Infomat最新文献

Stretchable epidermal electronics with stable electrical performance have been widely applied in numerous fields, including advanced medical therapy, wearable electronics, soft robotics, and human–machine interaction. However, conventional stretchable devices, which typically integrate a pliant substrate and a conductor, often encounter inferior electrical performance under sustained or intense stretching due to poor stretchability, limited permeability, and the notable disparity in Young's modulus between the substrate and the conductor. This mechanical discord intensifies problems such as reduced durability and inconsistent conductivity. In this work, we address these limitations by devising a liquid metal-based flexible conductor via an innovative direct coating method. This conductor, supported by an electrospun fiber nanomesh, reveals markedly enhanced permeability through a pre-stretch activation process. The resulting electrode demonstrates remarkable electrical conductivity reaching 3730 S cm⁻¹, superior permeability with a water vapor transmission rate of 40.2 g m⁻² h⁻¹, and extraordinary stretchability (>2000% strain), coupled with exceptional mechanical durability. The liquid metal fiber mat structure allows for the creation of breathable, on-skin electronics capable of long-term electrophysiological monitoring, rendering it ideal for continuous health monitoring applications.

Stretchable and moisture-permeable LM-SBS electrodes for high precise and long-term electrophysiological monitoring.

Neuromorphic computing provides a remarkably efficient and adaptable alternative to traditional computing architectures by embodying the impressive power efficiency and parallel processing capabilities of the human brain. However, the prevailing focus on integrate-and-fire mode in current artificial neurons fails to fully acknowledge the nuanced multifunctionality and adaptive characteristics, especially the temporally variable operating modes and spatial heterogeneity present in natural neurons. Here we report a spatiotemporal-specific artificial neuron implemented with a ferroelectric planar memristor, by engineering the inherent in-plane ferroelectricity of α-In₂Se₃ and the extensive regulation capability of the co-planar multi-electrodes. With enhanced information processing capabilities, the artificial neuron facilitates adjustable reservoir computing and reconfigurable 16 types of logic-gate operations, ultimately achieving precise speech recognition with an accuracy approaching 100%. Our work clearly demonstrates the benefits of spatiotemporal specificity in artificial neurons, and contributes to the advancement of more realistic neuromorphic computing systems.

Defect engineering in photocatalytic materials has garnered significant interest due to the considerable impact of defects on light absorption, charge separation, and surface reaction dynamics. However, a limited understanding of how these defects influence photocatalytic properties remains a persistent challenge. This review comprehensively analyzes the vital role of defect engineering for enhancing the photocatalytic performance, highlighting its significant influence on material properties and efficiency. It systematically classifies defect types, including vacancy defects (oxygen and metal vacancies), doping defects (anion and cation), interstitial defects, surface defects (step edges, terraces, kinks, and disordered layers), antisite defects, and interfacial defects in the core–shell structures and heterostructure borders. The impact of complex defect groups and manifold defects on improved photocatalytic performance is also examined. The review emphasizes the principal benefits of defect engineering, including the enhancement of light adsorption, reduction of band gaps, improved charge separation and movements, and suppression of charge recombination. These enhancements lead to a boost in catalytic active sites, optimization of electronic structures, tailored band alignments, and the development of mid-gap states, leading to improved structural stability, photocorrosion resistance, and better reaction selectivity. Furthermore, the most recent improvements, such as oxygen vacancies, nitrogen and sulfur doping, surface defect engineering, and innovations in heterostructures, defect-rich metal–organic frameworks, and defective nanostructures, are examined comprehensively. This study offers essential insights into modern techniques and approaches in defect engineering, highlighting its significance in addressing challenges in photocatalytic materials and promoting the advancement of effective and adaptable platforms for renewable energy and environmental uses.

Different from traditional software encryption, hardware encryption shows obvious advantages in AI information encryption application scenarios with high reliability and high security requirements. With the development of memristors, memristor-based hardware encryption attracted the interests of researchers in secure communication. Hafnium-based memristors have received widespread attention due to fast speed, low power consumption, and compatibility with CMOS technology. In this study, a HfAlO_x-based memristor with an ON/OFF ratio of >10⁴, an endurance characteristic of 10⁵ cycles, and a low operating voltage of 0.56 V/−0.135 V was proposed. Eight-level states were achieved and used to design a hardware encryption scheme through a neural network. Parallel information encryption operations of “S” “D” “U” were realized in a memristor array. By constructing an artificial neural network, the recognition rate of encrypted letters without/with memristor is 62.3% and 98.1%, respectively. The memristor-based encryption scheme further expands the choices and application prospects of hardware encryption.

Ferroelectric materials hold great potential for modulating two-dimensional (2D) materials to achieve electrically tunable homojunction (ETH). However, ETH based on conventional ferroelectrics encounters significant challenges attributed to the surface with dangling bonds and the associated depolarization field. Here, we introduce a novel 2D ETH device based on the anomalous interfacial effect between 2D layered ferroelectric CuCrP₂S₆ and ambipolar WSe₂, creating a versatile platform for nonvolatile memory and high-performance optoelectronic applications. The device capitalizes on the realization of ETH through a localized doping strategy facilitated by ferroelectric polarization-assisted charge trapping. When modulated to a p–n junction diode, the device showcases superior rectifying characteristics and high-performance self-powered photodetection, with a highest responsivity over 0.14 A·W⁻¹. Moreover, the nonvolatile ETH device enables a single device to implement complex optoelectronic logics of exclusive OR (XOR), OR, and not implication (NIMP) that can be reconfigured by light illumination. Compared to the traditional CMOS-based logics, the ETH device significantly reduces the transistor number by 87.5%, 83.3%, and 87.5% for XOR, OR, and NIMP, respectively. The successful demonstration of the ETH device based on 2D ferroelectric materials paves the way for the development of advanced and simplified photo-electric interconnected circuits.

The Ge-based MoS2 floating-gate phototransistor demonstrates excellent storage characteristics and bidirectional light response capabilities. Leveraging these features, wavelength extraction from visible to infrared is achieved at the device level, significantly reducing system complexity and enhancing image recognition efficiency.

The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information, with applications from financial services to information security. However, the physical separation of sensing, memory, and computational units in traditional biometric systems introduces severe decision latency and operational power consumption. Herein, an in-sensor reservoir computing (RC) system based on MoTe₂/BaTiO₃ optical synapses is proposed to detect and recognize the faces and fingerprints information. In optical operation mode, the device exhibits low energy consumption of 41.2 pJ, long retention time of 3 × 10⁴ s, high endurance of 10⁴ switching cycles, and multifunctional sensing-memory-computing visual simulations. The light intensity-dependent optical sensing and multilevel optical storage properties are exploited to achieve sunburned eye simulation and image memory functions. These nonlinear, multi-state, short-term storage, and long-term memory characteristics make MoTe₂/BaTiO₃ optical synapses a suitable reservoir layer and readout layer, with short-term properties to project complicated input features into high-dimensional output features, and long-term properties to be used as a readout layer, thus further building an in-sensor RC system for face and fingerprint recognition. Under the 40% Gaussian noise environment, the system achieves 91.73% recognition accuracy for face and 97.50% for fingerprint images, and experimental verification is carried out, which shows potential in practical applications. These results provide a strategy for constructing a high-performance in-sensor RC system for high-accuracy biometric identification.

The realization of intelligent tactile perception in robotic systems requires multifunctional sensors capable of mimicking the dual-mode sensing mechanisms of human skin. Herein, we present a biomimetic hydrogel-based sensor capable of dynamic tactile detection through triboelectric sensing and static pressure monitoring via ionic-supercapacitive sensing. The triboelectric unit achieves a peak voltage of 14.64 V, with <5% signal decay over 5000 s of cycling, enabling robust detection of transient interactions (e.g., tapping or sliding). Additionally, the ionic-supercapacitive unit exhibits a high sensitivity of 2.69 kPa⁻¹ between 0.8–28 kPa, a rapid response time of 0.5 s, and minimal signal drift of <5% during 7-day continuous operation, providing stable monitoring of static interactions (e.g., touching or pressing). By leveraging a multilayer perceptron neural network, a robotic hand equipped with a biomimetic hydrogel-based bimodal sensor demonstrates intelligent recognition of material types and hardness levels with a high accuracy of 98.5%. This study establishes a paradigm for high-performance electronic skins, which advances human-machine interfaces and artificial intelligence-driven robotics through biomimetic tactile perception.

Understanding the statistical behaviors from films to devices is crucial for performance prediction and materials innovation. Here, we present the first fully automated high-throughput experimental platform for metal-halide perovskite research in China, integrating solution preparation, film fabrication, electrode evaporation, and comprehensive optical/optoelectronic characterization. This platform enables human-interference-free data collection with high repeatability, facilitating reliable statistical analysis. Through systematic investigation of over 1000 perovskite samples, we first identify the key factor of solvent atmosphere affecting experimental repeatability, and then introduce a super-absorbent resin to effectively mitigate solvent-related variability. By quantitative tracking of statistical distributions across the film-to-device transformation, we reveal that the deposition of charge transport layers also alters the bulk properties of perovskite films, as manifested by statistical changes in bandgap and Urbach energy. Finally, we develop a machine learning-based predictive model that links thin-film optical features to device performance, demonstrating the feasibility of AI-driven approaches to accelerate the evolution of perovskite materials.

Handwriting identification is widely accepted as scientific evidence. However, its authenticity is questioned because it depends on the appraiser's professional skills and susceptibility to deliberate false identification by expert witnesses. Consequently, there is an urgent need for an effective handwriting identification system (HWIS) that reduces reliance on the appraiser's skills and mitigates the risk of international false identification. Here, we report a HWIS that integrates a self-powered handwriting signal data acquisition device with an advanced deep learning architecture possessing powerful feature extraction ability and one-class classification function. The device successfully captures the characteristic differences in handwriting behavior between genuine writers and forgers, and the handwriting identification results demonstrate the excellent performance of our system, showcasing its powerful potential to solve the longstanding challenge of handwriting identification that has perplexed humans for a considerable period. Moreover, this work exhibits the system's capability for remote access and downloading the handwriting signal data through the data cloud, highlighting its practical value for fulfilling the requirements of handwriting recognition and identification applications, and it can effectively advance signature information security and ensure the protection of private information.