Extreme Mechanics Letters最新文献

Transcatheter aortic valve replacement (TAVR) has emerged as a promising treatment option for aortic stenosis. However, the prevalent stent used for valve placement restricts the post-release adjustment or movement of the artificial valve, increasing the potential risk to patients once accidental mispositioning occurs. Herein, we propose a 4D printing strategy to realize a proof-of-concept thermal-activated transcatheter aortic valve (TAV) stent that allows for programmable manipulation. Polylactic acid/polyurethane composites are directly printed to perform as the active units that tailor the configuration of the programmable TAV stent, accommodating to different tasks such as blood vessel navigation and topological fixation with cardiac cavity. A theoretical model is developed to explore the curvature evolutions of the active composite, realizing good agreement with experimental observations. Guided by the model, we seek out the optimized programming and activation conditions that allow for desired transformations to realize permanent fixation under intra-annular release and thermal-activated retraction under infra-annular release, inspiring the future development of TAV stents with shape memory principle.

The rapid advancement of micro-nano machining technology has led to a decrease in the dimensions of microdevices and microchips, following the principles of Moore’s law. In addition to conventional semiconductor materials like silicon, emerging nanoscale materials such as nanowires, nanotubes, and two-dimensional materials are being considered as promising alternative constituent materials. The mechanical properties of these materials have a significant impact on the performance and service life of these microdevices and microchips. However, conventional mechanical testing methods have difficulty in accurately measuring the properties of these materials at the nanoscale due to limitations in displacement control and microforce sensing. Consequently, there is an urgent need to develop a micromechanical device capable of testing nanoscale solid materials. In this study, we propose a concept based on high-resolution image sequences for the design of an integrated micromechanical device capable of synchronously measuring the force and deformation of tested specimens. The device has been fabricated using ultrafast femtosecond laser etching technology, which offers an efficient and cost-effective approach for manufacturing microstructures and is suitable for processing various materials such as metals and nonmetals. The stiffness of the device plays a crucial role in the design of the micromechanical device, and a stiffness-matching criterion is introduced to ensure appropriate design parameters. The fabricated device is employed to conduct in-situ tension experiments on SiC nanowires and multilayer molybdenum disulfide nanosheet within a scanning electronic microscope, enabling accurate measurement of their strength, modulus, and fracture strain.

Stick-slip friction exists widely in our life especially the occurrence of large earthquakes, but people cannot predict and control by a limited understanding of the mechanisms involved. In the present work, the whole process of stick-to-slip transition has been investigated through digital image correlation and acoustic emission. Two phases, namely, the nucleation and abrupt rupture phases have been discovered during the transition that are characterized well by nucleation and transient emission of dislocations, which may support the combination of pre-slip and cascade-up models. Based on the findings simple yet analytical expressions then have been obtained to predict the earthquake cycles consistent with available simulations and practical observations.

Generative artificial intelligence (AI) is shown to be a useful tool to automatically learn from existing information and generate new information based on their connections, but its usage for quantitative mechanical research is less understood. Here, we focus on the structure-mechanics relationship of architected graphene as graphene with void defects of specific patterns. We use Molecular Dynamics (MD) to simulate uniaxial tension on architected graphene, extract the von Mises stress field in mechanical loading, and use the results to train a fine-tuned generative AI model through a Low-Rank Adaptation method. This model enables the freely designed architected graphene structures and predicts its associated stress field in uniaxial tension loading through simple descriptive language. We demonstrate that the fine-tuned model can be established with a few training images and can quantitatively predict the stress field for graphene with various defect geometries and distributions not included in the training set. We validate the accuracy of the stress field with MD simulations. Moreover, we illustrate that our generative AI model can predict the stress field from a schematic drawing of the architected graphene through image-to-image generation. These features underscore the promising future for employing advanced generative AI models in end-to-end advanced nanomaterial design and characterization, enabling the creation of functional, structural materials without using complex numerical modeling and data processing.

Spider dragline silk is one promising material for producing artificial muscles, owing to its remarkable capacity for supercontraction and twist when exposed to high humidity. Based on the hydration absorption equation and the standard reinforcing model, we develop a phenomenological theory for elucidating the hydration-induced supercontraction and twist of spider dragline silk. The theory can reasonably predict the responses of softening, anisotropy, hydration-supercontraction, and twist of spider dragline silk. The theoretical predictions align with the experimental results. This study provides valuable insight into the underlying mechanisms of the hydration-induced deformation of spider dragline silk.

Interpenetrating Phase Composite (IPC) metamaterials, based on lattice topologies, have garnered significant attention as advanced materials for structural applications. However, conventional IPCs, which rely on periodic lattice unit cells, are prone to catastrophic failure due to their global deformation modes. To overcome this limitation, we present a novel IPC design utilizing aperiodic truss unit cells, inspired by the elusive “Einstein” monotile pattern. Our concept is demonstrated through IPC 3D printed via polymer jetting, using a hard polymer as the lattice filler and a soft polymer as the matrix. The distinctive mechanical properties of IPCs are characterized through single and cyclic quasi-static compression testing. Our findings demonstrate that aperiodic IPCs enable progressive deformation with gradual compression stress plateaus. Additionally, aperiodic IPCs exhibit remarkable damage tolerance, retaining 67.59 % of residual energy absorption and 73.83 % of ultimate strength after multiple cyclic compressions up to 30 % strain. These mechanisms are attributed to the synergistic deformation of interconnected unit cells, which lead to self-adjusting plastic collapse, progressive displacement evolution and delocalized deformation. This aperiodic concept paves the way for developing high-performance cushioning protection materials.

Elastic wave sensing is a crucial information acquisition technology with extensive applications in structural health monitoring, nondestructive testing, and other fields. However, traditional elastic wave sensing systems face challenges such as poor performance, high power consumption, and limited adaptability in complex environments. Here, a robust elastic wave sensing system integrating disordered metasurface and deep learning is demonstrated, enhancing the sensing performance in the environments with harsh noise or unknown signals. The scheme fully utilizes the complementary advantages of disordered metasurface and deep learning in physical encoding and intelligent decoding respectively. The meticulously designed disordered metasurface efficiently encodes elastic waves, and a single sensor acquires the encoding signals, enabling low-power information acquisition. The deep learning model performs adaptive and rapid intelligent decoding of the encoding signals, achieving efficient and robust information sensing while overcoming the sensing limitations of traditional compressed sensing in complex scenarios with low SNR and unknown signals. A series of experimental results demonstrate that, even under severe noise interference (known signal $\mathrm{SNR}\ge -15\mathrm{dB}$, unknown signal $\mathrm{SNR}\ge -7\mathrm{dB}$), the system can sense location information in elastic waves with a millisecond-level sensing speed and an accuracy above 90%. Furthermore, the successful application of the sensing system in vibration-tracking imaging and mechanical reading–writing further validates its practicability and robustness. This work may open up new avenues for the potential application of intelligent sensing in the fields of structural health monitoring, nondestructive testing, and human–machine interaction.

An inf–sup stable FE formulation for the thermo-chemo-mechanical simulation of thermoresponsive hydrogels is herein proposed by approximating the displacement field via quadratic shape functions and both the chemical potential (fluid pressure) and the temperature fields by linear functions. The formulation is implemented into a stable thermo-chemo-mechanical user-element subroutine (UEL) in Abaqus, denoted as Q2Q1Q1. The proposed formulation has been validated in relation to thermoresponsive hydrogels to interpret several examples of transient diffusion-driven swelling deformations. First, the upper/lower critical solution temperature behaviors of thermoresponsive hydrogels has been captured, studying several peculiarities comprising the diffusion length influence at the instantaneous loading state and the overlooked influence of the mass flux and the hyperelastic stretching on the temperature field. Subsequently, numerical analysis have been conducted in order to investigate the impact of temperature-dependent swelling ratio on the mechanical behavior of spheres undergoing compression. The accuracy of the proposed formulation has been assessed by numerically replicating the seminal experiments that explore the influence of crosslinking density on the thermally driven swelling of PNIPAAm hydrogels.

Impact performance is a key consideration when designing objects to be encountered in everyday life. Unfortunately, how a structure absorbs energy during an impact event is difficult to predict using traditional methods, such as finite element analysis, because of the complex interactions during high strain-rate compression. Here, we employ a physics-based model to predict impact performance of structures using a single quasistatic experiment and refine that model using intermediate strain rate and impact testing to account for strain-rate dependent strengthening. This model is trained and evaluated using experiments on additively manufactured generalized cylindrical shells. Using transfer learning, the trained model can predict the performance of a new design using data from a single quasistatic test. To validate the transfer learning model, we extrapolate to new impactor masses, new designs, and a new material. The accuracy of this model allows researchers to quickly screen new designs or leverage pre-existing databases of quasistatic test data. Furthermore, when impact tests are necessary to validate design selection, fewer impact tests are necessary to identify optimal performance.