搜索结果：metamaterials

Architected metamaterials derive their exceptional mechanical performance from their precisely-tailored underlying topologies, enabling access to regions of materials selection charts unattainable by conventional materials. While substantial advances have been achieved at micro-, meso-, and macroscales, further improvements are increasingly constrained, motivating exploration of nanoscale architected materials where surface and size effects dominate the overall multiphysics performance. Here, we resort to molecular dynamics simulations to systematically explore the mechanical response of nickel-based nano-architected metamaterials. By varying topology, relative density, crystallinity, and grain size, we demonstrate the broad tunability of elastic moduli, strength, and Poisson's ratio enabled by the rational design of underlying nano-architecture. Notably, the proposed nano-architected metamaterials outperform most previously reported architected materials at comparable densities, highlighting the effectiveness of nanoscale topology-driven designs. Atomistic analyses reveal that nanoscale free surfaces promote dislocation nucleation while inhibiting dislocation propagation, leading to flow stresses exceeding those of bulk counterparts. To bridge length scales and draw inspiration from crystallography, we design and 3D print hierarchical polymeric metamaterials and experimentally characterize their mechanical behavior. Despite being fabricated from an intrinsically brittle polymer, these structures exhibit topology-dependent stiffness and strength, alongside ductile plastic deformation and enhanced toughness, attributable to their hierarchical architectures. Together, this work introduces a crystallography-inspired architectural design paradigm for mechanical metamaterials and imparts scalable guidelines for achieving lightweight, mechanically efficient structures across multiple length scales.

4D scaffolds offer transformative potential for bone implants. Yet their application to metallic materials is constrained by the scarcity of suitable alloys and the requirement for harsh external stimuli to trigger shape change. Here, we introduce 4D metallic metamaterials driven by controlled biodegradation that combine biodegradable constraints with biometals of higher corrosion potential. We show that upon electrochemical degradation of the constraint, the metamaterials recover their original geometry-via stretching, bending, or expansion-generating programmable recovery forces tuned through structural design parameters. We demonstrate that when turned into scaffolds for bone implants, the 4D metallic metamaterials are cytocompatible and promote bone regeneration through the synergistic effects of bioactivity and mechanical stimulation in vivo. This strategy establishes a paradigm in 4D shape transformation of metal via metamaterial design, enabling bioactive, self-recovering implants with broad applicability across biomedical engineering.

Mechanical behavior of synthetic materials depends on their microstructure and geometric configurations. This dependency leads to unintended performance when the material remaps its microstructures for shape reconfigurations, such as diminished rigidity in unfolded aerospace morphing structures and reduced sensitivity in twisted soft sensors. Breaking this dependency through material design to improve overall performance has been a long-standing challenge. This work develops a transformation method to design a class of grounded metamaterials that decouples mechanical behavior from microstructure and shape reconfigurations. We fabricate these metamaterials and experimentally demonstrate both configuration-mapping-invariant displacement behavior and unconventional displacement control functions that have not previously been observed. We identify two physical principles that underpin the useful, but counterintuitive behavior: (i) Mapping-invariant displacement fields are the result of body torques that automatically balance non-concurrent internal forces from microstructure reconfigurations; (ii) Tailored displacement control functions are determined by Willis springs pinned to the ground. As a result, the grounded metamaterials are shown to enable the design of highly reconfigurable material systems that demonstrate tailored deformation behavior regardless of their microscopic and geometric configurations.

Directional audio systems, enabling personal sound sweet spots and spatially selective audio delivery, typically rely on parametric acoustic arrays that leverage nonlinear interactions of ultrasound beams to overcome the directivity constraint dictated by the wavenumber-aperture product. Such systems, known as parametric array loudspeakers (PALs), traditionally require phased arrays comprising tens to hundreds of ultrasonic emitters-posing persistent challenges for widespread adoption due to high cost, system complexity, and limited design flexibility. Here, we show an alternative form factor: a compact, single-body ultrasonic transducer integrated with dual-domain metamaterials, termed metamaterials-integrated parametric array loudspeakers (MiPALs). Unlike conventional PALs, the MiPAL utilizes a single piezoelectric driver co-integrated with dual-domain metamaterials-consisting of a co-designed acoustic metasurface and elastic meta-units-to yield a simple, cost-effective, and highly integrated architecture. To realize this, we exploit a unified design strategy that integrates theoretical modeling, metamaterial-transducer co-design, and comprehensive quantitative analyses of linear and nonlinear acoustic fields. Experiments demonstrate that the MiPAL generates ultra-broadband directional sound over four octaves, spanning from 500 Hz to 10 kHz. These findings underscore the potential of metamaterial-transducer co-integrated platforms for advanced audio systems, laying the foundation for immersive audio, spatial sound delivery, and extended reality.

Designing acoustic metamaterials with high sound absorption coefficients under low-frequency and broad-bandwidth conditions remains a highly challenging task. This paper proposes a tunable acoustic metamaterial based on a multistable structure to achieve low-frequency broadband sound absorption. The metamaterial integrates multistable thin-walled tube (MTWT) units with embedded neck structures to form Helmholtz-type resonators. The introduction of multistability enables a synergistic combination of multiple dissipation mechanisms: beyond classical Helmholtz resonance, the structure incorporates acoustic soft boundaries induced by thin-wall vibrations of the multistable units, as well as enhanced thermoviscous dissipation within the confined narrow regions. This multi-mechanism coupling not only enriches acoustic energy dissipation pathways but also provides a structural basis for tunable sound absorption. Furthermore, the multistable characteristic offers discrete and self-sustained geometric configurations, allowing the absorber to switch between well-defined acoustic states without continuous external actuation, thereby ensuring robust and energy-efficient tunability. Experimental and simulation results demonstrate that, considering low frequency, bandwidth, and structural compactness, the metamaterial achieves near-perfect sound absorption within the frequency range of 436-1141 Hz, demonstrating significant potential for broad applications in low-frequency broadband noise control. Notably, within this range, the absorption coefficient can be continuously tuned from 0 to 1, representing a highly flexible and nontrivial capability enabled by the multistable design. This work provides a new strategy for the design of acoustic metamaterials with multi-dimensional tunability.

The limited and scattered fatigue performances and their difficult predictability remain critical barriers for the widespread adoption of Laser-based Powder Bed Fusion (L-PBF) metamaterials in engineering applications, as fatigue damage initiation is highly sensitive to manufacturing-induced geometric imperfections. While X-ray computed tomography (CT) provides high-fidelity as-built reconstructions fundamental for metamaterials' structural health monitoring, its cost and complexity hinder routine integration into fatigue assessment workflows at the design stage. In this work, we propose a computationally efficient framework for the development of synthetic as-built CAD models, serving as digital twins for fatigue life and failure location prediction. The proposed model is herein reported for L-PBF Ti-6Al-4V struts, the elemental building blocks of metamaterial architectures, manufactured at different building orientations. Leveraging stereomicroscopy input images, a modular reconstruction pipeline capturing orientation-dependent surface morphology and partially fused particles allows the generation of as-built CAD models that retain the geometric variability governing fatigue behaviour, without reliance on volumetric imaging. Synthetic models are coupled with finite element analyses and a statistical strain energy density criterion to identify failure-critical locations. Validation against CT-derived counterparts demonstrates close morphological agreement and, since the design stage, the ability to estimate fatigue life and predict experimental failure locations within established scatter bands.

Internal stress fields govern failure and performance in architected metamaterials, yet volumetric stress evaluation typically requires volumetric imaging or repeated finite element (FE) simulations. Such requirements limit rapid iteration and practical non-destructive assessment, particularly for bicontinuous spinodoid architectures whose mechanical behavior is strongly geometry-dependent. Here, we present a physics- and topology-constrained generative framework that reconstructs internal 3D stress fields directly from surface observations alone. By leveraging surface-derived stress representations and regularizing the prescribed equilibrium equation and boundary-condition residuals through a differentiable physics constraint, the model produces volumetric stress fields that are structurally coherent and mechanically informed. In addition, topology-aware feature regularization further preserves characteristic bicontinuous load-transfer pathways during reconstruction. Across a GRF-defined spinodoid design space, the approach demonstrates stable stress-field recovery and reliable localization of stress concentration regions, including in the upper effective stiffness regime beyond the stiffness range predominantly represented during training. Using smartphone-acquired surface images of additively manufactured specimens, we demonstrate surface-based non-destructive stress inference with spatial correspondence between reconstructed high-stress regions and experimentally observed fracture initiation. Furthermore, embedding the framework within a genetic algorithm enables stress-field-driven parameter optimization in the global-local GRF space, resulting in experimentally validated improvements in multi-directional mechanical response. Within this controlled spinodoid design manifold, this work establishes a surface-based route to equilibrium-regularized internal stress-field reconstruction and field-driven design without requiring volumetric measurements at deployment.

Interactions between solitary waves are crucial for understanding nonlinear phenomena in systems such as optics, fluid dynamics, and mechanical metamaterials. Rarefaction solitary waves, in particular, offer insight into nonlinear wave dynamics in strain-softening media. Despite their proposed applications in waveguides and energy harvesting, key characteristics-particularly solitonic collisions-are not yet fully understood due to energy dissipation and the need for high-precision measurement techniques. In this work, we introduce an experimental platform for studying pure rarefaction solitons in a strain-softening lattice. Our results show that both symmetric and asymmetric collisions display elastic interactions and amplitude-dependent phase shifts. The experimentally observed dynamics, including soliton speed and phase shifts, closely match numerical simulations and analytical predictions based on the Boussinesq approximation. These findings not only validate our platform but also highlight the potential of mechanical rarefaction solitons for probing nonlinear wave interactions and advancing wave-based computing.

Dualities are mappings that connect seemingly unrelated physical systems, enabling simplification and reinterpretation via duality transformations. However, prior studies have been predominantly limited to one-to-one mappings isomorphic to a Z2 group, where self-duality occurs only at a single point at which the lattice maps onto itself under a duality transformation. Here, we extend the duality framework by incorporating gauge fields that modify symmetry representations, constructing more general duality groups, Z2×Z2 in two-dimensional systems and Z26 in three-dimensional systems. We theoretically establish and experimentally validate that such gauge-field-induced duality groups link multiple distinct metamaterials across different symmetry classifications while sharing identical band structures. Notably, in three-dimensional systems, gauge fields promote self-duality from a single point to a set, yielding fourfold degeneracies across the entire Brillouin zone and an eightfold-degenerate double Dirac point. Our work expands duality research and deepens the understanding of hidden symmetries in complex physical systems.

Three-dimensional graphene metamaterials based on triply periodic minimal surfaces (TPMS) can achieve exceptional mechanical properties; however, the P-type Schwarz Primitive surface remains largely underexplored. For the first time, we quantified how unit cell size (30 Å, 45 Å, 60 Å), architectural dimensionality (0D, 1D, 2D), and tensile strain rate govern the uniaxial strength and fracture strain of pure carbon P-type multidimensional structures. Reactive molecular dynamics simulations reveal that both strength and fracture strain increase significantly as the unit cell size decreases. Furthermore, these structures exhibit distinct mechanical responses depending on their dimensionality, demonstrating a pronounced size effect. Additionally, strain rate plays a critical role: moderate strain rates facilitate atomic rearrangement and the formation of truss-like load-bearing networks, thereby enhancing load-bearing capacity. These findings establish clear structure-property relationships for scalable, ultralight carbon nano-architectures. All atomistic tensile tests were performed with LAMMPS using the AIREBO potential to describe C-C interactions. P-type Schwarz Primitive surfaces were generated with an in-house protocol that hexagonalizes a Delaunay triangulation without Voronoi tessellation. After energy minimization and 50 ps NVT equilibration at 300 K with a Nosé-Hoover thermostat, uniaxial tension was applied in strain-control mode while the lateral cell faces remained traction-free. Stress-strain curves and atomic fracture sequences were analyzed with OVITO.

Deep learning has been extensively employed in the prediction of metamaterial properties. However, the multi-layer perceptron-kernelled methods lack interpretability and are highly dependent on large datasets, making the end-to-end mapping opaque and computationally expensive and hindering the exploration and application of physical mechanisms. To address these issues, the Kolmogorov-Arnold Operator Informed Network (KAOIN) method is proposed, achieving the lightest neural structure under small-sample conditions while improving accuracy and convergence speed. On this basis, a coupled metamaterial performance prediction framework is constructed, enabling dataset construction, high-fidelity analysis, and performance visualization. This framework is capable of predicting the specific energy absorption of the gradient triply-periodic minimal surface through a mere 50 sets of data. The interpretability and ability to extract physical laws of KAOIN were comprehensively verified through spatial symmetry and the Gibson-Ashby theoretical model. It has been demonstrated that a geometry-performance relationship improves accuracy by up to 44.6% and the convergence speed by 48-89%. This study introduces a novel neural network paradigm capable of exploring physical mechanisms in small datasets and demonstrates its potential for accurately modeling the geometric-performance relationships of metamaterials.

A metamaterial chain uses a physical learning framework to learn, forget, and relearn different shape changes.

This work proposes a gap-opening double-elliptical cylinder metamaterial that achieves dual polarization multi-resonance mode synergistic excitation by introducing out-of-plane symmetric breaking. Under TE polarization, an exceptionally high Q-factor (4.27 × 105) of quasi-BIC (Mode 1) is realized, with excellent sensing sensitivity of 750 GHz/RIU, whereas two different quasi-BIC modes (called Mode 2 and Mode 3) are gained under TM polarization. In particular, the near-field coupling of Mode 2 and guided-mode resonance (GMR) gives rise to an electromagnetically induced transparency (EIT), whose performance could be flexibly controlled by precisely tuning the structural asymmetry and other relevant parameters, thereby offering versatile spectral tunability. Multipole decomposition and near-field analysis reveal the formation mechanisms of dual-polarization multi-resonance modes. Our results could have application prospects in fields such as beamforming and biomedical sensing.

The plane wave transfer matrix method is a robust process for acquiring the acoustic properties of an arbitrary material. To achieve this, the specimen being tested is inserted in a waveguide and subjected to the four-microphone method to capture the pressure fields. This is a powerful and accurate process for simplifying complex three-dimensional geometries to simpler equivalent acoustic properties suitable for two-dimensional analysis, but it does not work for structures lacking plane wave symmetry. Sometimes, it is favorable to characterize a structure through spherical coordinates. A spherically spiraling acoustic metamaterial horn is an appropriate case of a technology that does not fit this traditional planar model. To acquire the acoustic properties that define structures such as these, the four-microphone method, the transfer matrix, and the scattering matrix, for a spherically symmetric system are derived. Unlike the planar transfer matrix analysis, the resulting acoustic properties in this paper are more complex. Variations of the spiraling acoustic metamaterial horn are evaluated by this method both in experimental measurements and simulated environments. This structure and methodology offer ample opportunities for classifying many spherically symmetric acoustic devices with an application in areas, such as ultrasound and audio technologies.