Chiara Daraio

Metamaterials can achieve exceptional functionality through careful engineering of their mesoscale structure. Although appropriately introduced irregularities can be advantageous, current approaches largely conform to regular structures to preserve tractability. Here, we contend that network theory, enriched with geometry and physics, provides a natural framework for designing metamaterials with controlled irregularities at relevant scales, thereby enabling the discovery of new property-enhancing structures. We examine how this augmented network theory can facilitate the creation of irregular metamaterials with enhanced or novel properties and how metamaterial research, in turn, is opening new directions in network science. Supported by machine learning and advanced self-assembly, the emerging field of irregular metamaterial networks is poised to transform inverse design and scalable manufacturing of novel materials.

Metamaterials can achieve exceptional functionality through careful engineering of their mesoscale structure. Although appropriately introduced irregularities can be advantageous, current approaches largely conform to regular structures to preserve tractability. Here, we contend that network theory, enriched with geometry and physics, provides a natural framework for designing metamaterials with controlled irregularities at relevant scales, thereby enabling the discovery of new property-enhancing structures. We examine how this augmented network theory can facilitate the creation of irregular metamaterials with enhanced or novel properties and how metamaterial research, in turn, is opening new directions in network science. Supported by machine learning and advanced self-assembly, the emerging field of irregular metamaterial networks is poised to transform inverse design and scalable manufacturing of novel materials.

Metamaterials can achieve exceptional functionality through careful engineering of their mesoscale structure. Although appropriately introduced irregularities can be advantageous, current approaches largely conform to regular structures to preserve tractability. Here, we contend that network theory, enriched with geometry and physics, provides a natural framework for designing metamaterials with controlled irregularities at relevant scales, thereby enabling the discovery of new property-enhancing structures. We examine how this augmented network theory can facilitate the creation of irregular metamaterials with enhanced or novel properties and how metamaterial research, in turn, is opening new directions in network science. Supported by machine learning and advanced self-assembly, the emerging field of irregular metamaterial networks is poised to transform inverse design and scalable manufacturing of novel materials.

We study the nonlinear wave dynamics of one-dimensional chains of polycatenated rings. These interlocked structures support amplitude-dependent nonlinear wave propagation driven by tensile activation and internal structural flexibility, unlike traditional granular crystals. Through dynamic impact experiments, finite-element modeling, and discrete-particle simulations of vertical chains pretensioned by gravity, we observe and explain nonlinear waves characterized by a compact leading wavefront followed by persistent trailing oscillations, which arise from energy partitioning into the rings' internal bending modes. Further, we demonstrate that the system's nonlinearity is not a fixed material constant. By altering the rings' geometric aspect ratio and contact angles, we can tune the effective contact exponent and the amplitude scaling of the wave speed. This work builds upon nonlinear wave propagation in classical granular crystals and establishes polycatenated systems as a highly tunable and designable platform to study and control nonlinear dynamics.

We study the nonlinear wave dynamics of one-dimensional chains of polycatenated rings. These interlocked structures support amplitude-dependent nonlinear wave propagation driven by tensile activation and internal structural flexibility, unlike traditional granular crystals. Through dynamic impact experiments, finite-element modeling, and discrete-particle simulations of vertical chains pretensioned by gravity, we observe and explain nonlinear waves characterized by a compact leading wavefront followed by persistent trailing oscillations, which arise from energy partitioning into the rings' internal bending modes. Further, we demonstrate that the system's nonlinearity is not a fixed material constant. By altering the rings' geometric aspect ratio and contact angles, we can tune the effective contact exponent and the amplitude scaling of the wave speed. This work builds upon nonlinear wave propagation in classical granular crystals and establishes polycatenated systems as a highly tunable and designable platform to study and control nonlinear dynamics.

Mobile robots are increasingly deployed in diverse settings, ranging from logistic and household applications to ecological monitoring and operation in extreme environments. In these contexts, robots must traverse diverse terrains, yet most existing designs rely on fixed morphologies that limit efficiency across domains. Biomimetic solutions emulate natural forms but cannot fully exploit engineered mechanisms, while active adaptive architectures typically require complex electronics and incur substantial energy costs. Inspired by amphibians and reptiles, we developed AdaptBot, an untethered adaptive soft robot that integrates rigid machinery with responsive soft materials to achieve passive reconfiguration for amphibious locomotion. AdaptBot employs a single bioinspired photothermal artificial muscle (PAM) to power multiple gaits by light, a fast and large swelling hydrogel (FLASH) to drive passive fin deployment in water, and a ratcheting transmission to convert reciprocating PAM motion into forward locomotion. These elements enable multimodal performance-including rolling, load-carrying, climbing, and paddling-under wireless control across terrestrial, aquatic, and transitional environments. Remarkably, following fin deployment, AdaptBot's swimming speed increased by 780%, demonstrating that passive adaptation is an effective strategy to enhance locomotor efficiency in robots operating in unstructured and dynamic environments.

Thermography via distributed sensors is essential in applications ranging from structural health monitoring to medical diagnostics, where localized thermal anomalies reveal subsurface damage or disease. Unlike dense sensor arrays, sparse sensor networks offer advantages such as gas exchange, mechanical conformability, and scalability. However, interpolated temperature maps are sensitive to sensor distribution and interpolation strategy. Many prior methods rely on application-specific data such as heat source location or variograms and lack a generalizable framework for comparing sensor density across scenarios. In this study, we pair a meshless radial basis function-based algorithm, the Adaptive Meshless Approximation (AMA), with a normalized region-of-interest sensor density metric (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N_ROI</i>) to predict interpolation error without requiring prior knowledge of the ROI. AMA yields temperature fields consistent with diffusive physics, enabling realistic reconstruction. We evaluate performance across gridded and random sampling, multiple phantom geometries, and 3D surfaces. Results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N_ROI</i> strongly correlates with interpolation error across conditions, providing a scalable framework for designing low-density thermal sensor networks in diverse applications.

Irregular architected materials offer a wide design space of mechanical properties beyond that of typical periodic architected materials. However, these irregular materials are often completely stochastic, offering little control over the structure-to-property relationships. Here, we show that intentional design of irregular materials, using topology and geometry, leads to control of the mechanical response. We demonstrate this experimentally using additively manufactured two-phase polymer composites, which we generate using a hexagonal virtual growth algorithm (hexa-VGA). The hexa-VGA tessellates a finite set of hexagonal tiles into an irregular network according to coordination number, defined as the average number of connections per network node. In contrast to prior work on the topic of network reinforced composites, the hexa-VGA allows the design of reinforcing architectures characterized by a broader design space, having a coordination number that spans from one to six, representing a purely monodisperse and regular network. The designs generated by the hexa-VGA are then additively manufactured into two-phase materials, with the network being made of a stiff reinforcing phase, while the space enclosed by the network is filled with a soft elastomeric matrix. Through plate tension tests, we show the dependence between the coordination number and the mechanical properties of the polymer composites, and we highlight design pathways to control temporal variations in the fracture response and improve damage tolerance. • We investigate periodic and irregular network reinforced polymer composites • Networks are generated using a virtual growth algorithm • Average network coordination controls the mechanical response • Network irregularity leads to diffuse fracture and improved damage tolerance

This repository contains supplemental data for the article &lt;p&gt; &lt;strong&gt;"Robust inverse material design with physical guarantees using the Voigt-Reuss Net",&lt;/strong&gt; &lt;/p&gt; accepted for publication in the International Journal for Numerical Methods in Engineering by Sanath Keshav and Felix Fritzen. &lt;p&gt; The data in this DaRUS repository complements the accompanying open-source implementation of the &lt;a href="https://github.com/DataAnalyticsEngineering/VoigtReussNet"&gt;Voigt-Reuss net&lt;/a&gt; and enables reproducibility of the numerical results in the manuscript [1]. The datasets are generated by solving periodic small-strain linear elasticity homogenization problems for a large number of biphasic periodic RVEs. Effective stiffness tensors were computed with our open-source implementation of the Fourier-Accelerated Nodal Solvers (&lt;a href="https://github.com/DataAnalyticsEngineering/FANS"&gt;FANS&lt;/a&gt;) [4] on voxelized microstructures under periodic boundary conditions. &lt;/p&gt; &lt;p&gt; The repository contains two labeled datasets: &lt;/p&gt; &lt;p&gt; &lt;strong&gt;(i) 3D linear elasticity.&lt;/strong&gt;&lt;br&gt; We use an open 3D microstructure dataset (90,000 stochastic microstructures, resolution 192x192x192), together with 236 scalar, image-derived morphological descriptors per microstructure [2]. For each microstructure, we sample three non-dimensional parameters that encode the bulk and shear moduli of the two phases, and compute the corresponding effective stiffness tensor (symmetric positive definite, 6x6 in Mandel notation). Overall, the dataset contains ~1.18 million microstructure-material combinations and includes the train/validation/test splits used in the paper. &lt;/p&gt; &lt;p&gt; &lt;strong&gt;(ii) 2D plane-strain elasticity.&lt;/strong&gt;&lt;br&gt; We use periodic microstructures obtained from thresholded trigonometric fields parameterized by an amplitude matrix A and a threshold [3]. For each sample, we provide the generator parameters, the rendered microstructure image, and the homogenized plane-strain stiffness tensor (symmetric positive definite, 3x3 in Voigt notation). The same split definitions and metadata used for training and evaluation are included to reproduce the forward-prediction comparisons and inverse-design experiments. &lt;/p&gt; &lt;p&gt; Further details on file formats, naming conventions, and the exact contents of each file are provided in &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=484328"&gt;README.md&lt;/a&gt;. &lt;/p&gt; [1] Keshav, S., and Fritzen, F. (2026). Robust inverse material design with physical guarantees using the Voigt-Reuss Net, International Journal for Numerical Methods in Engineering. &lt;a href="https://doi.org/10.1002/nme.70296 "&gt;https://doi.org/10.1002/nme.70296 &lt;/a&gt;&lt;br&gt;&lt;br&gt; [2] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. &lt;a href="https://doi.org/10.5281/zenodo.4047774"&gt;https://doi.org/10.5281/zenodo.4047774&lt;/a&gt; &lt;br&gt;&lt;br&gt; [3] Boddapati, J., &amp; Daraio, C. (2024). Planar structured materials with extreme elastic anisotropy. Materials &amp;amp; Design, 246, 113348. &lt;a href="https://doi.org/10.1016/j.matdes.2024.113348"&gt;https://doi.org/10.1016/j.matdes.2024.113348&lt;/a&gt; &lt;br&gt;&lt;br&gt; [4] Leuschner, M., and Fritzen, F. (2018). Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems. Computational Mechanics, 62(3), 359-392. &lt;a href="https://doi.org/10.1007/s00466-017-1501-5 "&gt;https://doi.org/10.1007/s00466-017-1501-5 &lt;/a&gt; &lt;br&gt;&lt;br&gt;

ABSTRACT Fabricating high‐performance, binder‐free biomaterials from microalgae grown during wastewater treatment is an opportunity in sustainable materials. However, the impact of strain morphology and mechanical preprocessing on material properties remains largely uncharacterized. This study investigates binder‐free biomaterials fabricated from wastewater‐grown, filamentous Tribonema minus and food‐grade, unicellular Chlorella vulgaris . The impact of three mechanical comminution methods (ball mill, mortar‐and‐pestle, and speed mixer) on the mechanical properties is evaluated. The results demonstrate that feedstock morphology and processing are critical, interacting factors. Under gentle comminution (mortar‐and‐pestle), filamentous Tribonema biomaterials exhibit significantly higher flexural modulus and strength than unicellular Chlorella . Conversely, high‐shear speed mixing diminishes Tribonema 's structural advantage while enhancing Chlorella 's particle packing, leading to a convergence in mechanical properties. All final biomaterials exhibit near‐hydrophobic surfaces (contact angles &gt; 85°). This research validates that non‐food‐competing wastewater algae can be transformed into high‐performance biomaterials, yielding materials with densities of ≈1.0–1.1 g/cm 3 and flexural moduli ranging from ≈0.3 to 1.0 GPa.

Efficient transmission of elastic waves across interfaces is central to several applications, including medical imaging, seismic isolation, and transducer design. Interfaces with abrupt changes in the material properties significantly impede wave transmission, leading to reflections. This limitation, known as impedance mismatch, becomes even more prominent for mode conversion between different wave types due to polarization mismatch. In this study, we investigate a mechanism employing two-dimensional functionally graded anisotropic metamaterials to facilitate longitudinal--shear mode conversion as waves propagate from a stiff to a compliant medium. By embedding density and anisotropic shape gradients within the functionally graded metamaterial, polarization-induced impedance mismatch is mitigated and efficient mode conversion is enabled. We use unit cell dispersion analysis to tailor the frequency range for mode conversion through gradation in the dispersion behavior and coupling between modes. Using frequency-domain finite element analysis, we demonstrate broadband mode conversion across interfaces with large stiffness contrast operating in the 1--10 kHz range. We then experimentally validate and quantify mode conversion through full-field velocity measurements on an additively manufactured specimen. We further apply the methodology to design a device capable of converting radial--tangential wave modes.

Efficient transmission of elastic waves across interfaces is central to several applications, including medical imaging, seismic isolation, and transducer design. Interfaces with abrupt changes in the material properties significantly impede wave transmission, leading to reflections. This limitation, known as impedance mismatch, becomes even more prominent for mode conversion between different wave types due to polarization mismatch. In this study, we investigate a mechanism employing two-dimensional functionally graded anisotropic metamaterials to facilitate longitudinal--shear mode conversion as waves propagate from a stiff to a compliant medium. By embedding density and anisotropic shape gradients within the functionally graded metamaterial, polarization-induced impedance mismatch is mitigated and efficient mode conversion is enabled. We use unit cell dispersion analysis to tailor the frequency range for mode conversion through gradation in the dispersion behavior and coupling between modes. Using frequency-domain finite element analysis, we demonstrate broadband mode conversion across interfaces with large stiffness contrast operating in the 1--10 kHz range. We then experimentally validate and quantify mode conversion through full-field velocity measurements on an additively manufactured specimen. We further apply the methodology to design a device capable of converting radial--tangential wave modes.

Wearable electronics are essential for health monitoring, haptic feedback, and human-computer interaction. However, maintaining stable contact with skin remains challenging due to its softness, roughness, and variability across body sites. Conventional solutions such as grounding bands or adhesive tapes often suffer from contact loss and limited repeatability. Suction-based adhesives offer a promising alternative by generating negative pressure without tight compression or chemical adhesives, but most designs assume rigid surfaces and overlook skin mechanics. Inspired by traditional cupping therapies, we introduce a suction adhesive system that attaches to diverse skin regions through elastic deformation and recovery. Using analytical modeling, simulations, and experiments, we develop a mechanics-based framework that links suction performance to cup geometry, substrate compliance, and interfacial adhesion. We show that wide, flat cups are effective on rigid surfaces but fail on soft substrates, while narrow, tall cups maintain recoverable volume and strong adhesion. To improve sealing on rough, dry skin, we introduce a soft, tacky interfacial layer guided by contact mechanics. Our design principles enable robust attachment of motion sensors, haptic actuators, and electrophysiological electrodes across diverse anatomical regions, offering a versatile, skin-friendly platform for next-generation wearable electronics.

High Resolution Image Download MS PowerPoint Slide Spirulina platensis is a promising bioresource for developing structural materials, offering a renewable alternative to conventional polymers due to its rapid growth and characteristic helical microstructure. While its biochemical properties have been widely studied, the role of cellular morphology in determining macroscale mechanical performance remains underexplored. In this work, we examine how maintaining versus disrupting Spirulina ’s native trichome structure and cell walls impacts the cohesion, rheology, and mechanical behavior of 3D-printed biomaterials. Using hydroxyethyl cellulose (HEC) as a binder, we developed two classes of bioinks: trichome biocomposites, based on freeze-dried Spirulina trichomes, and lysed biocomposites, formed from thermally lysed Spirulina cells. Differential scanning calorimetry revealed stronger molecular interactions between lysed cells and HEC, while trichomes contributed instead via physical interlocking and structural integrity of the cell wall. Despite weaker molecular interactions, trichome-based biocomposite bioinks exhibited higher viscosity, improved printability, and higher rheological yield stress by up to 499%. Upon dehydration, trichome biocomposites showed lower shrinkage and higher mechanical performance under compression, with normalized compressive modulus and yield strength significantly exceeding that of lysed biocomposites (by up to 107% and 108%, respectively). These effects are attributed to mechanical interlocking and enhanced stress transfer through intact cell walls. Our findings demonstrate that preserving biological microstructure may enable improved material cohesion and function, offering design principles for scalable, sustainable biofabrication of algae-based structural materials.

Disordered microstructures are key to the distinct multifunctional properties of many natural materials. However, understanding the relationship between their microstructures and physical functions remains formidable, hindering engineering applications. Here, we introduce a physics-guided, self-supervised artificial intelligence (AI) framework called generative networks for disordered metamaterials (GNDM), trained on a progressively expanding dataset starting from a few initial samples. We integrate a formula writing module in the training process of neural networks to enforce the identification of the most selective set of hidden geometric invariants that dictate bulk properties. By inversely solving the formulae, GNDM manipulate disordered geometric features to extrapolate property space and design previously unknown structures via its generator module, validated by experiments. GNDM offers an all-in-one AI framework that closes the loop of feature extraction, property prediction, formula writing, and inverse design, unraveling the regulative role of disorder, a critical challenge in the study of metamaterials with complex microstructures.

The mechanical behavior of composite materials is significantly influenced by their structure and constituent materials. One emerging class of composite materials is irregular network reinforced composites (NRC's), whose reinforcing phase is generated by a stochastic algorithm. Although design of the reinforcing phase network offers tailorable control over both the global mechanical properties, like stiffness and strength, and the local properties, like fracture nucleation and propagation, the fracture properties of irregular NRC's have not yet been fully characterized. This is because both the irregular reinforcing structure and choice of matrix phase material significantly affect the fracture response, often resulting in diffuse damage, associated with multiple crack nucleation locations. Here, we propose irregular polymer NRC's whose matrix phase has a similar stiffness but half the strength of the reinforcing phase, which allows the structure of the reinforcing phase to control the fracture response, while still forming and maintaining a primary crack. Across a range of network coordination numbers, we obtain J-integral and R-curve measurements, and we determine that low coordination polymer NRC's primarily dissipate fracture energy through plastic zone formation, while high coordination polymer NRC's primarily dissipate energy through crack extension. Finally, we determine that there are two critical length scales to characterize and tailor the fracture response of the composites across the coordination numbers: (i) the size of the plastic zone, and (ii) the size and geometry of the structural features, defined as the areas enclosed by the reinforcing network.

Graphene is a material with potential applications in electric, thermal, and mechanical fields, and has seen significant advancements in growth methods that facilitate large-scale production. However, defects during growth and transfer to other substrates can compromise the integrity and strength of graphene. Surprisingly, the literature suggests that, in certain cases, defects can enhance or, at most, not affect the mechanical performance of graphene. Further research is necessary to explore how defects interact within graphene structure and affect its properties, especially in large-area samples. In this study, we investigate the interaction between two preexisting cracks and their effect on the mechanical properties of graphene using molecular dynamics simulations. The behavior of zigzag and armchair graphene structures with cracks separated by distances ($W_\text{gap}$) is analyzed under tensile loading. The findings reveal that crack coalescence, defined as the formation of a new crack from two existing crack tips, occurs for lower values of the distance between cracks, $W_\text{gap}$, resulting in a decline in the strength of structures. As $W_\text{gap}$ increases, the stress-strain curves shift upward, with the peak stress rising in the absence of crack coalescence. The effective stress intensity factor formulated in this study exhibits a clear upward trend with increasing $W_\text{gap}$. Furthermore, an increase in $W_\text{gap}$ induces a transition in fracture behavior from crack coalescence to independent propagation with intercrack undulation. This shift in fracture behavior demonstrates a brittle-to-ductile transition, as evidenced by increased energy absorption and delayed failure. A design guideline for the initial crack geometry is suggested by correlating peak stress with the $W_\text{gap}$, within a certain range.

Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface. Our method works by extracting a dispersion relation from the video and then solving a physics-based optimization problem to find the best-fitting thickness and stiffness parameters. We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements. Our technique provides a proof-of-concept for at-home health monitoring of medically-informative tissue properties, and it is further applicable to fields such as human-computer interaction.

The use of periodic materials for wave control and signal processing has been a focus of intensive research over the past two decades and continues to garner significant attention. Common signal processing mechanisms like switches and rectifiers often depend on magnetic fields and/or logic gates for their activation. We propose a metamaterial that enables the control of mechanical waves—surface acoustic waves—through an ON–OFF mechanism that switches the propagation of the waves through a tunable platform of elastic beams. In the OFF configuration, the beams remain in their undeformed state and resonate at a specific frequency range, creating a bandgap that stops wave propagation. Conversely, in the ON configuration, the beams undergo buckling, redistributing the vibration energy across multiple modes and eliminating the bandgap, thus allowing wave propagation. Analytical and numerical findings demonstrate the significant potential of this mechanism for controlling wave propagation in nonlinear periodic materials. This switching mechanism relies purely on mechanical processes, thereby eliminating the need for external fields.

Core body temperature (CBT) is a vital phenotype that provides information on an individual’s health and metabolic activity. It correlates with a variety of physical and mental conditions and requires monitoring when an individual is under environmental or medical duress. Current sensing materials lack the desired temperature sensitivity for fabricating ultrathin, wearable CBT sensors with the accuracy needed for medical applications. Here, the realization of an ultrathin CBT sensor based on dual heat flux (DHF) thermometry is reported, uniquely enabled by the application of a novel class of synthetic polymers. By optimizing the chemical composition, the material’s properties were tuned to achieve an optimal temperature response. Furthermore, the measurement error in the device was evaluated using finite element (FE) analysis. Building on this knowledge, this highly temperature-sensitive polymer was embedded into an ultrathin DHF sensor, and its sensitivity and repeatability were characterized using an anthropomorphic phantom model. The results presented in this work pave the way for first-in-class wearable and accurate DHF sensors, allowing continuous CBT monitoring.

The mechanical behavior of composite materials is significantly influenced by their structure and constituent materials. One emerging class of composite materials is irregular network reinforced composites (NRC’s), whose reinforcing phase is generated by a stochastic algorithm. Although design of the reinforcing phase network offers tailorable control over both the global mechanical properties, like stiffness and strength, and the local properties, like fracture nucleation and propagation, the fracture properties of irregular NRC’s have not yet been fully characterized. This is because both the irregular reinforcing structure and choice of matrix phase material significantly affect the fracture response, often resulting in diffuse damage, associated with multiple crack nucleation locations. Here, we propose irregular polymer NRC’s whose matrix phase has a similar stiffness but half the strength of the reinforcing phase, which allows the structure of the reinforcing phase to control the fracture response, while still forming and maintaining a primary crack. Across a range of network coordination numbers, we obtain J-integral and R-curve measurements, and we determine that low coordination polymer NRC’s primarily dissipate fracture energy through plastic zone formation, while high coordination polymer NRC’s primarily dissipate energy through crack extension. Finally, we determine that there are two critical length scales to characterize and tailor the fracture response of the composites across the coordination numbers: (i) the size of the plastic zone, and (ii) the size and geometry of the structural features, defined as the areas enclosed by the reinforcing network.

Wearable electronics are emerging as essential tools for health monitoring, haptic feedback, and human-computer interactions. While stable contact at the device-body interface is critical for these applications, it remains challenging due to the skin's softness, roughness, and mechanical variability. Existing methods, such as grounding structures or adhesive tapes, often suffer from contact loss, limited repeatability, and restrictions on the types of electronics they can support. Suction-based adhesives offer a promising alternative by generating negative pressure without requiring tight bands or chemical adhesives. However, most existing cup designs rely on rigid-surface assumptions and overlook mechanical interactions between suction cups and skin. Inspired by traditional cupping therapies, we present a suction-based adhesive system that attaches through elastic deformation and recovery. Using analytical modeling, numerical simulations, and experiments, we present a mechanics-based framework showing how suction performance depends on cup geometry, substrate compliance, and interfacial adhesion. We show that cup geometry should be tailored to substrate stiffness. Wide, flat suction cups perform well on rigid surfaces but fail on soft ones like skin due to substrate intrusion into the chamber. Narrow and tall domes better preserve recoverable volume and generate stronger suction. To improve sealing on rough, dry skin, we introduce a soft, tacky interfacial layer informed by a contact mechanics model. Using our design principles for skin suction adhesives, we demonstrate secure attachment of rigid and flexible components including motion sensors, haptic actuators, and electrophysiological electrodes across diverse anatomical regions. These findings provide a fundamental basis for designing the next generation of skin-friendly adhesives for wearable electronics.

We study the dynamic behavior of periodic multilayered structures composed of compliant freestanding vertically aligned carbon nanotube (VACNT) arrays alternating with rigid metallic inter-layers, subjected to small transient excitations. The presence of intrinsically nonlinear effects in VACNTs arrays enable us to create materials and structures with tunable properties in which, for example, the stiffness can be tuned over a large range. As a result, the dynamic stress propagation properties in these materials can be tailored to specific applications.

We performed experiments on the collisions of micrometer scale (200-300 mm) stainless steel (316 and 440 C) particles and studied the interaction properties and the coefficient of restitution. We used a novel experimental apparatus to enable non-contact measurements based on laser excitations/detection and high-speed photography. The colliding particles were aligned in v-shaped grooves on a silicon wafer, fabricated using anisotropic etching techniques. We used high-power lasers to excite one of the particles (striker), and to control the impact velocity with high repeatability. The motion of the striker particle was triggered by partial laser ablation of its surface. The displacements of the particles involved in the collision were recorded with a high-speed camera mounted on an optical microscope. The particle velocities were obtained from the recorded images using digital image correlation. We calibrated the setup by tracking the dynamic excitation of single particles, and tested collisions between two particles. This study introduces a new experimental approach to understand the fundamental dynamic response of micro-particle collisions, and to test the limits of validity of the Hertzian interaction law.

Crystal Structure Prediction (CSP) of molecular crystals plays a central role in applications, such as pharmaceuticals and organic electronics. CSP is challenging and computationally expensive due to the need to explore a large search space with sufficient accuracy to capture energy differences of a few kJ/mol between polymorphs. Dispersion-inclusive density functional theory (DFT) provides the required accuracy but its computational cost is impractical for a large number of putative structures. We introduce FastCSP, an open-source, high-throughput CSP workflow based on machine learning interatomic potentials (MLIPs). FastCSP combines random structure generation using Genarris 3.0 with geometry relaxation and free energy calculations powered entirely by the Universal Model for Atoms (UMA) MLIP. We benchmark FastCSP on a curated set of 28 mostly rigid molecules, demonstrating that our workflow consistently generates known experimental structures and ranks them within 5 kJ/mol per molecule of the global minimum. Our results demonstrate that universal MLIPs can be used across diverse compounds without requiring system-specific tuning. Moreover, the speed and accuracy afforded by UMA eliminate the need for classical force fields in the early stages of CSP and for final re-ranking with DFT. The open-source release of the entire FastCSP workflow significantly lowers the barrier to accessing CSP. CSP results for a single system can be obtained within hours on tens of modern GPUs, making high-throughput crystal structure prediction feasible for a broad range of scientific applications.

Topological physics has been driving exciting progress in the area of condensed matter physics, with findings that have recently spilled over into the field of metamaterials research inspiring the design of structured materials that can govern in new ways the flow of light and sound. While so far these advances have been driven by fundamental curiosity-driven explorations, without a focused interest on their technological implications, opportunities to translate these findings into applied research have started to emerge, in particular in the context of sound control. Our team has been leading a highly collaborative research effort on advancing the field of topological acoustics, dubbed ‘New Frontiers of Sound’ and connecting it to technological opportunities for computing, communications, energy and sensing. In this comment, we outline our vision towards the future of topological sound, and its translation towards industry-relevant functionalities and operations based on extreme control of acoustic and phononic waves.

Interlocked and polycatenated material systems, consisting of discrete, nonconvex particles linked to their nearest neighbors, such as chainmail fabrics, have been shown to undergo a jamming transition that increases their rigidity under boundary compression. This rigidity transition is associated with an increase in contact number between particles. In architected materials, rigidity is described by theories such as the Maxwell criterion. In this Letter, we propose a rigidity theory for a type of interlocked material system: the torus knot tessellation. Torus knot tessellations are structured fabrics composed of particles shaped as torus knots. In these fabrics, we theoretically demonstrate that in-plane rigidity is governed by a modified Maxwell criterion, while out-of-plane rigidity is governed by a crease line criterion. These theories provide a framework for the design of rigidity of these fabrics.

Natural materials often feature a combination of soft and stiff phases, arranged to achieve excellent mechanical properties, such as high strength and toughness. Many natural materials have even independently evolved to have similar structures to obtain these properties. For example, interlocking structures are observed in many strong and tough natural materials, across a wide range of length scales. Inspired by these materials, we present a class of two-phase composites with controllable interlocking. The composites feature tessellations of stiff particles connected by a soft matrix and we control the degree of interlocking through irregularity of particle size, geometry and arrangement. We generate the composites through stochastic network growth, using an algorithm which connects a hexagonal grid of nodes according to a coordination number, defined as the average number of connections per node. The generated network forms the soft matrix phase of the composites, while the areas enclosed by the network form the stiff reinforcing particles. At low coordination, composites feature highly polydisperse particles with irregular geometries, which are arranged non-periodically. In response to loading, these particles interlock with each other and primarily rotate and deform to accommodate non-uniform kinematic constraints from adjacent particles. In contrast, higher coordination composites feature more monodisperse particles with uniform geometries, which collectively slide. We then show how to control the degree of interlocking as a function of coordination number alone, demonstrating how irregularity facilitates controllability.

Acoustic metasurfaces are two-dimensional architected materials designed to enable non-trivial control of waves, with a thickness that is either thinner than or comparable to the wavelength. However, most metasurfaces today have a fixed geometry and lack the ability to tune acoustic waves on command. This limits their ability to perform multiple functions, such as beam steering and dynamic focusing. This study introduces inflatable acoustic metasurface (IAM) lenses that enable tunable focusing. The IAMs feature two-dimensional diffractive focusing patterns embedded in a membrane that can be inflated nonplanarly through hydraulic control. It is experimentally demonstrated that inflation allows continuous focal length adjustment from -2.49λ to +3.17λ. To characterize the lens performance, changes in focal characteristics, including peak pressure, full width at half-maximum, and full length at half-maximum, are tracked at different levels of inflation. Furthermore, it is shown that IAMs can correct aberrations that occur as the angle of incidence increases in conventional planar lenses. To validate this, IAMs were tested in a concave configuration at a 20° oblique incidence angle. The results of this study may be applicable to fields requiring continuous and real-time response in tunable focusing, including acoustic imaging and communication, ultrasound surgery, and neuromodulation.

In the field of metamaterial research, irregular structures offer a novel and less conventional approach compared to traditional periodic designs. Designing irregular metamaterials is challenging when it comes to ensuring interconnectivity, which is essential for manufacturability. This study introduces an innovative framework for generating irregular metamaterials using graph algorithms, ensuring connectivity and adapt-ability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of design representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of property magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiffness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. The framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e

Two-Photon-Lithography (TPL) is a powerful nano-3D printing technique known for leveraging non-linear absorption to enable sub-micron printing resolution. However, the throughput of TPL is limited owing to its single laser spot scanning mechanism and the limited field-of-view of conventional microscope objectives. We present a novel parallelized TPL platform that replaces the single high-NA objective with a large array of high-NA, polymer immersion metalenses. Independent control over the focusing intensity from each metalens is achieved using a Spatial Light Modulator (SLM) to modulate the intensity of each metalens focusing spot, enabling the large scale writing of periodic and aperiodic patterns with time scales and stitching errors that exceed the capabilities of conventional platforms.

Abstract Structured materials can be engineered to support polarized waves generated by the rotation of the wave's displacement field, leading to spin angular momentum (SAM). This property can be leveraged to control the propagation of sound or vibrations through momentum locking or to induce nonreciprocal propagation via phonon‐magnon coupling. However, the physical realization of elastic metamaterials that support SAM is a challenge. Discrete lattices can be used to realize mechanical systems that support SAM associated with the rotational motion of particles as the waves propagate. Here, a discrete elastic system is demonstrated, consisting of repulsive magnets mounted on cantilevers, which exhibit elastic spin waves arising from the coupling between the longitudinal and transverse motion of the magnets. It is shown that the direct observation of spin variation can be employed to detect lattice defects. The lattice also enables unidirectional wave propagation and can transmit waves with a negative group velocity due to nonlocal interactions.

Architected materials derive their properties from the geometric arrangement of their internal structural elements. Their designs rely on continuous networks of members to control the global mechanical behavior of the bulk. In this study, we introduce a class of materials that consist of discrete concatenated rings or cage particles interlocked in three-dimensional networks, forming polycatenated architected materials (PAMs). We propose a general design framework that translates arbitrary crystalline networks into particle concatenations and geometries. In response to small external loads, PAMs behave like non-Newtonian fluids, showing both shear-thinning and shear-thickening responses, which can be controlled by their catenation topologies. At larger strains, PAMs behave like lattices and foams, with a nonlinear stress-strain relation. At microscale, we demonstrate that PAMs can change their shapes in response to applied electrostatic charges. The distinctive properties of PAMs pave the path for developing stimuli-responsive materials, energy-absorbing systems, and morphing architectures.

Extrusion 3D-printing of biopolymers and natural fiber-based biocomposites enables the fabrication of complex structures, ranging from implants' scaffolds to eco-friendly structural materials. However, conventional polymer extrusion requires high energy consumption to reduce viscosity, and natural fiber reinforcement often requires harsh chemical treatments to improve adhesion. We address these challenges by introducing a sustainable framework to fabricate natural biocomposites using Chlorella vulgaris microalgae as the matrix. Through bioink optimization and process refinement, we produced lightweight, multifunctional materials with hierarchical architectures. Infrared spectroscopy analysis reveals that hydrogen bonding plays a critical role in the binding and reinforcement of Chlorella cells by hydroxyethyl cellulose (HEC). As water content decreases, the hydrogen bonding network evolves from water-mediated interactions to direct hydrogen bonds between HEC and Chlorella, enhancing the mechanical properties. A controlled dehydration process maintains continuous microalgae morphology, preventing cracking. The resulting biocomposites exhibit a bending stiffness of 1.6 GPa and isotropic heat transfer and thermal conductivity of 0.10 W/mK at room temperature, demonstrating effective thermal insulation. These characteristics make Chlorella biocomposites promising candidates for applications requiring both structural performance and thermal insulation, offering a sustainable alternative to conventional materials in response to growing environmental demands.