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Suitable bandgap, high solar-blind light sensitivity, and high stability against harsh environments make Ga₂O₃ a promising candidate in the application of solar-blind photodetectors. However, Ga₂O₃ photodetectors, particularly those dominated by the photoconductive effect, inevitably face a trade-off between photoresponsivity and response speed. Common methods to mitigate this trade-off usually improve one aspect with the compromise of another. In this work, bilayer-structure Ga₂O₃ films are adopted for solar-blind photodetectors to alleviate the trade-off of photoresponsivity and response speed. The performance improvement effect of the bilayer-structure device is credited to its favorable modulation of carrier redistribution between two layers and extraction accessibility by the electrode. Through further optimization of film crystallinity by annealing, the bilayer-structure device acquires improved photoresponse performance, including a low dark current of 1.16 pA, a high photo to dark current ratio of 3.49 × 10⁷, a high R of 236.10 A W^–1, a high rejection ratio (R_254nm/R_365nm) of 1.98 × 10⁵, and a fast decay speed of 50 ms. Such excellent comprehensive performance ranks it into the top level among similar Ga₂O₃ photodetectors dominated by the photoconductive effect. This work provides a universal and facile design to mitigate the trade-off between photoresponsivity and response speed of Ga₂O₃ solar-blind photodetectors.

As social networks and related data processes have grown exponentially in complexity, the efficient resolution of combinatorial optimization problems has become increasingly crucial. Recent advancements in probabilistic computing approaches have demonstrated significant potential for addressing these problems more efficiently than conventional deterministic computing methods. In this study, we demonstrate a highly durable probabilistic bit (p-bit) device utilizing two-dimensional materials, specifically hexagonal boron nitride (h-BN) and tin disulfide (SnS₂) nanosheets. By leveraging the inherently stochastic nature of electron trapping and detrapping at the h-BN/SnS₂ interface, the device achieves durable probabilistic fluctuations over 10⁸ cycles with minimal energy consumption. To mitigate the static power consumption, we integrated an active switch in series with a p-bit device, replacing conventional resistors. Furthermore, employing the pulse width as the control variable for probabilistic switching significantly enhances noise immunity. We demonstrate the practical application of the proposed p-bit device in implementing invertible Boolean logic gates and subsequent integer factorization, highlighting its potential for solving complex combinatorial optimization problems and extending its applicability to real-world scenarios such as cryptographic systems.

High-entropy alloys (HEAs), which are near-equimolar alloys of four or more metal elements, have long been used to achieve the desired properties of catalytic materials. However, a novel alloying approach that includes multiple principal elements at high concentrations to generate HEAs as novel catalytic materials has been reported. The fabrication of well-defined ultrastable supported HEAs, which provide superior performance and stability of catalysts owing to their augmented entropy and lower Gibbs free energy, remains a critical challenge. Supported HEA catalysts are sophisticated because of the variety of their morphologies and large sizes at the nanoscale. To address these challenges, PtPdInGaP@TiO₂, comprising five different metals, is prepared via ultrasonic-assisted coincident electro-oxidation–reduction precipitation (U-SEO-P). The electronic structure and catalytic performance of HEA nanoparticles (NPs) are studied using hard scanning transmission electron microscopy (STEM), which is the first direct observation of the electronic structure of HEA NPs. This research takes an important step forward in fully describing individual HEA NPs. Combining STEM with deep learning with convolutional neural network (CNN) of selected individual HEA NPs reveals significant aspects of shape and size for widespread and commercially important PtPdInGaP@TiO₂ NPs. The proposed method facilitates the detection and segmentation of HEA NPs, which has the potential for the development of high-performance catalysts for the reduction of organic compounds.

The interfacial engineering in solid-state lithium batteries (SSLBs) is attracting escalating attention due to the profoundly enhanced safety, energy density, and charging capabilities of future power storage technologies. Nonetheless, polymer/ceramic interphase compatibility, serious agglomeration of ceramic particles, and discontinuous ionic conduction at the electrode/electrolyte interface seriously limit Li⁺ transport in SSLBs and block the application and large-scale manufacturing. Hence, garnet Li₇La₃Zr₂O₁₂ (LLZO) nanoparticles are introduced into the polyacrylonitrile (PAN) nanofiber to fabricate a polymer-ceramic nanofiber-enhanced ultrathin SSE membrane (3D LLZO-PAN), harnessing nanofiber confinement to aggregate LLZO nanoparticles to build the continuous conduction pathway of Li⁺. In addition, a novel integrated electrospinning process is deliberately designed to construct tight physical contact between positive electrode/electrolyte interphases. Importantly, the synergistic effect of the PAN, polyethylene oxide (PEO), and lithium bis((trifluoromethyl)sulfonyl)azanide (LiTFSI) benefits a stable solid electrolyte interphase (SEI) layer, resulting in superior cycling performance, achieving a remarkable 1500 h cycling at 0.2 mA cm⁻² in the Li|3D LLZO-PAN|Li battery. Consequently, the integrated polymer-ceramic nanofiber-enhanced SSEs simultaneously achieve the balance in ultrathin thickness (16 μm), fast ion transport (2.9 × 10⁻⁴ S cm⁻¹), and superior excellent interface contact (15.6 Ω). The LiNi_0.8Co_0.1Mn_0.1O₂|3D LLZO-PAN|Li batteries (2.7–4.3 V) can work over 200 cycles at 0.5 C. The pouch cells with practical LiNi_0.8Co_0.1Mn_0.1O₂||Li configuration achieve an ultrahigh energy density of 345.8 Wh kg⁻¹ and safety performance. This work provides new strategies for the manufacturing and utilization of high-energy-density SSLBs.

Various forms of intelligent light-controlled soft actuators and robots rely on advanced material architectures and bionic systems to enable programmable remote actuation and multifunctionality. Despite advancements, significant challenges remain in developing actuators and robots that can effectively mimic the low-intensity, wide-wavelength light signal sensing and processing functions observed in living organisms. Herein, we report a design strategy that integrates light-responsive artificial synapses (AS) with liquid crystal networks (LCNs) to create bionic light-controlled LCN soft actuators (AS-LCNs). Remarkably, AS-LCNs can be controlled with light intensities as low as 0.68 mW cm⁻², a value comparable to the light intensity perceivable by the human eye. These AS-LCNs can perform programmable intelligent sensing, learning, and memory within a wide wavelength range from 365 nm to 808 nm. Additionally, our system demonstrates time-related proofs of concept for a tachycardia alarm and a porcupine defense behavior simulation. Overall, this work addresses the limitations of traditional light-controlled soft actuators and robots in signal reception and processing, paving the way for the development of intelligent soft actuators and robots that emulate the cognitive abilities of living organisms.

Developing cost-effective and highly efficient oxygen evolution reaction (OER) electrocatalysts that operate in both acidic and alkaline media is crucial for industrial electrocatalytic water splitting. However, achieving high performance under dual pH conditions remains a significant challenge. Herein, we report the synthesis of multi-sized RuO₂ sub-nanoclusters on Co₃O₄ nanoarrays via a facile method, which demonstrates exceptional OER activity in both acidic and alkaline environments. The optimized catalyst exhibits remarkably low overpotentials of 165 mV in 0.5 M H₂SO₄ and 223 mV in 1 M KOH at a current density of 10 mA cm⁻², respectively. Additionally, it exhibits outstanding stability, maintaining performance over a 10-h continuous operation, which is attributed to the robust structural stability of the dispersed RuO₂ sub-nanocluster morphology. Atomic-scale investigations reveal a layer-by-layer growth mechanism of Ru on the Co₃O₄ substrate, transitioning from single atoms to monolayer clusters and ultimately to sub-nanoclusters as Ru loading increases. This growth mechanism provides a rational strategy for the precise design and synthesis of advanced cluster-based catalysts. Density functional theory (DFT) calculations further elucidate the strong oxide-support interactions between RuO₂ clusters and the Co₃O₄ matrix, facilitating electron transfer from RuO₂ to Co₃O₄ and generating an electron-deficient region. This electronic modulation enhances –OH adsorption and accelerates OER kinetics. These findings underscore the potential of metal sub-nanoclusters for designing highly efficient and durable electrocatalysts for water electrolysis.

The large-scale touch position sensor as a key human–machine interface toolkit holds immense significance in smart city and home construction. However, prior alternatives suffer from high power consumption, material limitations, and implementation costs. Herein, a self-powered and scalable touch position strategy that integrates contact electrification with a screen-printing technique is proposed. Simply, high-impedance electrodes with stagger patterns are screen-printed onto various substrates before being covered with a dielectric layer. The locating mechanism originates from the touch-generated triboelectric charge shunt effect in the electrodes. The screen-printing parameters that affect the positional accuracy are discussed in detail. Leveraging this strategy, we realize a tailorable and large-scale triboelectric touch position sensor (LTTPS) that offers flexibility, self-powered capability, and a minimized signal channel, making it suitable for various practical scenarios. Demonstrations include an intelligent bookshelf mat with book management functionality, a rollable and foldable film-like keyboard, and a 4 m² walk-tracking carpet. The LTTPS in this work provides an appealing alternative for large-scale touch positioning and enriches human–machine interaction.

MXenes, a class of two-dimensional (2D) transition metal carbides, and covalent organic frameworks (COFs) deliver unique structural and electrochemical properties, making them promising candidates for energy storage and conversion applications. MXenes exhibit excellent conductivity and tunable surface chemistries, whereas the COFs provide high porosity and structural versatility. Recent advances in integrating MXene-COF composites have revealed their potential to enhance charge transfer and energy storage/conversion properties. The work highlights key developments in MXene-COF integration, offering insights into their applications in batteries (Li-ion, K-ion, Na-ion, and Li-S), supercapacitors, and electrocatalysis (HER, OER, RR, NRR, and ORRCO2), while also addressing current challenges and future directions for not only energy conversion but also other electronic devices.

Lithium-ion batteries (LIBs) have dominated the market for a long time. However, the scarcity of lithium resources has sparked concerns about future energy storage devices, leading many researchers to turn their attention to other energy storage devices, such as sodium-ion batteries (SIBs), potassium-ion batteries (KIBs), zinc-ion batteries (ZIBs), and so on. Among them, SIBs have attracted widespread attention from researchers due to their abundant sodium resources, high safety, and excellent low-temperature performance. Because the cathode of the battery determines the energy density, cycle life, charge/discharge rate, and cost, the research on the cathodes for SIBs is particularly important. Layered oxide cathodes, with their periodic layered structure, good electrical conductivity, and two-dimensional ion transport channels, are regarded as the most promising cathode materials for SIBs. Currently, the main issues facing layered oxide cathodes include irreversible phase transitions, high air sensitivity, insufficient energy density, surface residual alkali, and the migration and dissolution of transition metals. The key to solving these problems lies in the development of a new generation of high-performance layered oxide cathodes. Hence, we review the current research progress of layered oxide cathode materials for SIBs and various optimizing strategies, and finally summarize and provide an outlook on the future development trends of SIBs.

Respiratory muscle training can improve respiratory function by strengthening muscle mass, which is of great help to populations with respiratory system diseases and athletes. Existing respiratory muscle training methods rely on resistance that hinders breathing, and the resistance cannot be adjusted automatically. However, the detection of the user's current muscle fatigue state and precise adjustment of resistance during respiratory muscle training are crucial to training efficiency. Here, we have developed a hybrid sensor that combines a triboelectric nanogenerator and a piezoelectric nanogenerator. This hybrid sensor can simultaneously collect both high-frequency and low-frequency signals generated by the Karman vortex street effect with low hysteresis. When the airway height is 30 mm, the sensor size is 52 μm × 40 mm × 17 mm, the output performance of the sensor is optimal, and the minimum response amplitude for the sensor is approximately 3 mm. Under normal breathing conditions, the output peak voltage is 7 V, the current is 100 μA, the charge transfer amount generated by one movement is 55 nC, the response time is 0.16 s, and the sensitivity is 0.07 V/m·s⁻¹. With the help of the principal component analysis algorithm, features related to the fatigue state of muscles were extracted from the collected signals, and the accuracy rate can reach 94.4%. Subsequently, the stepper motor will rotate to adjust the resistance appropriately. We fused the hybrid sensor, machine learning, control circuits, and stepper motors and fabricated a resistance self-adaptation program. Our findings inspire researchers in the field of rehabilitation and sports training to evaluate training status and improve training efficiency.