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.

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.

The rapid advancements in humanoid robotics and autonomous driving demand smart artificial optoelectronic vision systems that can emulate human-like perception. Although many studies have reported multi-functional visual chips based on artificial optoelectronic synaptic devices, few can simulate complex behavioral characteristics of humans, like specific living habits and physiological adaptations. In this study, we demonstrated MoS₂ optoelectronic synapses capable of exhibiting tunable human-like visual adaptation abilities under various alcohol concentrations, featuring remarkable photo-induced conductance plasticity for emulating alcohol-sensitive human visual recognition. Two working mechanisms involving hydrogen-atom and oxygen-atom doping were unveiled during the concentration-dependent doping process. The visual adaptation abilities were systematically explored by controlling the doping concentration of alcohol molecules, and were further enhanced by electric and optoelectronic stimuli to emulate human-like behaviors, such as slight drunkenness, heavy drunkenness, and sobering up. Under the influence of alcohol molecules and the modulation of device operating voltage, the accuracy of handwritten digit recognition for this device has greatly increased from 78.9% to 94.7%.

High-power broadband near-infrared (NIR) light sources have attracted extensive interest toward emerging non-invasive imaging and detection applications. However, exploring highly stable luminescent materials with targeted broadband NIR emission remains a great challenge. Here, MgAl₂O₄:Cr³⁺ translucent ceramics have been designed and fabricated by a spark plasma sintering method, and a giant redshift of the emission band occurs from 686 to 928 nm due to the decreasing local structural symmetry around the isolated Cr³⁺ ions induced by the abundant cation vacancies. As Cr³⁺ content increases, MgAl₂O₄:6%Cr³⁺ ceramic realizes the optimized external quantum efficiency of 73% with broadband NIR emission centered at 890 nm and a full-width at half-maximum of 315 nm under 450 nm excitation. The next-generation laser-driven light source containing NIR ceramic provides an output power exceeding 2 W and a light conversion efficiency of 22% when pumped with a blue laser of 10 W·mm^–2. The proof-of-concept demonstrations in imaging and detection reveal the advantages of high-power and high-efficiency laser-driven broadband NIR light sources and promote future development in the chemical design of NIR emitters.

The resistance and immune evasion of methicillin-resistant Staphylococcus aureus (MRSA) in biofilms are the culprits behind persistent infections. There is an urgent need for safe and effective antibacterial strategies to address MRSA and biofilm-related infections. Herein, we propose the development of an all-in-one optical microfiber that integrates rapid quantitative analysis with synergistic antimicrobial therapy for deep-seated MRSA in biofilms. The prepared interfacial-functionalized sensor can be used for quantitative analysis of MRSA in clinical whole-blood samples with low volumes (10 μL), reducing the detection time to 30 min and effectively preventing false-positive and false-negative results. The sensor can also be used for multimode antimicrobial therapy. This one-time treatment accelerates recovery and prevents recurrence through the synergistic effect of photothermal therapy, photodynamic therapy, and the antibacterial effect of Ag⁺, as well as the activation of immune memory. The therapy is localized with relatively low hyperthermia and does not cause harm to the surrounding healthy tissues. The integration of therapeutic agents onto the optical microfiber precludes their enrichment in other organs. The light guided through the optical fiber can reach deep-seated biofilms, which other light sources fail to reach. This work is promising for the clinical diagnosis and treatment of deep-seated infections.

Printed electronics technology, characterized by its low cost, large-area compatibility, operational simplicity, and high-speed processing, has been extensively utilized in the fabrication of flexible electronic devices. Liquid metals, with their exceptional electrical conductivity and room-temperature fluidity, are considered ideal materials for the development of flexible and stretchable electronics. However, the adhesion mechanisms at the interface between liquid metals and substrates, a fundamental aspect of liquid metal-based printed electronics, have not been comprehensively explored in the existing literature. This review first introduces the fundamental properties of liquid metals and their adhesion mechanisms to various substrates, followed by a summary of printing technologies designed to enhance or reduce substrate adhesion. Additionally, techniques for printing on non-adhesive substrates through material modification, as well as methods for achieving detachment on adhesive substrates by controlling interfacial properties, are demonstrated. Finally, future research challenges and developmental trends in materials, methods, equipment, and applications are discussed. This review provides a comprehensive understanding of the interfacial adhesion effects between liquid metals and substrates, offering valuable insights for printing on a wide range of substrates, including plastics, silicones, paper, and even biological surfaces.

Underwater strain sensors are crucial for marine exploration, amphibious robotics, and aquatic dynamic monitoring. However, frequent dry–wet transitions in practical applications can lead to structural degradation and sensitivity loss, limiting their long-term stability. Traditional designs relying on waterproof or hydrophobic layers isolate the core structure from water but suffer from interface delamination and performance decline during dry–wet cycles. Additionally, these layers increase weight, restricting lightweight and flexible applications. Herein, we developed a novel fiber-based underwater strain sensor by coaxially spinning cuprammonium rayon (CR) and Ti₃C₂T_x. A “water-compatible” strategy was introduced to overcome the limitations of traditional “water-repellent” approaches by leveraging molecular-level material design. Ammonium ions in the cuprammonium spinning solution induce MXene gelation, forming a compact core–shell interface. CR's amorphous regions' hydroxyl and amino groups establish dynamic hydrogen bonds with water, enhancing interfacial bonding, mechanical strength, and wet sensitivity. During dry–wet cycles, the water network stabilizes the wet structure and facilitates rapid water release upon drying, restoring molecular interactions to maintain mechanical strength and conductivity. This sensor combines high strength, excellent wet sensitivity, and stable dry conductivity with exceptional adaptability to cycling. It offers a lightweight, high-performance, multifunctional solution for underwater sensing in low-latitude high-humidity environments, ensuring broad applicability.

As demand for customized wearable electronics grows, free-form Li-ion batteries (LIBs) are attracting significant attention. Although substantial advancements have been made in printed LIBs for shape-versatile electronics, the development of printable solid-state electrolytes remains challenging due to the difficulty of simultaneously achieving desirable rheological properties and ionic conductivity. In this study, a solvent-free, non-flammable solid polymer electrolyte (SPE) is designed as a novel three-dimensional (3D) printable electrolyte via direct ink writing (DIW) for all-solid-state batteries (ASSBs). The solvent-free nature of this SPE eliminates post-annealing steps, enhancing safety by mitigating risks of leakage, short-circuiting, and fire. Additionally, precise control over polymer molecular weight and electrolyte composition enables high printing resolution (~100 μm), high ionic conductivity (0.705 mS cm^–1 at 25°C), and intrinsic non-flammability. A 3D-printed ASSB, featuring a LiFePO₄ cathode and Li₄Ti₅O₁₂ anode with a mass loading of 7 mg cm^–2, achieves a high areal capacity of 1.14 mAh cm^–2, surpassing all previously reported directly printed ASSBs. This SPE facilitates scalable production of fully DIW-printed ASSBs with superior design flexibility and space efficiency, enabling printing onto customized targets such as flexible substrates and advancing the development of next-generation wearable electronics.

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.