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Biomimicking natural structures to create structural materials with superior mechanical performance is an area of extensive attention, yet achieving both high strength and toughness remains challenging. This study presents a novel bottom-up approach using self-assembled block copolymer templating to synthesize bicontinuous nanohybrids composed of well-ordered nanonetwork hydroxyapatite (HAp) embedded in poly(methyl methacrylate) (PMMA). This structuring transforms intrinsically brittle HAp into a ductile material, while hybridization with PMMA alleviates the strength reduction caused by porosity. The resultant bicontinuous PMMA/HAp nanohybrids, reinforced at the interface, exhibit high strength and toughness due to the combined effects of topology, nanosize, and hybridization. This work suggests a conceptual framework for fabricating flexible thin films with mechanical properties significantly surpassing those of traditional composites and top-down approaches.

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The confinement of waves within a waveguide can enable directional transmission of signals, which has found wide applications in communication, imaging, and signal isolation. Extending this concept to static systems, where material deformation is piled up along a spatial trajectory, remains elusive due to the sensitivity of localized deformation to structural defects and impurities. Here, we propose a general framework to characterize localized static deformation responses in two-dimensional generic static mechanical metamaterials, by exploiting the duality between space in static systems and time in one-dimensional non-reciprocal wave systems. An internal time-reverse symmetry is developed by the space-time duality. Upon breaking this symmetry, quasi-static load-induced deformation can be guided to travel along a designated path, thereby realizing a stress guide. A combination of time-reverse and inversion symmetries discloses the parity-time symmetry inherent in static systems, which can be leveraged to achieve directional deformation shielding. The tailorable stress guides can find applications in various scenarios, ranging from stress shielding and energy harvesting in structural tasks to information processing in mechanical computing devices.

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Generative machine learning models have shown notable success in identifying architectures for metamaterials-materials whose behavior is determined primarily by their internal organization-that match specific target properties. By examining kirigami metamaterials, in which dependencies between cuts yield complex design restrictions, we demonstrate that this perceived success in the employment of generative models for metamaterials might be akin to survivorship bias. We assess the performance of the four most popular generative models-the Variational Autoencoder (VAE), the Generative Adversarial Network (GAN), the Wasserstein GAN (WGAN), and the Denoising Diffusion Probabilistic Model (DDPM)-in generating kirigami structures. Prohibiting cut intersections can prevent the identification of an appropriate similarity measure for kirigami metamaterials, significantly impacting the effectiveness of VAE and WGAN, which rely on the Euclidean distance-a metric shown to be unsuitable for considered geometries. This imposes significant limitations on employing modern generative models for the creation of diverse metamaterials.

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Auxetics are materials displaying a negative Poisson's ratio, i.e., getting thicker in one or both transverse axes when subject to strain. In 2018, liquid crystal elastomers (LCEs) displaying auxetic behaviour, achieved via a biaxial reorientation, were first reported. Studies have since focused on determining the physics underpinning the auxetic response, with investigations into structure-property relationships within these systems so far overlooked. Herein, we report the first structure-property relationships in auxetic LCEs, examining the effect of changes to the length of the spacer chain. We demonstrate that for LCEs with between six and four carbons in the spacer, an auxetic response is observed, with the threshold strain required to achieve this response varying from 56% (six carbon spacers) to 81% (four carbon spacers). We also demonstrate that Poisson's ratios as low as -1.3 can be achieved. Further, we report that the LCEs display smectic phases with spacers of seven or more carbons; the resulting internal constraints cause low strains at failure, preventing an auxetic response. We also investigate the dependence of the auxetic threshold on the dynamics of the samples, finding that when accounting for the glass transition temperature of the LCEs, the auxetic thresholds converge around 56%, regardless of spacer length.

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The complex sequential response of frustrated materials results from the interactions between material bits called hysterons. Hence, a central challenge is to understand and control these interactions, so that materials with targeted pathways and functionalities can be realized. Here, we show that hysterons in serial configurations experience geometrically controllable antiferromagnetic-like interactions. We create hysteron-based metamaterials that leverage these interactions to realize targeted pathways, including those that break the return point memory property, characteristic of independent or weakly interacting hysterons. We uncover that the complex response to sequential driving of such strongly interacting hysteron-based materials can be described by finite state machines. We realize information processing operations such as string parsing in materia, and outline a general framework to uncover and characterize the FSMs for a given physical system. Our work provides a general strategy to understand and control hysteron interactions, and opens a broad avenue toward material-based information processing.

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Topological bosonic systems have recently aroused intense interests in exploring exotic phenomena that have no counterparts in electronic systems. The squeezed bosonic interaction in these systems is particularly interesting, because it can modify the vacuum fluctuations of topological states, drive them into instabilities, and lead to topological parametric oscillators. However, these phenomena remain experimentally elusive because of limited nonlinearities in most existing topological bosonic systems. Here, a topological parametric phonon oscillator is experimentally realized based on a nonlinear nanoelectromechanical Dirac-vortex cavity with strong squeezed interaction. Specifically, the Dirac-vortex cavity is parametrically driven to provide phase-sensitive amplification for topological phonons, leading to the observation of coherent parametric phonon oscillation above the threshold. Additionally, it is confirmed that the random frequency variation caused by fabrication disorders can be suppressed effectively by increasing the cavity size, while the free spectral range reduces at a much slower rate, which benefit the realization of large-area single-mode lasers. Our results represent an important advance in experimental investigations of topological physics with large bosonic nonlinearities and parametric gain. This article is protected by copyright. All rights reserved.

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Machines have continually developed with the needs of daily life and industrial applications. While the careful design of molecular-scale devices often displays enhanced properties along with mechanical movements, controlling mechanics within solid-state molecular structures remains a significant challenge. Here, we explore the distinct mechanical properties of zeolitic imidazolate frameworks (ZIFs)-frameworks that contain hidden mechanical components. Using a combination of experimental and theoretical approaches, we uncover the machine-like capabilities of ZIFs, wherein connected composite building units operate similarly to a mechanical linkage system. Importantly, this research suggests that certain ZIF subunits act as core mechanical components, paving an innovative view for the future design of solid-state molecular machines.

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Self-sealing is one of the fascinating functions in nature that enables living material systems to respond immediately to damage. A prime plant model is Delosperma cooperi, which can rapidly self-seal damaged succulent leaves by systematically deforming until the wound closes. Inspired by this self-sealing principle, a novel programmable mechanical metamaterial has been developed to mimic the underlying damage management concept. This material is able to react autonomously to changes in its physical condition caused by an induced damage. To design this ability into the programmable metamaterial, a permeable unit cell design has been developed that can change size depending on the internal pressure. The parameter space and associated mechanical functionality of the unit cell design is simulated and analyzed under periodic boundary conditions and various pressures. The principles of self-sealing behavior in designed metamaterials are investigated, crack closure efficiency is identified for different crack lengths, the limitations of the proposed approach are discussed, and successful crack closure is experimentally demonstrated in the fabricated metamaterial. Although this study facilitates the first step on the way of integrating new bio-inspired principles in the metamaterials, the results show how programmable mechanical metamaterials might extend materials design space from pure properties to life-like abilities.

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Transformable mechanical structures can switch between distinct mechanical states. Whether this kind of structure can be self-assembled from simple building blocks at microscale is a question to be answered. In this work, we propose a self-assembly strategy for these structures based on a nematic monolayer of segmented colloidal rods with lateral cutting. By using Monte Carlo simulation, we find that rods with different cutting degrees can self-assemble into different crystals characterized by bond coordination z that varies from 3 to 6. Among these, we identify a transformable superisostatic structure with pgg symmetry and redundant bonds (z = 5). We show that this structure can support either soft bulk modes or soft edge modes depending on its Poisson's ratio, which can be tuned from positive to negative through a uniform soft deformation. We also prove that the bulk soft modes are associated with states of self-stress along the direction of zero strain during uniform soft deformation. The self-assembled transformable structures may act as mechanical metamaterials with potential applications in micromechanical engineering.

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The flowing, jamming, and avalanche behavior of granular materials is satisfyingly universal and vexingly hard to tune: A granular flow is typically intermittent and will irremediably jam if too confined. Here, we show that granular metamaterials made from particles with a negative Poisson's ratio yield more easily and flow more smoothly than ordinary granular materials. We first create a collection of auxetic grains based on a re-entrant mechanism and show that each grain exhibits a negative Poisson's ratio regardless of the direction of compression. Interestingly, we find that the elastic and yielding properties are governed by the high compressibility of granular metamaterials: At a given confinement, they exhibit lower shear modulus, lower yield stress, and more frequent, smaller avalanches than materials made from ordinary grains. We further demonstrate that granular metamaterials promote flow in more complex confined geometries, such as intruder and hopper geometries, even when the packing contains only a fraction of auxetic grains. Moreover, auxetic granular metamaterials exhibit enhanced impact absorption. Our findings blur the boundary between complex fluids and metamaterials and could help in scenarios that involve process, transport, and reconfiguration of granular materials.

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Recent advances in two-photon polymerization fabrication processes are paving the way to creating macroscopic metamaterials with microscale architectures, which exhibit mechanical properties superior to their bulk material counterparts. These metamaterials typically feature lightweight, complex patterns such as lattice or minimal surface structures. Conventional tools for investigating these microscale structures, such as scanning electron microscopy, cannot easily probe the internal features of these structures, which are critical for a comprehensive assessment of their mechanical behavior. In turn, we demonstrate an optical confocal microscopy-based approach that allows for high-resolution optical imaging of internal deformations and fracture processes in microscale metamaterials under mechanical load. We validate this technique by investigating an exemplary metamaterial lattice structure of 80 × 80 × 80 µm3 in size. This technique can be extended to other metamaterial systems and holds significant promise to enhance our understanding of their real-world performance under loading conditions.

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Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled "Deep-DRAM" that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem.

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Inspired by the adaptive features exhibited by biological organisms like the octopus, soft machines that can tune their shape and mechanical properties have shown great potential in applications involving unstructured and continuously changing environments. However, current soft machines are far from achieving the same level of adaptability as their biological counterparts, hampered by limited real-time tunability and severely deficient reprogrammable space of properties and functionalities. As a steppingstone toward fully adaptive soft robots and smart interactive machines, an encodable multifunctional material that uses graphical stiffness patterns is introduced here to in situ program versatile mechanical capabilities without requiring additional infrastructure. Through independently switching the digital binary stiffness states (soft or rigid) of individual constituent units of a simple auxetic structure with elliptical voids, in situ and gradational tunability is demonstrated here in various mechanical qualities such as shape-shifting and -memory, stress-strain response, and Poisson's ratio under compressive load as well as application-oriented functionalities such as tunable and reusable energy absorption and pressure delivery. This digitally programmable material is expected to pave the way toward multienvironment soft robots and interactive machines.

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Inspired by the potential of architected materials for achieving biomimicking functionalities and the advancement of multi-material additive manufacturing to fabricate parts with complex structures and heterogeneous material distributions, this study investigates the feasibility of using a multi-material, flexible chain mail sheet for the design of an additively manufactured artificial spinal disc for reproducing patient-specific anisotropic and nonlinear rotational behaviors. The application of a chain mail-based structure is motivated by its similarities in behaviors compared with a natural disc's fiber network that likewise has negligible bending stiffness and shape-changing ability. The proposed approach for the chain mail sheet design includes an initial characterization of the uniaxial tensile responses of the chain mail unit cell defined as the basic building block of the chain mail sheet, modeling and response calculation, and material optimization. Results show that the additively manufactured chain mail sheet is not only able to exhibit a natural strain-stiffening rotational response but also is able to reproduce natural anisotropy of three natural disc specimens in the six most common rotational scenarios in daily life. This study shows the potential of additively manufactured mechanical-metamaterials-inspired structures for implant design to restore natural mechanics.

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Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing is proposed, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eliminates the demanding requirements for the fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechanical logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is experimentally implemented by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. The research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials can have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond.

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Grip strength is a biomarker of frailty and an evaluation indicator of brain health, cardiovascular morbidity, and psychological health. Yet, the development of a reliable, interactive, and point-of-care device for comprehensive multi-sensing of hand grip status is challenging. Here, a relation between soft buckling metamaterial deformations and built piezoelectric voltage signals is uncovered to achieve multiple sensing of maximal grip force, grip speed, grip impulse, and endurance indicators. A metamaterial computational sensor design is established by hyperelastic model that governs the mechanical characterization, machine learning models for computational sensing, and graphical user interface to provide visual cues. A exemplify grip measurement for left and right hands of seven elderly campus workers is conducted. By taking indicators of grip status as input parameters, human-computer interactive games are incorporated into the computational sensor to improve the user compliance with measurement protocols. Two elderly female schizophrenic patients are participated in the real-time interactive point-of-care grip assessment and training for potentially sarcopenia screening. The attractive features of this advanced intelligent metamaterial computational sensing system are crucial to establish a point-of-care biomechanical platform and advancing the human-computer interactive healthcare, ultimately contributing to a global health ecosystem.

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Rationally engineered porous structures enable lightweight broadband electromagnetic (EM) wave absorbers for countering radar signals or mitigating EM interference between multiple components. However, the scalability of such structures has been hindered by their limited mechanical properties resulting from low density. Herein, an additively manufactured Kelvin foam-based EM wave absorber (KF-EMA) is reported that exhibits multifunctionality, namely EM wave absorption and light-weighted load-bearing structures with constant relative stiffness made possible using bending-dominated lattice structures. Based on tuning design parameters, such as the backbone structures and constituent materials, the proposed KF-EMA features a multilayered 3D-printed design with geometrically optimized KF structures made of carbon black-based backbone composites. The developed KF-EMA demonstrated an absorbance greater than 90% at frequencies ranging from 5.8 to 18 GHz (average EM wave absorption rates of 95.89% and maximum of 99.1% at 15.8 GHz), while the low-density structures of the absorber (≈200 kg m-3 ) still maintained a compression index between the stiffness and relative density (n = 2) under compression. The design strategy paves the way for using metamaterials as mechanically reinforced EM wave absorbers that enable multifunctionality by optimizing unit-cell parameters through a single and low-density structure.

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Structural components designed to absorb energy and shield a more valuable structure ideally require mechanical properties that combine a relatively high load-carrying capacity followed by a practically zero stiffness. This ensures that a specified energy quantity may be absorbed within a limited displacement and that any stress transfer to the valuable structure is minimized. Material damage has been historically mobilized to provide such properties, but this obviously renders such components to be single-use. By contrast, mobilization of elastic instability can also provide the desired combination of properties but without necessarily damaging the material. This reveals an intriguing possibility of such components being potentially repairable and theoretically re-usable with no significant loss in performance. A series of analytical, finite-element and experimental studies are presented for a bespoke mechanical metamaterial arrangement that is designed to buckle sequentially and behave with the desired 'high strength-low stiffness' characteristic. It is found that the various axial and rotational stiffnesses associated with the geometric arrangement and its constituent connections may be tuned to provide the desired mechanical behaviour within the elastic range and delay the onset of significant damage, thereby rendering the concept of harnessing instability to be feasible.

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Active mechanical metamaterials with customizable structures and deformations, active reversible deformation, dynamically controllable shape-locking performance and stretchability are highly suitable for applications in soft robotics and flexible electronics, yet it is challenging to integrate them due to their mutual conflicts. Here, we introduce a class of phase-transforming mechanical metamaterials (PMMs) that integrate the above properties. Periodically arranging basic actuating units according to the designed pattern configuration and positional relationship, PMMs can customize complex and diverse structures and deformations. Liquid-vapor phase transformation provides active reversible large deformation while a silicone matrix offers stretchability. The contained carbonyl iron powder endows PMMs with dynamically controllable shape-locking performance, thereby achieving magnetically assisted shape locking and energy storing in different working modes. We build a theoretical model and finite element simulation to guide the design process of PMMs, so as to develop a variety of PMMs with different functions suitable for different applications, such as a programmed PMM, reconfigurable antenna, soft lens, soft mechanical memory, biomimetic hand, biomimetic flytrap and self-contained soft gripper. PMMs are applicable to achieve various 2D deformations and 2D-to-3D deformations, and integrate multiple properties, including customizable structures and deformations, active reversible deformation, rapid reversible shape locking, adjustable energy storing and stretchability, which could open a new application avenue in soft robotics and flexible electronics.

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Extrusion-based 4D-printing, which is an emerging field within additive manufacturing, has enabled the technical transfer of bioinspired self-shaping mechanisms by emulating the functional morphology of motile plant structures (e.g., leaves, petals, capsules). However, restricted by the layer-by-layer extrusion process, much of the resulting works are simplified abstractions of the pinecone scale's bilayer structure. This paper presents a new method of 4D-printing by rotating the printed axis of the bilayers, which enables the design and fabrication of self-shaping monomaterial systems in cross sections. This research introduces a computational workflow for programming, simulating, and 4D-printing differentiated cross sections with multilayered mechanical properties. Taking inspiration from the large-flowered butterwort (Pinguicula grandiflora), which shows the formation of depressions on its trap leaves upon contact with prey, we investigate the depression formation of bioinspired 4D-printed test structures by varying each depth layer. Cross-sectional 4D-printing expands the design space of bioinspired bilayer mechanisms beyond the XY plane, allows more control in tuning their self-shaping properties, and paves the way toward large-scale 4D-printed structures with high-resolution programmability.