systematic search method and trend analysis using connected network mapping of key terms is developed. Within discrete time intervals, an accelerated publication rate for anatomic orthopedic tissue engineering (AOTE) of osteochondral defects, tendons, menisci, and entheses is identified. Within these growing fields, the top-listed key terms are extracted and stratified into evident categories, such as biomaterials, delivery method, or 3D printing and biofabrication. It is then identified which categories decreased, remained constant, increased, or emerged over time, identifying the specific emergent categories currently driving innovation in orthopedic repair technologies. Together, these data demonstrate a significant convergence of material types and descriptors used across tissue types. From this convergence, design criteria to support future research of anatomic constructs that mimic both the form and function of native tissues are formulated. In summary, this review identifies large-scale trends and predicts new directions in orthopedics that will define future materials and technologies.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Advanced supramolecular design for direct ink writing of soft materials

The exciting advancements in 3D-printing of soft materials are changing the landscape of materials development and fabrication. Among various 3D-printers that are designed for soft materials fabrication, the direct ink writing (DIW) system is particularly attractive for chemists and materials scientists due to the mild fabrication conditions, compatibility with a wide range of organic and inorganic materials, and the ease of multi-materials 3D-printing. Inks for DIW need to possess suitable viscoelastic properties to allow for smooth extrusion and be self-supportive after printing, but molecularly facilitating 3D printability to functional materials remains nontrivial. While supramolecular binding motifs have been increasingly used for 3D-printing, these inks are largely optimized empirically for DIW. Hence, this review aims to establish a clear connection between the molecular understanding of the supramolecularly bound motifs and their viscoelastic properties at bulk. Herein, extrudable (but not self-supportive) and 3D-printable (self-supportive) polymeric materials that utilize noncovalent interactions, including hydrogen bonding, host-guest inclusion, metal-ligand coordination, micro-crystallization, and van der Waals interaction, have been discussed in detail. In particular, the rheological distinctions between extrudable and 3D-printable inks have been discussed from a supramolecular design perspective. Examples shown in this review also highlight the exciting macroscale functions amplified from the molecular design. Challenges associated with the hierarchical control and characterization of supramolecularly designed DIW inks are also outlined. The perspective of utilizing supramolecular binding motifs in soft materials DIW printing has been discussed. This review serves to connect researchers across disciplines to develop innovative solutions that connect top-down 3D-printing and bottom-up supramolecular design to accelerate the development of 3D-print soft materials for a sustainable future.

Stress-Induced Shape-Shifting Materials Possessing Autonomous Self-Healing and Scratch-Resistant Ability

Covalent adaptable networks (CANs) capable of both shape-shifting and self-healing ability offer a viable alternative to 4D printing technology to gain access to various complex shapes in a simplified manner. However, most of the reported CANs exhibit shape-shifting ability in the presence of temperature, light or chemical stimuli, which restricts their further utilization as realization of such a controlled environment is not feasible under complex scenarios. Herewith, we report a set of CANs based on a room-temperature exchangeable thia-Michael adduct, which undergoes rearrangement in network topology on application of external stress. These CANs with tensile strength (≤6 MPa) and modulus (≤71.4 MPa) adopt to any programmed shape under application of nominal stress. The CANs also exhibit stress-induced recyclability, self-welding and self-healing ability under ambient conditions. The transparency and ambient condition self-healing ability render these CANs to be utilized as scratch-resistant coatings on display items.

Analysis of an Adaptive Periodic Low-Frequency Wave Filter Featuring Magnetorheological Elastomers

This study aims to enhance and tune wave-propagation properties (Bandgaps) of periodic structures featuring magnetorheological elastomers (MREs). For this purpose, first, a basic model of periodic structures (square unit cell with cross-shaped arms), which does not possess noise filtering properties in the conventional configuration, is considered. A passive attenuation zone is then proposed by adding a cylindrical core mass to the center of the conventional geometry and changing arm angles, which permitted new bandgap areas. It was shown that better wave-filtering performance may be achieved by introducing a large radius of the cylindrical core as well as low negative cross-arm angles. The modified configuration of the unit cell was subsequently utilized as the basic model for the development of magnetoactive metamaterial using a MRE capable of varying the bandgaps areas upon application of an external magnetic field. The finite element model of the proposed MRE-based periodic unit cell was developed, and the Bloch theorem was employed to systematically investigate the ability of the proposed adaptive periotic structure to attenuate low-frequency noise and vibration. Results show that the proposed MRE-based periodic wave filter can provide wide bandgap areas which can be adaptively changed and tuned using the applied magnetic field. The findings in this study can provide an essential guide for the development of novel adaptive periodic structures to filter low-frequency noises in the wide frequency band.

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young's moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young's modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young's modulus.

Plasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis

Nanophotonics has been widely utilized in enhanced molecularspectroscopy or mediated chemical reaction, which has major applications in the field of enhancing sensing and enables opportunities in developing healthcare monitoring. This review presents an updated overview of the recent exciting advances of plasmonic biosensors in the healthcare area. Manufacturing, enhancements and applications of plasmonic biosensors are discussed, with particular focus on nanolisted main preparation methods of various nanostructures, such as chemical synthesis, lithography, nanosphere lithography, nanoimprint lithography, etc., and describing their respective advances and challenges from practical applications of plasmon biosensors. Based on these sensing structures, different types of plasmonic biosensors are summarized regarding detecting cancer biomarkers, body fluid, temperature, gas and COVID-19. Last, the existing challenges and prospects of plasmonic biosensors combined with machine learning, mega data analysis and prediction are surveyed.