Extreme Mechanics Letters最新文献

“Lithium metal” batteries operate via electroplating/stripping of Li metal and promise vast theoretical capacities. However, significant technical barriers must be addressed prior to commercialization. The primary challenges include the generation of mechanical stresses and strains due to "infinite volume expansion,” as well as non-uniform deposition of lithium metal, which often leads to dendrite formation and growth. Lithium dendrite formation is particularly critical, as dendrites can penetrate solid-state electrolytes, eventually shorting to the cathode, thereby diminishing the capacity of the battery and inducing severe safety hazards. These primary issues are intrinsically linked to the mechanical behavior of lithium; as such, this study focuses on the mechanical response of lithium electrodeposition under various electrochemical conditions. Experimental tests herein reveal that larger applied current densities induce significantly larger mechanical stresses during electroplating of Li metal. This manuscript concludes by detailing practical implications of these experimental observations, particularly regarding dendrite growth through solid-state electrolytes of solid-state batteries.

Defects are inevitable in two-dimensional (2D) materials. Thus, the strength prediction and design are crucial for practical application of defective 2D materials. Utilizing a dataset from molecular dynamic (MD) simulations, this study aims to predict, as well as design the strength of defective graphene. Through convolutional neural networks (CNN), the constructed residual network ResNet34 can accurately predict the fracture strength directly from the defect configuration of graphene. Meanwhile, ablation class activation map (Ablation-CAM) further identifies the sensitive domains that dominate the fracture strength, in accordance with the crack initiation regions confirmed by MD simulations and experiments. In particular, a new descriptor named sensitive domain factor (SDF) was developed to characterize the important features of sensitive domains. Furthermore, a genetic algorithm (GA) is then applied to strategically optimize the defect configuration under a given defect density, achieving an ideal configuration with the maximum fracture strength. This work pioneers a machine learning framework for the extraction and optimization of defective features in monolayer graphene, offering a novel approach to design the mechanical properties through defect engineering.

The global incidence of diabetes increases yearly. About 10 % of adults are affected. This study established a multiple linear regression (MLR) time domain analysis model of radial artery pulse wave characteristic parameters in CUN, GUAN and CHI based on traditional Chinese medicine (TCM) to assess diabetes. Pulse signals of diabetic subjects were collected by a pulse instrument. The locations of CUN, GUAN and CHI were determined by a TCM practitioner, and all pulse signals were normalized. Measurement data from 100 male and 100 female patients were used to establish a new time domain analysis method for extracting pulse wave characteristic parameters called equal pressure pulse transit time (EP-PTT). For these mentioned samples, the EP-PTT is different for male and female diabetic subjects, it was smaller for male diabetic subjects than those of female diabetic subjects. EP-PTT₁ and EP-PTT₆ are relevant to predicting age based on CUN, GUAN and CHI signals, which showed a linear negative correlation (P < 0.05). The EP-PTT_i (i = 2, 3, 4) of female diabetic subjects are linearly correlated with age, and all of these are positively correlated (P < 0.05). These observations were clearly different from observations in healthy controls. The pulse waveform of CUN, GUAN and CHI of diabetic subjects can explore the relationship between the age of diabetic subjects and EP-PTT. The preliminary findings will provide foundational data for type 2 diabetes assessment based on CUN, GUAN and CHI pulse signals of radial artery, and the proposed new analysis method can be used for assessing type 2 diabetes.

The deformation of single crease origami units under partial stretch loads along the crease extension direction is influenced by their plates’ bending stiffness. For their application, it is of great significance to analyze the non-uniform deformation pattern of origami units and clarify the efficiency of partial driving on global unfolding. In this paper, the unfolding behavior of single crease origami units under partial stretch is systematically investigated. From the bending phenomenon of paper models, a parametrical simulation analysis is performed to analyze deformation patterns and virtual crease distribution. Furthermore, the overall uniform motion efficiency of origami units with local driving is discussed, while the criterion of uniform unfolding motion of the crease is defined. The feasible ranges of partial load factors corresponding to single crease origami units with different initial crease angles are also clarified under the constraint of the required uniform motion range. Mechanical and kinematic models are also established based on equivalent rigid plate nonlinear crease elements and equivalent single-vertex six-crease patterns, which can accurately identify the deformation characteristics of the single-crease origami unit under partial stretch. Furthermore, the flattening analysis of derived single-vertex six-crease origami units and their arrays is also carried out to investigate the influence of sector angles and crease rotation stiffness. Two different decay characteristics are observed, corresponding to the fully flattened planar state and the non-fully flattened planar state, respectively. A motion snap that occurs before the fully flattened planar state is also identified. The findings can be regarded as the research basis for the complex mechanical behavior of origami structures composed of single crease origami units.

Photosensitive elastomers have the ability to undergo significant deformations upon the illumination by light of a specific wave length. Compared to other non-mechanical stimuli used in materials such as electro- or magneto-active polymers, light offers interesting advantages such as ultra high application speed and spatial precision. Moreover, as light powers and controls the movement of the material, the need for stiff wiring or external energy sources is eliminated. This becomes especially important in the design of miniature sized applications such as nano-scale robots. In the scope of the present work the conversion of photonic energy into a mechanical response originates from switching molecules embedded into a soft polymer matrix. Upon stimulation by UV-light, these molecules can switch from their stable trans - into a meta-stable cis-isomer, which induces a mechanical response on the macro scale. Depending on the experiment this can be visible as a deformation of the material or an increase in stiffness. This contribution presents the results of various mechanical and photo-mechanical experiments performed with the silicone Elastosil P 7670™ mixed with azobenzene molecules. These experiments are conducted in such a fashion that the obtained results are well suited for the identification of material parameters that arise from a photo-viscoelastic continuum modeling approach.

Origami cores are increasingly recognized as effective structures for energy absorption in sandwich plates. However, as most origami sandwich cores are made of tessellations of orthotropic unit cells, their free edges may hinder the formation of plastic hinges and reduce energy absorption capacity. To eliminate such free edges, in this work, by trimming the popular Miura-ori unit cells to form ring-shaped loops, we create a new origami sandwich plate with improved energy absorption efficiency. We study the energy absorption characteristics of these origami ring cores through a combination of theory, numerical simulations, and experiments. Both simulations and experiments verify that the origami ring cores possess quantized energy absorption capacity, related to the number of additional plastic hinges derived from strong local buckling of origami creases. We develop a theoretical model that effectively captures the formation of plastic hinges and predicts their absorbed energy. In summary, the origami ring cores present a novel and promising sandwich plate design approach, characterized by quantized energy absorption performance. This innovation holds significant potential for diverse engineering applications across sectors such as the aeronautic and marine industries and infrastructure development.

The ability to automatically discover interpretable mathematical models from data could forever change how we model soft matter systems. For convex discovery problems with a unique global minimum, model discovery is well-established. It uses a classical top-down approach that first calculates a dense parameter vector, and then sparsifies the vector by gradually removing terms. For non-convex discovery problems with multiple local minima, this strategy is infeasible since the initial parameter vector is generally non-unique. Here we propose a novel bottom-up approach that starts with a sparse single-term vector, and then densifies the vector by systematically adding terms. Along the way, we discover models of gradually increasing complexity, a strategy that we call best-in-class modeling. To identify and select successful candidate terms, we reverse-engineer a library of sixteen functional building blocks that integrate a century of knowledge in material modeling with recent trends in machine learning and artificial intelligence. Yet, instead of solving the NP hard discrete combinatorial problem with $2^{16}=\text{65,536}$ possible combinations of terms, best-in-class modeling starts with the best one-term model and iteratively repeats adding terms, until the objective function meets a user-defined convergence criterion. Strikingly, for most practical purposes, we achieve good convergence with only one or two terms. We illustrate the best-in-class one- and two-term models for a variety of soft matter systems including rubber, brain, artificial meat, skin, and arteries. Our discovered models display distinct and unexpected features for each family of materials, and suggest that best-in-class modeling is an efficient, robust, and easy-to-use strategy to discover the mechanical signatures of traditional and unconventional soft materials. We anticipate that our technology will generalize naturally to other classes of natural and man made soft matter with applications in artificial organs, stretchable electronics, soft robotics, and artificial meat.

It is challenging to achieve broadband isolation of ground vibration. In this work, pillared metastructures are proposed for broadband vibration isolation of surface wave in sandy soil numerically and experimentally. We first investigate two kinds of pillared metastructures, namely the pillars exposed on top of the soil or partially embedded in soil. Numerical and experimental results show that the case of partially embedded pillar has a wider and higher bandgap. Then we study gradient metastructures with linear or non-linear distributions of embedded depths, resulting in lower and wider attenuation frequency ranges, which are also validated by experiments. It is shown that gradient metastructures with a fixed ratio of bandgap overlaps to adjacent bandwidths have a greater advantage in low-frequency isolation. Our study provides great inspiration for simple design and manufacturing of new seismic metastructures to reduce surface waves or vibrations.

The force-displacement relationship is a fundamental mechanical property of materials, and the ability to inversely customize a prespecified relationship is useful for complex energy absorption systems, substrates of wearable electronics, and programmable vibration control. The recent development of mechanical metamaterials introduces graded strength into porous frameworks, which, however, can only achieve designable strain-hardening behavior. This is because the soft layers always deform prior to the hard layers due to the minimum energy gradient principle, regardless of the spatial arrangement of the component strength. Inspired by the “Domino effect” of tube inversion where its deformation sequence is governed by its kinematic compatibility, this paper introduces graded strength into a progressive and sequential tube inversion process, and correspondingly achieves arbitrarily prescribable force-displacement curves. Parametric study, numerical simulations for 9 different target curves, theoretical modeling leading to an inverse design framework, and experiments are carried out. This strategy paves the way for the inverse design of materials with arbitrary nonlinear mechanical responses essential for various novel applications.

Multistable morphing structures can reconfigure between different stable states that are separated by energy barriers, and one-degree-of-freedom (1-DOF) mechanisms have many merits, like simple actuation. This paper combines the two and proposes a new family of reconfigurable compliant linkages with many (2−6) 1-DOF kinematic paths that are separated by energy barriers. This new type of design is an extension of multistable structures, where each stable state corresponds to not just one configuration but a 1-DOF configuration space, i.e., a kinematic path. Components of the linkages are made elastically compliant, therefore enabling the switch between two isolated compatible paths with multi-stability. A generation-selection hybrid design algorithm to follow prescribed reconfigurable paths is proposed, and a minimum energy path (MEP) finding method to guide actuation to switch between different kinematic paths is developed. Four design examples with 2–3 reconfigurable paths and their experiments are presented, and the effectiveness of this method is verified. This work provides a fresh perspective to design the single-DOF reconfigurable mechanisms with larger design space, more reconfigurable kinematic paths, and easier reconfiguration actuation.