Artificial neurons are pivotal for neuromorphic hardware, but the development of compact and uniform devices remains challenging. Conventional volatile memristors suffer from abrupt switching, which hinders spatiotemporal consistency. In this study, we developed a two-terminal artificial neuron with intrinsic leaky integrate-and-fire (LIF) dynamics, eliminating the need for bulky capacitors or additional reset circuits and enabling exceptional compactness. Crucially, the device exhibited superior spatiotemporal uniformity across arrays compared to typical volatile memristors—which show abrupt transitions—achieved through gradual volatile switching. Combined theoretical and experimental analyses revealed that this behavior resulted from the controlled formation and self-rupture of pure oxygen vacancy–based conductive filaments, which were modulated by electric field and Joule heating. Neuronal dynamics, including the firing threshold and relaxation, were tuned by adjusting the input amplitude and frequency. To validate functionality, a two-layer spiking neural network leveraging these neurons was developed, which achieved 97.4% accuracy on MNIST classification, rivaling ideal LIF models even under noisy conditions. This highlights the remarkable noise tolerance of the device, which is crucial for real-world applications. This study elucidates filament-driven volatility mechanisms and establishes a scalable approach to energy-efficient neuromorphic systems, advancing the development of bio-inspired computing hardware.

This paper comprehensively reviews the development of millimeter-wave (MMW) and terahertz (THz) near-field imaging technologies, with an emphasis on the state of synthetic aperture radar (SAR)-based imaging technologies. Near-field imaging technologies are categorized into passive and active imaging modes, among which active imaging is favored because of its strong signal-to-noise ratio and three-dimensional (3D) reconstruction capabilities. This paper discusses SAR-based active imaging systems with various antenna array structures, including planar SISO (Single-Input Single-Output)-SAR, cylindrical SISO-SAR, planar MIMO (Multiple-Input Multiple-Output)-SAR and cylindrical MIMO-SAR. Specifically, the paper emphasizes the advancements in SISO-SAR and MIMO-SAR technologies, highlighting the advantages of MIMO-SAR in improving imaging speed and reducing costs. Finally, the paper provides a summary and outlook on SAR-based MMW and THz near-field imaging technologies.

Anode-free all-solid-state batteries (AFASSBs) have garnered considerable attention as promising next-generation energy storage systems, offering high energy density, enhanced safety, and improved cost efficiency by eliminating the lithium metal anode. However, these advantages are offset by critical challenges, most of which stem from issues at the solid electrolyte–electrode interface. These challenges include interfacial instability, non-uniform lithium deposition during initial charging, and void formation owing to volumetric changes during cycling. Meanwhile, thin-film AFASSBs (TF-AFASSBs), fabricated by implementing the anode-free design in thin-film battery systems, offer distinct advantages over conventional thin-film systems, including higher energy density and a more streamlined cell structure. Moreover, their fabrication using deposition-based approaches promotes better interfacial wettability, thereby enhancing contact between the solid electrolyte and current collector. Despite these advantages, however, TF-AFASSBs are often more susceptible to interfacial degradation and uneven lithium plating than their bulk-type counterparts. In response to these challenges, considerable research has focused on mitigating interfacial and mechanical failures in both AFASSBs and TF-AFASSBs. This review presents an overview of the core characteristics and key limitations of AFASSBs and discusses recent advances in material development and interfacial engineering. These insights aim to guide the development of effective strategies for meeting the performance and stability requirements of practical TF-AFASSBs.

The field of polymer science experiences a significant transformation through data-driven approaches, automated synthesis, and machine learning (ML) systems, which create next-generation polymers that form the core of polymer informatics. In this study, we evaluated polymer informatics through its predictive modeling, inverse design, and synthesis techniques. The technology has been discussed in detail, including its applications in materials development, the sustainable circular economy, polymer creation, biomaterials production, and additive manufacturing optimization. The combination of multitask deep neural networks, graph neural networks, and transformer-based architectures with SMILES strings and molecular graphs has enabled ML algorithms to predict thermal, mechanical, electrical, and optical properties with high performance. The field requires standardized data as its foundation, sourced from major databases (e.g., PoLyInfo and PI1M) to improve property predictions in computational systems. Researchers employ graph-based models, topological indices, and ML to design polymers via optimized closed-loop systems that track processes in real time. The development of polymer informatics faces two primary obstacles: researchers lack access to sufficient standardized, high-quality datasets, and they struggle to represent polymer structures effectively (e.g., BigSMILES). The upcoming development of polymer informatics will focus on self-driving laboratories, AI-based retrosynthesis, and large language models for synthesis planning.

The key application performances of magnetorheological fluids (MRFs), such as shear yield strength, sedimentation stability, zero-field viscosity, redispersibility, and durability, exhibit trade-off relationships. However, the traditional methods that change a single parameter, such as the special morphology, chemical composition, or interface properties, produce limited improvements in the comprehensive performance of MRFs. A multidirectional synergistic modification that combines particle composition, interface, morphology, and multiscale particle systems will be more conducive to the improvement of the comprehensive performance of MRFs. In this work, we develop a transformative flaky FeSiCr MRF, which exhibits high magnetic permeability, low remanence, and excellent wear resistance, via solvent-assisted ball-milling. The results demonstrate that, when compared with commercial MRFs, the shear yield strength, sedimentation stability, redispersibility, and durability of these bidisperse MRFs ultimately improve over the entire magnetic-field range. The specific mechanism of the enhancement in the comprehensive performance is clarified based on Brownian-dynamics simulations, interface synergy effect, and performance characterization of the particles-chain-like structure. The combination of superior comprehensive performance and simple manufacturing process further enhances the engineering applicability of the bidisperse-particle-system MRF.