Porous Metamaterials Research Articles

Acoustic properties of porous metamaterials featured by acoustic streaming, homogenization based modelling

We consider secondary effects induced by acoustic waves in viscous barotropic fluids saturating pores in elastic, or rigid scaffolds with elastically suspended particles. The asymptotic homogenization method is applied to derive models of the acoustic wave propagation and of the related acoustic streaming (AS) as the secondary effect. This phenomenon originating due to the nonlinearity (divergence of the Reynolds stress) in the Navier Stokes equation is retained using the perturbation analysis. This yields the first and the second order sub-problem enabling to linearize the model governing fluid dynamics. The local AS in the microstructure produces forces acting on the suspended particles, transforming the pore geometry, hence, modulating the acoustic properties. As the consequence, the permeability involved in both the subproblems is modified due to the slowly oscillating particles. The sensitivity analysis can be employed to introduce the homogenized coefficients of the model depending on the particle motion.

3D Printing of Periodic Porous Metamaterials for Tunable Electromagnetic Shielding Across Broad Frequencies

HighlightsThe stable periodic porous shielding materials were prepared by combining the strategies of 3D printing and metamaterial design.The relationship between porous material structure and electromagnetic interference shielding efficiency (EMI SE) effectiveness was deeply explored, revealing the important structural parameters for realizing tunable EMI SE property.The optimized design of the periodic porous shielding box achieves effective EMI shielding in a wide wavelength range (over 2.4 GHz).

Tensile deformation and failure of AlSi10Mg random cellular metamaterials

Cellular metamaterials are an emerging class of porous materials, in which the topology of the solid is precisely-engineered to achieve attractive combinations of properties. This work specifically examines the tensile response of a novel type of cellular metamaterials, in which pores of arbitrary elliptical shape are randomly-dispersed into an aluminum alloy matrix. Their porous mesostructure is generated numerically via a random sequential absorption algorithm, and is fabricated by laser powder bed fusion from AlSi10Mg powders. The results – obtained by means of digital image correlation combined with X-ray tomography – highlight the advantages offered by a random cellular topology. They are notably a low sensitivity to geometric imperfections (which inevitably result from manufacturing), coupled with the ability to delay long-wavelength strain localization, which is in turn responsible for failure. Structural disorder also leads to highly heterogeneous deformation patterns, which result from the interaction between geometric pores and promote void growth and coalescence during plastic straining. The presence of manufacturing defects exacerbates void interaction and promotes early plastic flow localization. Interestingly, experiments reveal that this class of porous solids effectively displays features of the ductile fracture of metals at the mesoscale. For example, void-sheeting is observed with increasing pore aspect ratio and is accompanied by large geometric distortions of the voids upon coalescence. Measured data for the pore strains are highly scattered and show a departure from McClintock’s model predictions for the voids within the fracture band. Collectively, this study highlights the potential offered by random porous metamaterials, which can be harnessed to revisit the complex mechanisms of ductile fracture at the mesoscale.

Reconstruction and Generation of Porous Metamaterial Units via Variational Graph Autoencoder and Large Language Model

Abstract In this paper, we propose and compare two novel deep generative model-based approaches for the design representation, reconstruction, and generation of porous metamaterials characterized by complex and fully connected solid and pore networks. A highly diverse porous metamaterial database is curated, with each sample represented by solid and pore phase graphs and a voxel image. All metamaterial samples adhere to the requirement of complete connectivity in both pore and solid phases. The first approach employs a Dual Decoder Variational Graph Autoencoder to generate both solid phase and pore phase graphs. The second approach employs a Variational Graph Autoencoder for reconstructing/generating the nodes in the solid phase and pore phase graphs and a Transformer-based Large Language Model (LLM) for reconstructing/generating the connections, i.e., the edges among the nodes. A comparative study is conducted, and we found that both approaches achieved high accuracy in reconstructing node features, while the LLM exhibited superior performance in reconstructing edge features. Reconstruction accuracy is also validated by voxel-to-voxel comparison between the reconstructions and the original images in the test set. Additionally, discussions on the advantages and limitations of using LLMs in metamaterial design generation, along with the rationale behind their utilization, are provided.

Hierarchical Gradient Structural Porous Metamaterial with Selective Spectral Response for Daytime Passive Radiative Cooling

AbstractPorous photonic structures have greatly advanced large‐scale radiative cooling application owing to its satisfying optical properties, easy‐designation, and low cost. However, current reported porous radiative cooling materials mainly focuses on uniform or random pore distribution structure, which failed to achieve precise spectrum control for sunlight and mid‐infrared light, thereby weakening the radiative cooling performance. Herein, gradient structural porous metamaterials (GSPMs) are proposed and successfully constructed for maximizing cooling effectiveness based on step‐by‐step freeze‐casting technology. The designed GSPMs with gradient micro‐nano porous structure can wide‐range scatter entire solar spectrum while possess gradual refractive index transition to increase air‐medium interface absorption at mid‐infrared regions. Notably, solar reflectivity and mid‐infrared emissivity of GSPMs reach 97.3% and 97.6%, achieving maxinum cooling temperature of 8.7 °C and net cooling power of 94.1 W m−2, presenting a higher cooling effect than random porous materials. Moreover, GSPMs enable achieve compatibility with color aesthetic and cooling superiorities due to selective spectra response behavior, as well as can be applied in building materials, realizing efficient energy saving and CO2 emission reduction. This work proposed gradient porous structure can achieve highly efficient daytime radiative cooling, offering new possibilities in the structure design of next‐generation porous radiative cooling materials.

Investigation on mechanical properties of Ti-6Al-4 V multilayer micro-lattice biomaterials under dynamic compression loading

Novel skull repair Ti-6Al-4 V multilayer micro-lattice biomaterials (MMBs) proposed recently have promising application in clinical practice. Herein, the dynamic compression performances of MMBs are concerned and investigated by Split Hopkinson Pressure Bar (SHPB) system and finite element (FE) methods. Typical shear band, strut yielding and damage deformation modes of MMBs are characterized by FE and SHPB experiment. Through density gradient regulation, deformation mode of MMBs is transformed from shear band to localized collapse pattern. It is interesting that MMBs with different unit cell sizes exhibit different strain rate sensitivities. For strain rate-insensitive MMBs (BCC(3), BCC(5) and BCCZ(3)), the influence of relative density on strength shows a linear increasing trend under both low and high strain rates. For strain rate-sensitive ones (BCC(7), BCCZ(5) and BCCZ(7)), the influence of relative density on strength under low strain rate is stronger than that under high strain rate. The strength range of MMBs matches skull/bone materials well under same loading strain rate. This work not only reveals dynamic mechanical properties of MMBs but also has enlightenment for investigating dynamic properties of porous metamaterials.

Impact induced compression and decompression waves in porous meta-materials modeled using a continuum theory of phase transitions

Porous meta-materials with both regular and random microstructure are of intense research interest today due to their interesting dynamical properties, including but not limited to, their acoustic band structure, shock absorption properties, and fracture toughness. Some of these materials can exist in a rarefied or densified state depending on the state of stress, and recover their original configuration after a cycle of loading and unloading. Often, they exhibit a hysteretic stress–strain response under quasistatic uniaxial compression. As such, many aspects of their mechanical behavior can be captured using a continuum theory of phase transitions. In this work, the dynamical behavior of such materials is explored. It is shown that the impact problem for these materials can result in propagating shocks, phase boundaries, and fans. The impact problems admit multiple solutions for the same set of initial and boundary conditions leading to non-uniqueness. This non-uniqueness can be remedied using a nucleation criterion and kinetic laws, as is known from the continuum theory of phase transitions. The fan solutions which arise in decompressive impact problems have not received much attention in the literature and may be regarded as a novel contribution of this work. The analysis presented here may have applications in the dynamic behavior of a broad class of porous materials including architected truss-like metastructures and random fiber networks.

Introduction of Asymmetry to Enhance Thermal Transport in Porous Metamaterials at Low Temperature

Introducing porosity with different degrees of disorder has been widely used to regulate thermal properties of materials, which generally results in decrease of thermal conductivity. We investigate the thermal conductivity of porous metamaterials in the ballistic transport region by using the Lorentz gas model. It is found that the introduction of asymmetry and Gaussian disorder into porous metamaterials can lead to a strong enhancement of thermal conductivity. By dividing the transport process into ballistic transport, non-ballistic transport, and unsuccessful transport processes, we find that the enhancement of thermal conductivity originates from the significant increase ballistic transport ratio. The findings enhance the understanding of ballistic thermal transport in porous materials and may facilitate designs of high-performance porous thermal metamaterials.

3D printing Al porous metamaterials with triply periodic minimal surfaces (TPMS) for hydrogen generation from Al-water reaction

Porous metamaterials with three-dimensional periodic minimal surfaces provide new inspirations to develop catalytic hydrogen generation materials owing to their excellent mass transfer. Herein, ten kinds of Al porous metamaterials with triply periodic minimal surfaces (TPMS) were prepared by selective laser melting (SLM) for hydrogen generation from Al–H2O reaction. The structures of TPMS type Al materials significantly affect their hydrogen generation performance, and the increased reaction area and liquid flow rate of TPMS structure can effectively improve the galvanical replacement reaction and rapidly form a Ni/Al battery, thus accelerating the hydrogen generation. Compared to conventional aluminum powder, the obtained Diamond type Al porous metamaterial exhibit short induction period, high hydrogen generation yield (81.4 % after 2 h reaction) and high hydrogen generation rate (468 mL-H2·min−1·gAl−1), easy activation of surface passivation film, good structural stability and convenience to recycle the materials after use. This work highlights a simple, low-cost and optional porous structure design strategy that can be applied in the field of safe and efficient catalytic hydrogen generation.

Controlling biofilm transport with porous metamaterials designed with Bayesian learning

Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm transportation properties could significantly improve the rapid utilization of biofilms as engineered living materials for applications in pollution alleviation, material self-healing, energy production, and many more. We combine Bayesian optimization (BO) and individual-based modeling to conduct design optimizations for maximizing different porous materials’ (PM) biofilm transportation capability. We first characterize the acquisition function in BO for designing 2-dimensional porous membranes. We use the expected improvement acquisition function for designing lattice metamaterials (LM) and 3-dimensional porous media (3DPM). We find that BO is 92.89% more efficient than the uniform grid search method for LM and 223.04% more efficient for 3DPM. For all three types of structures, the selected characterization simulation tests are in good agreement with the design spaces approximated with Gaussian process regression. All the extracted optimal designs exhibit better biofilm growth and transportability than unconfined space without substrates. Our comparison study shows that PM stimulates biofilm growth by taking up volumetric space and pushing biofilms’ upward growth, as evidenced by a 20% increase in bacteria cell numbers in unconfined space compared to porous materials, and 128% more bacteria cells in the target growth region for PM-induced biofilm growth compared with unconfined growth. Our work provides deeper insights into the design of substrates to tune biofilm growth, analyzing the optimization process and characterizing the design space, and understanding biophysical mechanisms governing the growth of biofilms.

A computational building block approach towards multiscale architected materials analysis and design with application to hierarchical metal metamaterials

In this study we report a computational approach towards multiscale architected materials analysis and design. A particular challenge in modeling and simulation of materials, and especially the development of hierarchical design approaches, has been to identify ways by which complex multi-level material structures can be effectively modeled. One way to achieve this is to use coarse-graining approaches, where physical relationships can be effectively described with reduced dimensionality. In this paper we report an integrated deep neural network architecture that first learns coarse-grained representations of complex hierarchical microstructure data via a discrete variational autoencoder and then utilizes an attention-based diffusion model solve both forward and inverse problems, including a capacity to solve degenerate design problems. As an application, we demonstrate the method in the analysis and design of hierarchical highly porous metamaterials within the context of nonlinear stress–strain responses to compressive deformation. We validate the mechanical behavior and mechanisms of deformation using embedded-atom molecular dynamics simulations carried out for copper and nickel, showing good agreement with the design objectives.

Colorful Electrochromic Displays with High Visual Quality Based on Porous Metamaterials.

The introduction of metamaterials into electrochromic (EC) displays has recently inspired a great breakthrough in the EC field, as this can offer a variety of new attractive features, from a very wide gamut of colors to very fast switching times. However, such metamaterial-based EC displays still face significant constraints when developing from single electrodes to full devices, because other supportivecomponents in devices, such as counter electrodes and electrolytes, significantly affect light propagation and the subsequent perceived color quality in metamaterial-based EC devices. Herein, a new, cost-effective device design structured around a new type of porous metamaterial is reported to circumvent the critical problem in metamaterial-based EC displays. Owing to its unique design, the metamaterial-based EC device achieves good color quality with no drop in brightness or shift in color chromaticity when compared with a single electrode. Moreover, the porous-metamaterial-based EC device can exhibit non-iridescence and be viewed from a wide range of angles (5°-85°) and has fast switching response (2.4 and 2.5 s for coloration and bleaching, respectively), excellent cycling performance (at least 2000 cycles), and extremely low power consumption (4.0mWcm-2 ).

Flow through porous metamaterials formed by TPMS-based unit cells: Effects of advection

The design of metamaterials based on triply periodic minimal surfaces (TPMS) is currently a very active field of research. An upscaling approach is used here to study the flow in TPMS-based porous media, with focus on the effects of advection. The effective medium permeability, function of the Reynolds number Re of the flow through the pores, is numerically evaluated for varying porosity θ, for six types of TPMS-based structures, namely Gyroid, I-WP, Schwarz P, Split P, Fischer–Koch S, and Neovius. Inertial effects are found to be significant; for instance, the permeability is reduced by 15−50% (according to the surface type) as Re increases from 0 to 50000, when θ=0.98.

Machine learning boosting the discovery of porous metamaterials with an abnormal thermal transport property

Normally, the introduction of porous structures into materials can tune their thermal conductivity, showing great applications in thermal management and thermoelectric energy harvesting. However, the ability of disorder changing the thermal conductivity of porous materials has seldom been explored. In this work, we show that an introduction of disorder into the macroscopic porous materials with a certain porosity can lead to a desired effective thermal conductivity over a large range, where an abnormal enhancement of ∼7.9% and a normal reduction of ∼44% at room temperature are predicted by the machine-learning-optimized algorithm. All of these theoretical calculation results are further verified by our experiments performed in the current work by using the steady-state thermal flux method. Moreover, when these periodic units are artificially connected, a structural anisotropy up to 40 is achieved, which can be further used to adjust the direction of the thermal flux in a well-controlled way. Our work provides an efficient and convenient approach for designing high-performance porous materials with specific thermal conductivity and high structural anisotropy for various applications in thermal management.

Sound absorption performance of helically perforated porous metamaterials at high temperature

A high-temperature theoretical model is established to investigate the sound absorption performance of helically perforated porous metamaterials (HPPM) at high temperature. Finite element simulations are carried out to validate the theoretical model, and good agreements have been achieved. By perforating three-dimensional helical holes in homogeneous porous material, sound waves can enter the porous material matrix more fully through these extended macroscopic helical perforations, thereby obtaining good low-frequency sound absorption at high temperatures. The results show that with the increase of temperature, the impedance matching between the metamaterial and air becomes better, so as to improve the low-frequency sound absorption ability. Compared to homogeneous porous materials, the average absorption coefficient is greatly improved, especially at higher temperatures. The numerical results of sound pressure and energy dissipation at different temperatures show that high-temperature enhances the pressure diffusion effect at off-peak frequencies, resulting in more energy dissipation in high sound pressure regions. The helically perforated porous metamaterials with large helical diameter and low pitch exhibit better sound absorption performance in the low frequency range. The proposed metamaterial can be used for low-frequency sound absorption at high temperature.

Learning the nonlinear dynamics of mechanical metamaterials with graph networks

The dynamics of soft mechanical metamaterials provides opportunities for many exciting engineering applications. Previous works have shown tremendous success in describing the unique nonlinear dynamics of certain types of soft mechanical metamaterials. However, capturing the nonlinear dynamic response of these materials especially those with complex geometries, can be a challenge due to the strong nonlinearity and large computational cost. An efficient and reliable framework to predict the overall response of the metamaterials based on the geometry of their building blocks is not only key to understanding the unique behavior of metamaterials, but also vital to the rational design of such materials. In this work, we propose metamaterial graph network (MGN), a machine learning approach to address this challenge. MGN is based on a graph that represents the lattice-like metamaterial structure. The trained MGN is capable of simulating the dynamics of a metamaterial structure with over 200 by 200 unit cells, a task that is practically impossible for traditional direct numerical simulation using the finite element method. We also verify the accuracy of the proposed MGN against several representative numerical examples. In the first two examples, we show that MGN successfully captures the well-known pattern transformation behavior of porous metamaterials. In the later examples, we consider wave propagation in a dynamic setting and show that MGN produces quantitatively accurate results compared with direct numerical simulation. An additional feature of MGN is that defects/inhomogeneities can be easily incorporated into the metamaterial structure. We expect our method to open a new avenue for the study and modeling of mechanical metamaterials.

Normal incidence sound absorption of an acoustic labyrinthine metal-fibers-based porous metamaterial at high temperature

This work studies the sound absorption performance of acoustic labyrinthine porous metamaterials (ALPM) at high temperature. The proposed metamaterial is constructed by perforating a homogeneous metal-fibers-based porous matrix with folded slits. These macroscopic perforations provide long channels for sound waves to enter the structure, allowing sound waves to enter the porous material due to the pressure diffusion effect, resulting in excellent subwavelength absorption at high temperatures. Based on the double porosity theory and temperature-dependent parameters, we develop a high-temperature theoretical model and validate it by numerical method. The results show that with the increase of temperature, the peak frequency of the ALPM moves to higher frequencies and the half-absorption bandwidth becomes wider. The discussion of sound absorption mechanism based on the pressure distribution results shows that with the increase of temperature, the pressure diffusion effect exists in a wider frequency range, which helps to suppress the propagation of long-wavelength sound waves at high temperature. The study of particle vibration velocity and energy dissipation density shows that the higher the temperature, the stronger the motion of air particles, and more sound energy is dissipated in the porous material, resulting in enhanced sound absorption. The ALPM can be regarded as a combination of homogeneous porous material and space-coiled resonator. Compared to these two components, the ALPM exhibits excellent sound absorption in the frequency range of 200Hz to 600Hz, especially at high temperatures. The proposed porous metamaterials show promising applications in low-frequency sound absorption at high temperature.

An auxetic cellular structure as a universal design for enhanced piezoresistive sensitivity

•A mechanical metamaterial strategy to design piezoresistive sensing material •A universal approach to fabricate auxetic cellular structure with NPR •Pressure sensitivity is markedly improved in the meta-structured design Existing piezoresistive pressure sensors are limited because of the low sensitivity of conventional piezoresistive porous materials, which have positive Poisson’s ratio (PPR) values. Here, we propose a universally applicable strategy for fabricating piezoresistive metamaterials with an auxetic cellular structure, negative Poisson’s ratio (NPR), and enhanced pressure sensitivity. This hyperbolic and re-entrant microstructure enables porous, conductive metamaterials (e.g., titanium carbide [MXene], graphene, carbon nanotubes, silver nanowire, poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) [PEDOT:PSS]) to produce significant macroscopic transverse contraction under longitudinal compression with minimum NPR values below −0.45. This auxetic effect significantly increases the number of cellular wall-to-wall contact points and conductive pathway formations in the porous metamaterials under compression, significantly reducing electrical resistance. Sensitivity is markedly improved in this design compared with the traditional structure. The substantially enhanced mechanical elasticity and durability induced by the NPR effect gives piezoresistive metamaterials excellent sensing stability and reliability even under repeated episodes of significant compressive deformation. Existing piezoresistive pressure sensors are limited because of the low sensitivity of conventional piezoresistive porous materials, which have positive Poisson’s ratio (PPR) values. Here, we propose a universally applicable strategy for fabricating piezoresistive metamaterials with an auxetic cellular structure, negative Poisson’s ratio (NPR), and enhanced pressure sensitivity. This hyperbolic and re-entrant microstructure enables porous, conductive metamaterials (e.g., titanium carbide [MXene], graphene, carbon nanotubes, silver nanowire, poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) [PEDOT:PSS]) to produce significant macroscopic transverse contraction under longitudinal compression with minimum NPR values below −0.45. This auxetic effect significantly increases the number of cellular wall-to-wall contact points and conductive pathway formations in the porous metamaterials under compression, significantly reducing electrical resistance. Sensitivity is markedly improved in this design compared with the traditional structure. The substantially enhanced mechanical elasticity and durability induced by the NPR effect gives piezoresistive metamaterials excellent sensing stability and reliability even under repeated episodes of significant compressive deformation.

Build quality evaluation of a series of lattice structures of AlSi10Mg using X-ray tomography

Additive manufacturing is a manufacturing technology that has grown from prototyping applications to the production of end-use parts in various industries. It is today possible to produce complex-shaped parts such as cellular or lattice structures in various popular metal alloys relevant to engineering applications. These intentionally designed porous metamaterials allow unique properties that have applications in next-generation aerospace components and medical implants, amongst others. The wide adoption of lattice structures has been in part held back by quality concerns in the manufactured material, including porosity, surface roughness, microcracks, residual stress and microstructural nuances. These issues are generic to all AM materials, but at the scale of lattice structures they have a stronger influence on mechanical and other performance; and are more critical to the successful manufacturing and final application. While lattices have widely studied in the last few years, few comparisons of build quality exist for different designs or architectures. In this work, using the laser powder bed fusion process with AlSi10Mg alloy, we report an extensive and detailed characterization of the build quality of a range of different cellular architectures, utilizing X-ray computed tomography (CT). Some key conclusions are made that can be used to inform future designs, enhancing build success and design confidence. Some recommendations are made regarding the use of CT for lattice structure characterization.

Surface texture modulation via buckling in porous inclined mechanical metamaterials

Porous materials with well-defined periodicity are commonly encountered in biological and synthetic structures and exhibit a wide range of behaviors, ranging from negative Poisson’s ratios, to high energy absorption and acoustic damping. Recently, the response of these systems has been shown to be enhanced by mechanical instabilities that lead to sudden and reversible geometric transformations. Although buckling induces planar transformations in most of 2D porous metamaterials, here we describe the emergence of 3D morphologies triggered by mechanical instabilities in an elastomeric block with tilted cylindrical holes. As a proof of concept, we demonstrate that these structures can be leveraged to tune surface properties including friction and light reflection, thus providing a new experimental platform for investigating deformation-dependent dynamics for tribological and optical applications.