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Abstract Surface acoustic waves (SAWs) propagate along solid-air, solid-liquid, and solid-solid interfaces. Their characteristics depend on the elastic properties of the solid. Combining transmission electron microscopy (TEM) experiments with molecular dynamics (MD) simulations, we probe atomic environments around intrinsic defects that generate SAWs in vertically stacked two-dimensional (2D) bilayers of MoS₂. Our joint experimental-simulation study provides insights into SAW-induced structural and dynamical changes and thermomechanical responses of MoS₂bilayers. Using MD simulations, we compute mechanical properties from the SAW velocity and thermal conductivity from thermal diffusion of SAWs. The results for Young’s modulus and thermal conductivity of an MoS₂monolayer are in good agreement with experiments. The presence of defects, such as nanopores which generate SAWs, reduces the thermal conductivity of 2D-MoS₂by an order of magnitude. We also observe dramatic changes in moiré patterns, phonon focusing, and cuspidal structures on 2D-MoS₂layers. 

Abstract The use of transmission electron microscopy (TEM) to observe real-time structural and compositional changes has proven to be a valuable tool for understanding the dynamic behavior of nanomaterials. However, identifying the nanoparticles of interest typically require an obvious change in position, size, or structure, as compositional changes may not be noticeable during the experiment. Oxidation or reduction can often result in subtle volume changes only, so elucidating mechanisms in real-time requires atomic-scale resolution orin-situelectron energy loss spectroscopy, which may not be widely accessible. Here, by monitoring the evolution of diffraction contrast, we can observe both structural and compositional changes in iron oxide nanoparticles, specifically the oxidation from a wüstite-magnetite (FeO@Fe₃O₄) core–shell nanoparticle to single crystalline magnetite, Fe₃O₄nanoparticle. Thein-situTEM images reveal a distinctive light and dark contrast known as the ‘Ashby-Brown contrast’, which is a result of coherent strain across the core–shell interface. As the nanoparticles fully oxidize to Fe₃O₄, the diffraction contrast evolves and then disappears completely, which is then confirmed by modeling and simulation of TEM images. This represents a new, simplified approach to tracking the oxidation or reduction mechanisms of nanoparticles usingin-situTEM experiments. 

Abstract Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag₂S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag₂S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag₂S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors. 

Abstract Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed. 

We investigated surface acoustic wave (SAW) propagation and lattice vibrations in two-dimensional (2D) titanium carbide $\left({\mathrm{Ti}}_3{\mathrm{C}}_2{T}_{\mathrm{x}}\right)$MXene films as a function of surface termination and layer stacking, using atomistic simulations. We found that SAW propagation velocity is highly sensitive to both single-layer properties and interlayer bonding. Surface terminations significantly modulate wave behavior, with oxygen and fluorine terminations producing distinct effects on wave propagation, with oxygen-terminated monolayers exhibiting 20% higher wave speeds than fluorine counterparts due to strengthened intralayer bonds. Key observations include the transition from one to two layers causing wave speed variations, and the development of interlayer modes that generate more dispersed lattice vibrations. As the film layer thickness increases, SAW propagation becomes predominantly confined to the upper surface, with coherence of vibrational modes diminishing in multilayer structures. These findings suggest MXene terminations and layer stacking are crucial parameters for controlling SAW behavior, offering promising avenues for novel acoustic wave device applications. Published by the American Physical Society2025 

Robot swarms offer significant potential for inspecting di- verse infrastructure, ranging from bridges to space stations. However, effective inspection requires accurate robot localization, which demands substantial computational resources and limits productivity. Inspired by biological systems, we introduce a novel cooperative localization mech- anism that minimizes collective computation expenditure through self- organized sacrifice. Here, a few agents bear the computational burden of localization; through local interactions, they improve the inspection pro- ductivity of the swarm. Our approach adaptively maximizes inspection productivity for unconstrained trajectories in dynamic interaction and environmental settings. We demonstrate the optimality and robustness using mean-field analytical models, multi-agent simulations, and hard- ware experiments with metal climbing robots inspecting a 3D cylinder. 

Cyber Physical Systems (CPS) consist of integration of cyber and physical spaces through computing, communication, and control operations. In vehicular CPS, modern vehicles with multiple Electronic Control Units (ECUs) and networking with other vehicles help autonomous driving. Vehicular CPS is vulner-able to multitude of cyber attacks, including false data injection attacks. This paper presents an Asynchronous Federated Learning (AFL) with a Gated Recurrent Unit (GRU) model for identifying False Data Injection (FDI) attacks in a VCPS. The AFL model continuously monitors the network and constructs a digital twin using the data obtained from a VCPS for intrusion detection. The proposed model is evaluated using different evaluation metrics. Numerical results show that the AFL model outperforms other existing models. 

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.