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Owing to the various advances in artificial elastic surfaces, called metasurfaces, there have been growing demands on wave focusing devices in ultrasonic imaging, energy harvesting, etc. Nevertheless, previous approaches had suffered from critical limitation that it only works for a certain frequency, or it can be designed at only a small region. In this work, we propose a new metasurface design method for flexural wave focusing at various frequencies, i.e., achromatic metasurface, that can overcome the previous limitations and can be applied to any metasurface design. To this end, we newly investigated the theoretical requirements and design methods to achieve the achromatic metasurface. As a validation, the achromatic metasurface for wave focusing at various frequencies with the normal and obliquely incidence waves, which were impossible previously, are successfully designed. We expect the proposed achromatic design method can be applied in energy harvesting, wave control, and vibration control.

Thermoelectric generators offer a promising approach for harvesting waste heat from both natural and human-made sources, enabling sustainable electricity generation. While geometric design plays a crucial role in optimizing device performance, conventional approaches remain confined to simple configurations, limiting efficiency improvements. This constraint arises from the complex interplay of multiphysical interactions and diverse thermal environments, which complicates structural optimization. Here, we introduce a universal design framework that integrates topology optimization (TO) with additive manufacturing to systematically derive high-efficiency thermoelectric 3D architectures. By formulating an optimization problem to maximize power generation efficiency, our approach explores an unprecedentedly large design space, optimizing the geometries of thermoelectric materials across diverse thermal boundary conditions and material properties. The resulting TO-derived geometries consistently outperform conventional cuboids, demonstrating significant efficiency gains. Beyond in-silico studies, we provide theoretical insights and experimental validation, confirming the feasibility of our design approach. Our study offers a transformative way for enhancing thermoelectric power generation, with broad implications for next-generation sustainable energy technologies. This study applies topology optimization to thermoelectric materials to design power generator with maximum efficiency under diverse system conditions, then 3D prints the optimized legs and experimentally validates the performance improvements.

Memristive devices have been explored as electronic synaptic devices to mimic biological synapses for developing hardware-based neuromorphic computing systems. However, typical oxide memristive devices suffered from abrupt switching between high and low resistance states, which limits access to achieve various conductance states for analog synaptic devices. Here, we proposed an oxide/suboxide hafnium oxide bilayer memristive device by altering oxygen stoichiometry to demonstrate analog filamentary switching behavior. The bilayer device with Ti/HfO 2 /HfO 2−x (oxygen-deficient)/Pt structure exhibited analog conductance states under a low voltage operation through controlling filament geometry as well as superior retention and endurance characteristics thanks to the robust nature of filament. A narrow cycle-to-cycle and device-to-device distribution were also demonstrated by the filament confinement in a limited region. The different concentrations of oxygen vacancies at each layer played a significant role in switching phenomena, as confirmed through X-ray photoelectron spectroscopy analysis. The analog weight update characteristics were found to strongly depend on the various conditions of voltage pulse parameters including its amplitude, width, and interval time. In particular, linear and symmetric weight updates for accurate learning and pattern recognition could be achieved by adopting incremental step pulse programming (ISPP) operation scheme which rendered a high-resolution dynamic range with linear and symmetry weight updates as a consequence of precisely controlled filament geometry. A two-layer perceptron neural network simulation with HfO 2 /HfO 2−x synapses provided an 80% recognition accuracy for handwritten digits. The development of oxide/suboxide hafnium oxide memristive devices has the capacity to drive forward the development of efficient neuromorphic computing systems.

In this study, we propose an explicit topology optimization approach for multi-material composite structures that considers both geometric and material nonlinearities. Our method identifies each material using moving morphable components, resulting in explicit geometric descriptions and fewer design variables. In finite-element analysis, redundant degrees of freedom are removed to prevent highly distorted elements and improve computational efficiency. A numerical example demonstrated the methodology’s validity and the importance of accounting for geometric and material nonlinearities when designing a multi-material structure. We also show that, even with the same objective function and structural volume, the optimal usage ratio of the constituent materials varies depending on the problem. Using the proposed method, optimized structures with superior performance that cannot be achieved with a single material can be obtained.

Topology optimization has been increasingly used in various industrial designs as a numerical tool to optimize the material layout of a structure. However, conventional topology optimization approaches implicitly describe the structural design and require additional post-processing to generate a manufacturable topology with smooth boundaries. To this end, this paper proposes a novel robust topology optimization approach to produce an optimized topology with smooth boundaries directly. A truncated Karhunen–Loeve expansion and a sparse grid collocation method are integrated with the explicit moving morphable components method for uncertainty representation and propagation, respectively. The performance of the proposed method is assessed on three numerical examples of continuum structures under loading and material uncertainties through comparison with several robust topology optimization approaches. Results show that the proposed method is superior to the benchmark methods in terms of the balance among robustness of the objective function, boundary smoothness, and computational efficiency.

This work aims to provide a comprehensive review of the continuum models of the phase behaviors of liquid crystal networks (LCNs), novel materials with various engineering applications thanks to their unique composition of polymer and liquid crystal. Two distinct behaviors are primarily considered: soft elasticity and spontaneous deformation found in the material. First, we revisit these characteristic phase behaviors, followed by an introduction of various constitutive models with diverse techniques and fidelities in describing the phase behaviors. We also present finite element models that predict these behaviors, emphasizing the importance of such models in predicting the material’s behavior. By disseminating various models essential to understanding the underlying physics of the behavior, we hope to help researchers and engineers harness the material’s full potential. Finally, we discuss future research directions necessary to advance our understanding of LCNs further and enable more sophisticated and precise control of their properties. Overall, this review provides a comprehensive understanding of the state-of-the-art techniques and models used to analyze the behavior of LCNs and their potential for various engineering applications.

Alterations in the intestinal microbial flora are known to cause various diseases, and many people routinely consume probiotics or prebiotics to balance intestinal microorganisms and the growth of beneficial bacteria. In this study, we selected a peptide from fish (tilapia) skin that induces significant changes in the intestinal microflora of mice and reduces the Firmicutes/Bacteroidetes ratio, which is linked to obesity. We attempted to verify the anti-obesity effect of selected fish collagen peptides in a high-fat-diet-based obese mouse model. As anticipated, the collagen peptide co-administered with a high-fat diet significantly inhibited the increase in the Firmicutes/Bacteroidetes ratio. It increased specific bacterial taxa, including Clostridium_sensu_stricto_1, Faecalibaculum, Bacteroides, and Streptococcus, known for their anti-obesity effects. Consequently, alterations in the gut microbiota resulted in the activation of metabolic pathways, such as polysaccharide degradation and essential amino acid synthesis, which are associated with obesity inhibition. In addition, collagen peptide also effectively reduced all obesity signs caused by a high-fat diet, such as abdominal fat accumulation, high blood glucose levels, and weight gain. Ingestion of collagen peptides derived from fish skin induced significant changes in the intestinal microflora and is a potential auxiliary therapeutic agent to suppress the onset of obesity.

Enrichment techniques that employ nonconforming mesh are effective in modeling structures with discontinuities because numerical issues regarding mesh quality are avoided. However, the accurate integration of the bilinear and linear forms on the discretized domain, which is required in the standard Galerkin-based finite element method, is computationally expensive due to the complexity of the enriched basis function. In this paper, we present a fast and accurate alternative method of numerical integration using nonlinear regression enabled by a multi-perceptron feedforward neural network. The relationship between an implicitly represented geometry and the quadrature rule derived from the moment fitting method is predicted by the neural network; the neural network-based regression model circumvents complex computation and significantly reduces the overall online time by avoiding expensive function evaluations. Through the selected numerical examples, we demonstrate the efficiency and accuracy of the current method, as well as the flexibility of the trained network to be used in different contexts.

Recently, topology optimization has drawn interest from both industry and academia as the ideal design method for additive manufacturing. Topology optimization, however, has a high entry barrier as it requires substantial expertise and development effort. The typical numerical methods for topology optimization are tightly coupled with the corresponding computational mechanics method such as a finite element method and the algorithms are intrusive, requiring an extensive understanding. This paper presents a modular paradigm for topology optimization using OpenMDAO, an open-source computational framework for multidisciplinary design optimization. This provides more accessible topology optimization algorithms that can be non-intrusively modified and easily understood, making them suitable as educational and research tools. This also opens up further opportunities to explore topology optimization for multidisciplinary design problems. Two widely used topology optimization methods—the density-based and level-set methods—are formulated in this modular paradigm. It is demonstrated that the modular paradigm enhances the flexibility of the architecture, which is essential for extensibility.

Structural failure of the polycrystalline material is influenced by the interaction between the crystal and their boundaries. Specifically, a ductile material such as copper exhibit the different mechanisms of failure depending on the direction of the crack propagation within the grain boundary. Such directional anisotropy is often studied based on Rice’s criteria, which has the analytic solution in the grain boundary with [110] rotation of the axis. In this work, we expand the study of such intergranular directionality to a propagation within [100] grain boundary. This work introduces the inherent bias found in the intergranular fracture of [100] grain boundaries, using molecular dynamics simulations. Later, such observation is shown to agree with the relative crack propagation velocities, and cohesive energies obtained at the crack tip vicinity. These anisotropic trends are lastly correlated with the detailed atomistic movements observed during structural failures. These findings are to be used in improving the simulation capability and predictability of crack propagation.