Kevin T. Turner

Abstract The failure of mechanical metamaterials is a function of the interplay between the properties of the base material and the microstructural geometry. Stochastic failure properties of the base material and disordered microstructural geometries can contribute to variations in the global failure mechanics that are not captured in traditional analyses of ordered, deterministic architected materials. We present a probabilistic framework that couples stochastic material failure and geometric disorder to predict failure in lattice mechanical metamaterials. These predictions are verified through finite element analysis, which confirms that disorder and stochasticity affect both the mean and variance of the damage initiation load in a lattice, with average failure loads being generally reduced and variance increasing with higher levels of disorder and stochasticity. The fracto-cohesive length and representative volume element size are also predicted and constrain the minimum defect and lattice sizes, respectively, for failure to be considered a fracture process. The framework is extended to consider the fracture behaviour of the lattice, the development of damage zones and their impact on the fracture toughness.

We report the formulation and printing of Cu inks composed of binary mixtures of colloidal ∼5 nm Cu nanocrystals (NCs) and ∼500 nm Cu microcrystals (MCs) and postdeposition chemical and low-temperature thermal treatments to achieve micron-thick, high-conductivity metal traces that yield high-performance, printed radio frequency (RF) electronic devices. Solid-state NH 4 Cl treatment of binary Cu NC/MC mixed films removes insulating ligands and surface oxides and establishes Cl – -mediated surface chemistry that drives NC-enabled “nano-soldering” between MCs and increased MC faceting and interparticle necking. Subsequent mild annealing under N 2 promotes further densification to yield micron-scale films with resistivities as low as ∼14.5 times that of bulk Cu for optimized 75 wt % NC/MC films after annealing at 150 °C for 5 min. By formulating these binary NC/MC systems in α-terpineol/ethyl cellulose/poly(vinylpyrrolidone) vehicles, we obtain non-Newtonian inks compatible with both screen printing and direct ink writing (DIW) and we deposit micron-thick patterned conductive traces on flexible substrates. Screen-printed, flexible RF inductively coupled interdigitated capacitors are fabricated from the mixed NC/MC inks and achieve Q = 4.89, corresponding to ∼68% of a bulk-Cu reference. DIW produces over 100 μm thick CAD-defined traces with an average resistivity of 95 ± 22.6 μΩ·cm. We show that NC-enabled processing of mixed NC/MC systems yield manufacturable, high-frequency metal components for printed Internet of Things platforms.

We report the formulation and printing of Cu inks composed of binary mixtures of colloidal ∼5 nm Cu nanocrystals (NCs) and ∼500 nm Cu microcrystals (MCs) and postdeposition chemical and low-temperature thermal treatments to achieve micron-thick, high-conductivity metal traces that yield high-performance, printed radio frequency (RF) electronic devices. Solid-state NH₄Cl treatment of binary Cu NC/MC mixed films removes insulating ligands and surface oxides and establishes Cl^–-mediated surface chemistry that drives NC-enabled “nano-soldering” between MCs and increased MC faceting and interparticle necking. Subsequent mild annealing under N₂ promotes further densification to yield micron-scale films with resistivities as low as ∼14.5 times that of bulk Cu for optimized 75 wt % NC/MC films after annealing at 150 °C for 5 min. By formulating these binary NC/MC systems in α-terpineol/ethyl cellulose/poly(vinylpyrrolidone) vehicles, we obtain non-Newtonian inks compatible with both screen printing and direct ink writing (DIW) and we deposit micron-thick patterned conductive traces on flexible substrates. Screen-printed, flexible RF inductively coupled interdigitated capacitors are fabricated from the mixed NC/MC inks and achieve <i>Q</i> = 4.89, corresponding to ∼68% of a bulk-Cu reference. DIW produces over 100 μm thick CAD-defined traces with an average resistivity of 95 ± 22.6 μΩ·cm. We show that NC-enabled processing of mixed NC/MC systems yield manufacturable, high-frequency metal components for printed Internet of Things platforms.

Printed hybrid electronics and sensors achieve increased functionality via the integration of silicon-based chips with printed devices. Paper has gained attention as a substrate for printed devices due to the sustainability and biodegradability of cellulose. Reliable attachment of silicon-based chips onto printed metal traces is essential for manufacturing paper-based printed hybrid sensors. Paper substrates often have high roughness and porosity, leading to infiltration and spreading of screen-printed inks. Here, a nanocellulose coating is used to reduce the roughness of commercial cardstock from 4.23 μm to 0.60 μm, leading to a 1.49 μm reduction in the average trace roughness of screen-printed silver. The improved geometric uniformity results in an 18.1% increase in the yield of flip-chip bonds between test chips and screen-printed silver traces using an anisotropic conductive adhesive. The circuit resistance of prints and the contact resistance of bonds on the coated paper are comparable to those of devices on polyimide, a widely used polymer substrate. In addition, the thermal budget of the nanocellulose coating was investigated by measuring flip-chip bond yield and device resistance as a function of annealing temperature and flip-chip bond parameters.

Bioinspired hierarchical microstructures offer a route toward engineered fatigue resistance in additively manufactured alloys. However, it remains unclear how discrete structural constituents independently govern damage accumulation, particularly during the critical fatigue initiation regime where short cracks strongly interact with local microstructure. Here, we investigate multiscale fatigue initiation in a dual-phase, nanolamellar AlCoCrFeNi 2.1 high-entropy alloy. By comparing microscale specimens that isolate the nanolamellar structure against macroscale specimens containing the full melt-pool architecture, we identify size-dependent fatigue initiation mechanisms. We find that failure is dictated by nanolamellar interfaces at the microscale, whereas mesoscale melt pool boundaries serve to initiate fatigue at the macroscale. This mechanistic shift is accompanied by a transition from macroscale quasi-brittle failure to microscale plasticity-driven crack extension. Our results provide a physical framework for understanding how structural hierarchy governs the transition from discrete microstructural deformation to continuum fatigue fracture behavior, informing the design of damage-tolerant, additively manufactured alloys.

Abstract Internet of things (IoT) systems rely on the broad deployment of electronic devices and sensors in diverse environments. In some applications, including agriculture, packaging, and medical, there is a need for devices with relatively short lifetimes. Thus, there is growing interest in bio-based substrates for printed electronics and sensors that improve sustainability and minimize the environmental impact of IoT devices. Paper is a natural choice as a substrate for disposable printed devices; however, the high surface roughness and porosity of typical papers lead to printed structures with high variability. Here, we report a nanocellulose-infiltrated paper consisting of a cellulose nanofibrils (CNFs) film supported on a cardstock substrate. The CNF solution was coated onto the cardstock and then press-dried to form a smooth and dense surface. The CNF partially infiltrates the substrate, but forms a distinct film on the surface. The CNF coating process reduced the root mean square surface roughness of the cardstock from 4.3 µ m to 189 nm. This improvement in surface properties enables the screen-printing of silver patterns with geometric uniformity comparable to that of patterns printed on conventional polyimide substrates. The moisture sensitivity of these cellulose-based substrates can be exploited for moisture sensing, and the moisture absorption/desorption and resulting change in relative permittivity of these substrates are characterized.

Hydrodynamically confined microflows (HCMs) can be created underneath microfluidic probes (MFPs) in open liquid environments for local processing of surfaces, cells, and tissues. The behavior of the flow inside the gap between the probe and a substrate has been widely characterized by monitoring the size and shape of the confined flow using fluorescent dyes. However, velocity vectors within the flow, which are critical to a full understanding of fluid mechanics, have not been measured directly. Here, we report direct measurement of in-plane velocity vectors in an HCM using a micrometer-resolution particle image velocimetry. The effects of probe geometry, gap height, flow rate, and flow rate ratio on HCMs are investigated using fluorescent particles and dyes. Multi-port polydimethylsiloxane microfluidic probes (MFPs) are used to generate microflows under various conditions. Acquisition of images from different x–y planes parallel to the substrate provides information on the three-dimensional (3D) nature of the confined flows with two components (2C). The in-plane shape and velocity measurements of the HCMs, along with computational fluid dynamics (CFD) simulations, are used to estimate a complete 3D three-component (3C) shape of the confined flow. The results presented here provide a more comprehensive understanding of fluid mechanics in HCMs and will facilitate their application in biology, chemistry, medicine, and engineering.

A new design paradigm has emerged for interfaces between materials, where architecture is used to control stress transfer, adhesion, and failure. In this Perspective, we consolidate disparate design strategies under a unified framework of “architected interfaces.” Different designs are characterized by their architectural feature size relative to a stress-localization length and dominant toughening mechanism. We conclude by identifying key challenges that must be overcome to realize architected interfaces in structures.

We report high figure-of-merit optical leaf sensors based on dielectric metasurfaces and stimuli-responsive polymers. The metasurfaces have narrowband resonances and are broadly transparent across the visible spectrum to allow photosynthesis for unobstructive monitoring of crop health. These sensors are fabricated at scale using direct nanoimprint lithography into UV curable-TiO2 nanocrystal (NC) inks to structure optical metasurfaces, followed by a room-temperature ligand exchange process to create a nanoporous TiO2 scaffold. The nanoporous TiO2 metasurfaces are then infiltrated by a stimuli-responsive polymer while retaining their geometry and optical quality. As a proof of concept, we demonstrate optical humidity sensors by incorporating the moisture-responsive chitosan biopolymer into the nanoporous TiO2 metasurfaces, resulting in a 430 % improvement in sensitivity compared to conventional RI sensors where the polymer surrounds the metasurface. The sensors are mounted on leaves and the highly reflective resonances, which are readily distinguishable from the leaf background, respond dynamically to leaf water stress, enabling passive, battery-free monitoring of leaf surface humidity.

Instrumented indentation is a common technique for measuring the elastic properties of thin materials, including elastomers, gels, and biological materials. Traditional indentation analysis yields a reduced modulus, which is a function of Young's modulus and Poisson's ratio, thus requiring one of the parameters to be estimated or independently measured to decouple the properties. It is difficult in some cases to know the true deformation of the surface due to substrate deformations, machine compliance, and thermal drift. To address these issues, a new technique is demonstrated in which 3D displacements are measured at discrete points along the surface of a transparent specimen during indentation tests using microscopy and fluorescent micrometer-scale particles embedded in the specimen. The out-of-plane displacements of the particles are measured using a defocused imaging technique, taking advantage of the change in spherical aberration ring radius with distance from the focal plane. A technique for tracking the motion of the particles and calibrating the system is described, and experimental measurements on a silicone elastomer are presented. Two optimization algorithms were developed to extract Young's modulus and Poisson's ratio from the experimental measurements. The first algorithm uses radial and normal displacements measured along the surface of the specimen. The second algorithm uses a combination of traditional indentation analysis and radial surface displacements. The elastic properties of polydimethylsiloxane (PDMS) were calculated from experimental data using both algorithms. The results from both methods were in agreement with each other, as well as with values of Young's modulus reported in the literature.

Biocompatible adhesive Resin A multi-functional polymeric resin system based on thiol-ene crosslinking provides a strong adhesive interface with dentin that composes the inside structures of teeth and is biocompatible with dental pulp cells residing in the dentinal tubules that extend from the dentin surface. More details can be found in the Research Article by Kyle H. Vining and co-workers (DOI: 10.1002/adhm.202501993).

The failure of mechanical metamaterials is a function of the interplay between the properties of the base material and the microstructural geometry. Stochastic failure properties of the base material and disordered microstructural geometries can contribute to variations in the global failure mechanics that are not captured in traditional analyses of ordered, deterministic architected materials. We present a probabilistic framework that couples stochastic material failure and geometric disorder to predict failure in lattice mechanical metamaterials. These predictions are verified through finite element analysis, which confirm that disorder and stochasticity affect both the mean and variance of the damage initiation load in a lattice, with average failure loads being generally reduced and variance increasing with higher levels of disorder and stochasticity. The fracto-cohesive length and representative volume element size are also predicted and constrain the minimum defect and lattice sizes, respectively, for failure to be considered a fracture process. The framework is extended to consider the fracture behavior of the lattice, the development of damage zones, and their impact on the steady-state fracture toughness.

Abstract Background Measuring the mode-I toughness of two-dimensional (2D) lattice materials and other thin-sheet materials poses a significant challenge with existing testing techniques. For example, material compression ahead of the crack or unstable crack growth frequently arise during testing of these materials and can complicate toughness measurements. Objective This study investigates a new experimental method, the hinged rigid beam (HRB), to evaluate the mode-I toughness of elastic-brittle 2D lattice and thin-sheet materials. Methods The HRB uses stiff beams and a hinged boundary to create a monotonically decreasing tensile stress along the length of the test material in the direction of the crack path. An analytical model, corrected using finite element studies, allows the critical strain energy release rate to be extracted from experimental data collected in HRB testing. Tests on homogeneous poly(methyl methacrylate) (PMMA) are performed to validate the technique and model. Then, 2D triangular and hexagonal lattices with varying relative densities are characterized using the HRB. Results Compliance measurements from experiments on homogeneous PMMA closely match the corrected model, and toughness measurements are consistent with previously reported values. Stable crack growth was observed in the tested lattice specimens, and toughness values were readily calculated. Toughnesses are compared to models for lattice fracture that use simple scaling laws. Good agreement is observed between experiments and model, especially at lower relative densities. As the relative density of the triangular lattices increased, the failure mode transitioned from strut-based to node-based, and the measured toughness values diverge from the models. Conclusions The HRB method allows for stable crack growth and prevents alternative modes of failure, like buckling, in thin-sheet materials and 2D lattices. This new experimental approach can be used for the fracture testing of a wide range of thin or highly compliant materials.

This study develops a biocompatible multifunctional thiol-ene resin system for adhesion to dentin mineralized tissue. Adhesive resins maintain the strength and longevity of dental composite restorations through chemophysical bonding to exposed dentin surfaces after cavity preparations. Monomers of conventional adhesive systems may result in inhomogeneous polymer networks and the release of residual monomers that cause cytotoxicity. In this study, a one-step multifunctional polymeric resin system by incorporating trimethylolpropane triacrylate (TMPTA) and bis[2-(methacryloyloxy)ethyl] phosphate (BMEP) is developed to enhance both mechanical properties and adhesion to dentin. Molecular dynamics simulations identify an optimal triacylate:trithiol ratio of 2.5:1, which is consistent with rheological and mechanical tests that yield a storage modulus of ≈30 MPa with or without BMEP. Shear bond tests demonstrate that the addition of BMEP significantly improves dentin adhesion, achieving a shear bond strength of 10.8 MPa, comparable to the commercial primer Clearfil SE Bond. Nanoindentation modulus mapping characterizes the hybrid layer and mechanical gradient of the adhesive resin system. Further, the triacrylate-BMEP resin shows biocompatibility with dental pulp cells and fibroblasts in vitro. These findings suggest that the triacrylate-trithiol crosslinking and chemophysical bonding of BMEP provide enhanced bond strength and biocompatibility for dental applications.

Temporary and reversible underwater adhesion is important for a number of robotic applications, including picking up objects, facilitating locomotion in confined environments, and attaching to surfaces during periods of observation. Here, we present a starfish-inspired tube foot composed of a soft hydrogel mouth and a rigid stem, fabricated by integrating two serially bonded cylindrical components with distinct mechanical properties. Upon swelling, the initially straight hydrogel cylinder undergoes a selective shape transformation into a soft, cupped pad that deforms to stretch and spread upon contact, enabling effective adhesion to target surfaces. During detachment, a vacuum is formed within the tube, leading to strong underwater adhesion. The artificial tube feet show high adhesion hysteresis, autonomous release by external stimuli, and immediate detachment by pneumatic actuation with integrated system. The temporary underwater adhesive inspired by the tube feet of starfish enables functionality in underwater robotics and is demonstrated through underwater manipulation of rocks.

This study developed a biocompatible multifunctional thiol-ene resin system for adhesion to dentin mineralized tissue. Adhesive resins maintain the strength and longevity of dental composite restorations through chemophysical bonding to exposed dentin surfaces after cavity preparations. Dental pulp cells are exposed to residual monomers transported through dentinal tubules. Monomers of conventional adhesive systems may result in inhomogeneous polymer networks and the release of residual monomers that cause cytotoxicity. In this study, we develop a one-step multi-functional polymeric resin system by incorporating trimethylolpropane triacrylate (TMPTA) and bis[2-(methacryloyloxy)ethyl] phosphate (BMEP) to enhance both mechanical properties and adhesion to dentin. Molecular dynamics simulations identified an optimal triacylate:trithiol ratio of 2.5:1, which was consistent with rheological and mechanical tests that yielded a storage modulus of ~30 MPa with or without BMEP. Shear bond tests demonstrated that the addition of BMEP significantly improved dentin adhesion, achieving a shear bond strength of 10.8 MPa, comparable to the commercial primer Clearfil SE Bond. Nanoindentation modulus mapping characterized the hybrid layer and mechanical gradient of the adhesive resin system. Further, the triacrylate-BMEP resin showed biocompatibility with fibroblasts in vitro. These findings suggest the triacrylate-trithiol crosslinking and chemophysical bonding of BMEP provide enhanced bond strength and biocompatibility for dental applications.

Precision agriculture systems enabled by small, biodegradable, passive, wireless soil sensors will allow for more judicious use of limited resources while increasing crop yield. Here, a capacitive soil moisture sensor is integrated with an inductive loop antenna to enable wireless sensing through loose inductive coupling. To minimize sensor costs, the sensor is manufactured by screen printing a conductive ink onto a biodegradable paper-based substrate. The sensor is interrogated wirelessly using a vector network analyzer, and has an interrogation range up to 10 cm in air and dry soil. Five sensors were buried at varying depths alongside a plant and a commercial soil sensor. The sensor’s ability to respond to changes in soil moisture at depths up to 5 cm is demonstrated. The sensors’ ability to respond to changes in soil moisture over long periods of time (i.e. up to 113 days) is demonstrated. All sensors remained functional through the end of the testing period, illustrating their suitability for use in agricultural settings over an entire growing season.

Abstract There is an increasing need for inexpensive biodegradable sensors that can be easily employed in networks such as the Internet of Things. Paper materials are renewable, biodegradable, and sustainable, and thus could be used as substrates for electronic sensors. This work examined two commodity cellulose materials, an envelope paper and a linerboard, as potential substrates. A multistage coating process was developed to create a smooth surface for screen-printing of sensors using inexpensive microfibrillated cellulose. Employing this process, approximately 10 g m −2 of microfibrillated cellulose was deposited, enhancing the mechanical performance of the coated materials compared with their uncoated counterparts. Sensors printed on the microfibrillated cellulose-coated substrates had reasonable electronic performance compared with those printed on a polymer substrate. Results indicate that further reducing surface roughness would be helpful for sensor performance.

Abstract Mechanical metamaterials with engineered failure properties typically rely on periodic unit cell geometries or bespoke microstructures to achieve their unique properties. We demonstrate that intelligent use of disorder in metamaterials leads to distributed damage during failure, resulting in enhanced fracture toughness with minimal losses of strength. Toughness depends on the level of disorder, not a specific geometry, and the confined lattices studied exhibit a maximum toughness enhancement at an optimal level of disorder. A mechanics model that relates disorder to toughness without knowledge of the crack path is presented. The model is verified through finite element simulations and experiments utilizing photoelasticity to visualize damage during failure. At the optimal level of disorder, the toughness is more than 2.6× of an ordered lattice of equivalent density.

Electroadhesive clutches with stiff dielectric films provide unique potential for programming stiffness and adhesion, but their low force capacities per unit area limit their applications. Reversible adhesives based on van der Waals forces leverage soft contact surfaces to achieve conformal contact and high adhesion, but such soft surfaces are challenging to use in electroadhesives as they add latent adhesion that reduces switchability. Herein, soft, elastomeric dielectrics that combine electrostatics and surface force‐mediated adhesion are used to build electroadhesive clutches with high force capacity per unit contact area and voltage. Analytical models from fracture mechanics explain how clutch compliance, shape, and surface roughness affect force capacity and switchability. These models are used to design electroadhesives with soft dielectric films that achieve force capacities similar to those of state‐of‐the‐art clutches with stiff dielectrics in terms of force capacity per unit area (22 N cm −2 ), but with simple dielectric materials with one‐fifteenth of the relative permittivity. In addition, controlled surface roughness is used to increase the switchability of any given electroadhesive clutch design. Finally, the ability of soft dielectric clutches is demonstrated to enable programmable stiffness in reconfigurable robotic fabrics, structural elements, and robotic fingers.

Abstract The ability to control adhesion on demand is important for a broad range of applications, including the gripping and manipulation of objects in robotics and manufacturing, and the temporary attachment of wearable devices. Despite recent advances in tunable adhesive materials, most existing solutions have modest adhesion strength and are limited by a compromise between the maximum and minimum adhesion, where increased strength prevents the release of lighter objects. To overcome these challenges, thermally responsive polymers, which can exhibit both high stiffness and a large reduction in stiffness via heating, have the potential to enable strong and tunable adhesion. Here, a microstructured composite adhesive with high strength (&gt;2 MPa) and dynamically tunable adhesion (16×) is realized using a solvent‐assisted molding technique. The adhesive consists of an array of composite micropillars whose small scale and material composition enable strong and tunable adhesion. While thermally actuated systems often have slow response times, it is shown that miniaturization allows response times to be reduced to &lt;1s for heating and &lt;10s for cooling. These strong, fast, and dynamically tunable adhesives offer advantages over existing solutions and can be manufactured for practical adoption through the scalable solvent‐assisted molding technique.

This paper investigates the directed interactive topology optimization design problem for multi-agent affine formation maneuver control. Firstly, considering the optimization indexes such as information interaction cost and information spreading energy consumption, a directed topology optimization model satisfying affine formation maneuver is established, including two sub-models of topology structure construction and weight allocation. Secondly, aiming at the topological structure construction for affine formation maneuver, a directed k-rooted graph detection method is proposed, which can realize the solution of d +1 -rooted constraint for directed information interaction topology, and then an improved NSGA-II topological structure construction optimization algorithm is designed. Finally, a formation of seven agents in twodimensional space is taken as an example for simulation verification. The results show that the improved topology NSGA -II topology construction optimization The algorithm has better optimization effects, can effectively provide a variety of feasible directed interactive topologies for affine formation maneuver control, and the generated interactive topology can meet the requirements of directed d +1 -rooted graph.

Aiming at the characteristics of small number and many types of rail surface defect samples, as well as the problems of unstable transfer learning effect and threshold segmentation being easily affected by environmental factors in real scenes, an improved Markov defect segmentation method with zero samples is proposed. Firstly, the collected data is processed by Gabor function to highlight the defect features and reduce the data dimension to obtain the reduced dimension feature map; Kmeans clustering is performed on the processed feature map to reduce the distribution of data and reduce the influence of reflection and shadow, and the clustering result is used as the pre-classification matrix; an improved Markov random field two-layer graph model is constructed and inferred through the reduced dimension feature map and the pre-classification matrix; the local geometric structure of the defect part is analyzed according to the eigenvalues of the classification matrix inferred by the model; finally, the defect area is marked and the defect segmentation is completed. The experimental part uses a self-sampling data set, and the final conclusion is drawn based on the comparative experiment and ablation experiment. The experimental results show that the pixel accuracy, average pixel accuracy, weighted intersection-over-union ratio, and average intersection-over-union ratio of this method on the self-sampling data set are respectively 93.6%、80.7%、89.4%、68.2% , which exceeds the accuracy of other comparative detection algorithms.

Mechanical metamaterials utilize geometry to achieve exceptional mechanical properties, including those not typically possible for traditional materials. To achieve these properties, it is necessary to identify the proper structures and geometries, which is often a non-trivial and computationally expensive process. Here, we propose a Substructure Perturbation Method (SSPM) for systematic design and search of these materials with programmed deformation modes. We present the theoretical fundamentals and computational algorithms of the SSPM, along with four design problems to investigate the effect and performance of the SSPM. Results reveal the necessity of analyzing multiple substructures simultaneously in obtaining successful designs, and its effectiveness in speeding up numerical processes. In one design case, SSPM is shown to be effectively two orders of magnitude faster than another state-of-art approach while using less computational resources. We also show an experimental validation where the fabricated prototypes can grasp objects respectively by undergoing programmed deformations under corresponding inputs. The proposed SSPM provides new fundamentals and strategies for the design of mechanical metamaterials with advanced functionalities.

Wear is a ubiquitous phenomenon that limits the life of many engineered components with sliding interfaces through the gradual removal of material. The wear of polymers is crucial in many applications, ranging from bearings to orthopedic implants to nanolithography processes. The wear rate of polymers is strongly affected by the stress and temperature at the interface. The effects of temperature and stress are often described empirically since the wear process involves complex interactions between multiple asperities on rough surfaces over a range of length scales. Nanoscale tribology experiments at the single-asperity level have provided new insights into the underlying mechanisms of wear. Experiments on hard covalently bonded materials, including silicon and diamond, have demonstrated that wear is an atomic attrition wear process that can be modeled using stress-assisted transition state theory. Here, we examine the wear of a common polymer, polymethylmethacrylate (PMMA), at the nanoscale as a function of stress and temperature and show that the polymer wear is controlled by a combination of atomic attrition and viscoelastic relaxation. While the wear experiments are conducted at the nanoscale via atomic force microscopy, the results show that accounting for the local stress distribution at the contact interface is critical to understanding the wear behavior, an effect that was not considered in earlier studies on hard materials. Using a model that accounts for the stress distribution, we demonstrate the ability to predict the wear volume within 8%.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

Mechanical metamaterials with engineered failure properties typically rely on periodic unit cell geometries or bespoke microstructures to achieve their unique properties. We demonstrate that intelligent use of disorder in metamaterials leads to distributed damage during failure, resulting in enhanced fracture toughness with minimal losses of strength. Toughness depends on the level of disorder, not a specific geometry, and the confined lattices studied exhibit a maximum toughness enhancement at an optimal level of disorder. A mechanics model that relates disorder to toughness without knowledge of the crack path is presented. The model is verified through finite element simulations and experiments utilizing photoelasticity to visualize damage during failure. At the optimal level of disorder, the toughness is more than 2.6x of an ordered lattice of equivalent density.

The conversion of raw images into quantifiable data can be a major hurdle and time-sink in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a sub-selection of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg .

A minimal number of rigid constraints makes soft robots versatile, but many of these robots use soft pneumatic actuators (SPAs) designed to inflate through a single trajectory. In an unloaded actuator, this trajectory is dictated by the arrangement of in-extensible and elastic materials. External strain limiters can be added post-fabrication to SPAs, but these are passive devices. In this paper, we offer design and control techniques for an electrically active strain limiter that is easily adhered to existing SPAs to provide signal-controlled force output. These sheathed electroadhesive (EA) clutches apply antagonistic forces through the constitutive properties of their silicone sheathing and through the variable friction of the clutch itself. We are able to design the sheathing to passively support loads or minimize passive stiffness. We control clutch forces via an augmented pulse-width-modulation (PWM) of the high voltage square-wave input. We perform an initial, empirical characterization on the system with tensile material testing. The clutch system resists motion with sustained forces ranging from 0.5N to 22N. We then demonstrate its ability to apply predictable nonconservative work in a dynamic catching task, where it can limit catching height from 15cm to 1cm. Finally, we attach it to an inverse pneumatic artificial muscle (IPAM) to show that variable strain limitation can control position of the SPA endpoint.

Bonded single lap joints are used to join structural components in automotive, aerospace, and other engineered systems. The stresses in the joint are nonuniform, and high stresses near the edge typically limit the force capacity of the joint. Here, we optimize the design of the adherend geometry to improve the stress uniformity at the interface and enhance the force capacity. Through the use of machine learning and mechanics-based finite element analysis, we quantify the functional relationship between the geometry of the adherend and the variance of the interface stress distribution. A neural network is used to identify an optimized adherend geometry with improved stress uniformity for cases with different constraints on joint stiffness and manufacturability. A fracture mechanics analysis is used to predict the force capacity enhancement of the optimized designs, and the results are verified through experiments on joints consisting of aluminum adherends bonded with a cyanoacrylate adhesive. The experimentally measured force-capacity enhancement is 2.4×, which closely agrees with model predictions.

Material architecture provides an opportunity to alter and control the fracture process zone shape and volume by redistributing the local stresses at a crack tip. Properly designed structures can enlarge the plastic zone and enhance the effective toughness. Here, we use a pillar array as a model structure to demonstrate how variations in geometry at a crack tip control the size and shape of the plastic zone and can be used to engineer the effective toughness. Elastic–plastic finite element simulations are used to show how the pillar width, spacing, and height can be varied to tailor the size and shape of the plastic zone. A set of analytical mechanics models that accurately estimate the shape, volume, and resulting toughness as a function of the base material properties and geometry are also presented. A case study extends the analysis to sets of non-regular pillar arrays to illustrate how architecture can be used to alter toughness along the crack path.

Abstract Electroadhesive devices with dielectric films can electrically program changes in stiffness and adhesion, but require hundreds of volts and are subject to failure by dielectric breakdown. Recent work on ionoelastomer heterojunctions has enabled reversible electroadhesion with low voltages, but these materials exhibit limited force capacities and high detachment forces. It is a grand challenge to engineer electroadhesives with large force capacities and programmable detachment at low voltages (&lt;10 V). In this work, tough ionoelastomer/metal mesh composites with low surface energies are synthesized and surface roughness is controlled to realize sub‐ten‐volt clutches that are small, strong, and easily detachable. Models based on fracture and contact mechanics explain how clutch compliance and surface texture affect force capacity and contact area, which is validated over different geometries and voltages. These ionoelastomer clutches outperform the best existing electroadhesive clutches by fivefold in force capacity per unit area (102 N cm −2 ), with a 40‐fold reduction in operating voltage (± 7.5 V). Finally, the ability of the ionoelastomer clutches to resist bending moments in a finger wearable and as a reversible adhesive in an adjustable phone mount is demonstrated.

The conversion of raw images into quantifiable data can be a major hurdle in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a portion of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg.

Internet of things (IoT) systems for precision agriculture offer the opportunity for more efficient use of water and fertilizers. Here, capacitive moisture sensors are screen-printed on a fully biodegradable paper substrate infiltrated with cellulose nanofibrils (CNFs). Screen-printed trace quality on the CNF-composite substrate is comparable to traces printed on polyimide and superior to traces printed on conventional cardstock. CNF-composite sensors absorb moisture and are shown to respond to changes in relative humidity (RH) in air. Sensors measured in loamy sand, similar to soil found in midwestern agricultural fields, are shown to respond to changes in soil moisture. The sensors demonstrate fast response times in both air and soil, making them ideal for use in agricultural applications. Small feature sizes achievable through screen-printing on the CNF-composite enable their direct use in 902–928 MHz chipless passive wireless sensing systems, as the fabricated sensors are shown to have a self-resonance well above the operating frequency band.

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

Precision agriculture systems enabled by passive, wireless, subsurface soil sensors can provide high resolution data on soil conditions and increase crop yield. These frequency-coded sensor nodes are composed of an antenna, acoustic resonator and capacitive soil moisture sensor. Here, a high performance Al <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.7</inf> Sc <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.3</inf> N Lamb-wave resonator (LWR) for operation in the 902-928 MHz industrial, scientific, medical (ISM) band is fabricated. Its figure of merit is 37% larger than state-of-the-art LWR at similar frequency. The LWR is integrated with a capacitive moisture sensor and its frequency tuning capabilities are demonstrated. A sensitivity of 65 kHz at interrogation ranges of up to 150 m is determined.