Neuromorphic computing demands artificial synaptic arrays that combine low power consumption, scalability, and full compatibility with semiconductor manufacturing. However, the development of electrolyte-gated transistors (EGTs) into wafer-scale synaptic systems has been hindered by the poor stability and CMOS incompatibility of conventional liquid or polymer electrolytes. Here, we demonstrate a brain-inspired all-inorganic synaptic transistor array integrating low-cost ZnSnO (ZTO) channels with LiPON solid-state electrolytes, both deposited in a CMOS-compatible process. The 6 x 6 crossbar array exhibits exceptional uniformity, high endurance (12 500 switching cycles), and dynamic synaptic plasticity, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/LTD). Notably, the devices achieve an ultra-high PPF index of 305%, symmetric conductance modulation, and negligible drift after repeated cycling. System-level validation demonstrates the functional relevance of the array: offline training combined with hardware-aware inference achieves 97.0% and 86.7% accuracy on the MNIST and Fashion-MNIST datasets, respectively. 96.0% accuracy in convolutional neural network (CNN) simulations for human action recognition. Furthermore, electromyography (EMG) signal classification improves from 88.4% to 96.5%, highlighting its practical potential in neuromorphic sensing interfaces. By combining a ZTO channel with an inorganic electrolyte, this work establishes a CMOS-compatible and scalable materials platform, providing a practical pathway toward system-level neuromorphic applications.

Abstract Although reconfigurable van der Waals devices featuring flexible logic transformation offer a promising strategy toward adaptable architectures to accommodate diverse computational demands, reliable polarity control and scalable integration remain challenging. Here, we demonstrate a reconfigurable field-effect transistor based on the scalable dielectric oxide-van der Waals quasi-floating-gate configuration, enabling nonvolatile polarity switching and multi-state programmability. Charge trapping engineering in an atomic-layer Al 2 O 3 /HfO 2 /Al 2 O 3 dielectric stack achieves performance with nonvolatile conductance update (>6-bits for 1000 s), robust endurance (>3 × 10 5 cycles), and well-balanced electron/hole transport (current mismatch ratio ~ 1%). TCAD simulation and surface potential analysis reveal oxygen vacancies-dominated polarity switching dynamics. Using a silicon-compatible top-gate dielectric process and complementary design, diverse logic gates—including eight Boolean operations and seamless AND-OR-Invert/OR-AND-Invert transformations—are accommodated into compact reconfigurable logic-in-memory circuits. These transistors also simplify ternary content-addressable memory design, underscoring their potential for efficient logic-in-memory computing.

Scaling the density of epidermal sensor matrices while maintaining sub-1 V driving voltages remains a challenge for achieving high-fidelity, skin-compatible electrophysiological interfaces. We present a 10 × 10 flexible amorphous indium-gallium-zinc oxide (a-IGZO) thin-film transistor (TFT) array gated by a novel chitosan/ionic liquid@covalent organic framework (CS/IL@COF) ion-gel dielectric. This composite enhances ionic conductivity, enabling sub-1 V operation, high transconductance (0.65 mS), a steep subthreshold swing (103 mV·dec-1), and exceptional mechanical stability. Operating as a skin-conformable interface, the array enables concurrent acquisition of electromyography (EMG) and electrocardiography (ECG) signals. Notably, it supports real-time muscle fatigue assessment by tracking the progressive downshift of median frequency (MF) and mean power frequency (MPF) during sustained contractions. To decode multichannel EMG signals, we propose a deep learning architecture integrating a Linear Mapping Network (LMN) and an Inception Time Module (ITM). The LMN performs spatial channel recombination, while the ITM extracts multi-scale temporal features, achieving 99.2% classification accuracy across 11 hand gestures. This low-voltage, hardware-software co-design framework paves the way for advanced wearable bioelectronics in healthcare and human-machine interaction.

Emerging ferroelectric non-volatile memories are revolutionizing von Neumann architectures by providing efficient hardware for both AI training and inference. However, as ferroelectric dimensions scale toward the nanoscale, reliable modulation is hindered by interfacial degradation and phase instability, leading to synaptic weight drift and computational inaccuracies. Here, a high-performance ferroelectric-van der Waals transistor (FeFET) for computing-in-memory by integrating a single-crystalline Bi2O2Se (BOS) layer into a ferroelectric/MoS2 heterostructure is demonstrated. The implementation of an asymmetrical capacitive stack ensures effective polarization-charge compensation during fine-state switching, achieving precise multi-level weight programming with significantly suppressed carrier fluctuations. Fabricated through a low-temperature process, the BOS-based FeFET exhibits exceptional reliability, including 10-year retention at 85°C, endurance exceeding 1011 cycles, stable 32-state analog switching with 0.9% retention variation over 10 000 s, and ultra-low programming error. Atomically smooth heterointerfaces yield high spatial uniformity (7% variation) across the FeFET array, enabling a hardware neural network that achieves 98.5% accuracy in nonlinear classification. Furthermore, by incorporating intrinsic ferroelectric switching variance into the training phase, it is elucidated how device imperfections can be leveraged to reshape learning dynamics in pixel-wise semantic segmentation. This work establishes a comprehensive co-design methodology bridging advanced ferroelectric materials, device engineering, and algorithmic optimization for next-generation neuromorphic computing.

Ion-sensitive thin-film transistors (ISTFTs) face a fundamental tradeoff between pH sensitivity and pixel resolution. To overcome this limitation, this work presents a novel ISTFT with super-Nernstian sensitivity and ultrahigh resolution using a dual-gate (DG) architecture. By synergizing capacitive coupling amplification with oxygen plasma-enhanced surface-chemistry engineering, the DG ISTFT array achieves a sensitivity of 110 +/- 20 mV/pH. Both TCAD simulations and experimental characterization demonstrate that this amplification is dimension-independent, ensuring scalability for high-density integration. Consequently, a fully functional 800-ppi 256 & times; 256 active-matrix ISTFT array with integrated gate-driver circuits is realized, enabling real-time pH imaging. Furthermore, the platform serves as a parallel DNA biochip for loop-mediated isothermal amplification (LAMP) to realize label-free detection of multiple foodborne pathogens. This work provides a foundry-compatible path toward a high-throughput multiplexed ISTFT array, bridging advanced sensor design with practical on-chip bioanalytical applications.

To overcome the poor ductility and limited formability of conventional B4C-reinforced Al-based thermal-neutron absorption materials, processable Al-Si-Gd alloys with fracture elongations exceeding 15% were developed through cold rolling and annealing. Increasing Gd content transforms the second phases from spherical and rodlike Gd(AlSi)2 into larger plate-like GdAlSi, accompanied by the formation of a core-shell Gd(AlSi)2@GdAlSi structure due to Gd segregation and sequential solidification. Cold rolling fragments coarse plate-like and core-shell particles and redistributes fine particles into clustered lamellar regions, effectively refining and homogenizing the microstructure. This microstructural regulation promotes load transfer and GND-induced dislocation hardening, while reducing stress concentration and delaying void evolution. This study provides a new strategy for designing Al-Si-Gd alloys with improved mechanical performance and effective thermal-neutron absorption capability.

Simultaneously balancing high mobility and ultra-low leakage current is a major challenge for metal-oxide thin-film transistors (TFTs) in ultra-low-power applications. Herein, we construct a high-performance InGaZnO/InGaO/InGaZnO tri-layer TFT based on a noncoplanar Schottky-Ohmic hybrid contact architecture. Remarkably, despite utilizing identical ITO electrodes, differential interfacial engineering explicitly decouples carrier transport: the bottom interface forms a 670 meV Schottky barrier to strictly suppress off-state leakage, while the top interface ensures low-resistance Ohmic extraction. Furthermore, a deep quantum potential well (ΔEc = 0.20 eV) formed between the high-impedance InGaZnO cladding layers and the highly conductive InGaO core strongly localizes carriers within the inner layer, constructing an ultra-low-scattering two-dimensional transport pathway. The device achieves an ultrahigh on/off current ratio exceeding 1010, together with a high field-effect mobility of 28 cm2/V s and a steep subthreshold swing of 120 mV/dec. The TFT also exhibits excellent bias stability, with a VTH shift of only 0.8 V under ± 20 V gate stress for 3600 s. Unipolar depletion-load inverters based on this architecture deliver full-swing operation and a maximum voltage gain of 55. These findings establish noncoplanar Schottky-Ohmic contacts as a powerful strategy to break the long-standing mobility-leakage trade-off, offering a scalable pathway toward low-power, high-performance oxide electronics for advanced display backplanes and large-area integrated circuits.

The industrial advancement of white organic light-emitting diodes (WOLEDs) remains constrained by the inherent conflict between high manufacturing costs and stringent performance requirements. Here, we demonstrate a breakthrough in all-fluorescent WOLEDs by simultaneously addressing the intrinsic efficiency limitations of two-color emitters and the complexity of conventional fabrication. We systematically investigated two-color WOLEDs, uncovering the critical role of triplet-triplet annihilation in their efficiency degradation. To overcome these bottlenecks, we first designed and realized high-efficiency three-color WOLEDs incorporating sky-blue, deep-blue, and orange emission layers, based on 5tCzBN, DABNA-1, and 4CzTPNBu dopants in an mCBP bipolar host. Efficient Forster resonance energy transfer, enabled by 5tCzBN sensitization and 4CzTPNBu electron trapping, produced directional excitation of both blue and orange fluorophores, yielding state-of-the-art white electroluminescence with chromatic coordinates (0.326, 0.321). Importantly, we further introduced a planar source evaporation (PSE) technique, which replaces multicomponent microdoping with a material-efficient process, enabling low-cost fabrication of high-performance OLEDs. As a result, our three-color devices reached maximum external quantum efficiency (EQEmax) to 17.24%, while optimized three-color WOLEDs achieved a maximum current efficiency (CEmax) of 69.78 cd/A and EQEmax to 26.35%, alongside low turn-on voltage, high brightness, and an LT 50 exceeding 100,000 h (@ 100 cd/m2). By uniting mechanism-level insights with process-level innovation, this work establishes a cost-effective and scalable route for next-generation WOLEDs in lighting and display applications.

A flexible bi-directional bio-interface design using indium zinc oxide (IZO) thin-film transistors (TFTs) to share the same microelectrode array and electrical interconnects is proposed for direct electrical stimulation (DES) and bio-potential signals sensing. The oxide TFTs present a high current driving capability (mA level) for stimulation and a high ON-OFF ratio (>10(9)) for switching with good uniformity. An active-matrix pixel design is developed to provide higher stimulation current and also enable the fabricated microelectrode array to be highly robust and tolerant to customizable cutting for various wearable applications. Finally, a bio-interface system is built based on the flexible microelectrode array for electrocardiogram (ECG) and electromyography (EMG) tests on a mouse.

To overcome the embrittlement and limited structural reliability of B4C/Al-based composites, Mg- and Zn-containing Al-5Gd thermal-neutron absorbing alloys were fabricated. Mg and Zn additions promoted the formation of Al2Gd0.45Mg0.55 and Al6.3Gd3Zn4.7 phases, respectively. The annealed Al-5Gd-5Mg alloy achieved a tensile strength of 305 MPa with 15.4% elongation, while Al-5Gd-5Zn exhibited 22.2% elongation at 149 MPa. After TIG welding, all alloys fractured in the heat-affected zone and showed reduced strength and ductility. Monte Carlo simulations confirm that both ternary alloys provide higher thermal-neutron absorption efficiency compared to 30 wt.% B4C/Al composite. These results demonstrate that Al-Gd-Mg/Zn alloys provide a promising alloying route for lightweight neutron-shielding structural materials with improved mechanical property and weld-joint reliability.

ABSTRACT The oxidation behavior of Ti‐Gd based alloys was systematically investigated at 900°C for up to 100 h. The results show that the oxidation kinetics of all alloys follow a parabolic law. Gd addition accelerates oxidation and degrades oxidation resistance. Fe further exacerbates this effect by stabilizing the β‐Ti phase, in which oxygen diffuses faster than in α‐Ti. In contrast, Al addition significantly improves oxidation resistance by forming an Al 2 O 3 ‐rich barrier layer in the inner oxide scale. Ti‐7.5Fe‐5Gd‐5Al exhibits the best oxidation resistance. These findings provide a theoretical basis for understanding the high‐temperature oxidation behavior of Ti‐Gd based alloys and offer valuable reference for their engineering application as neutron‐absorbing structural materials at elevated temperatures.

To cope with the extreme corrosion environment of 50% H2SO4 at 80 degrees C in the waste sulfuric acid recovery systems, the corrosion behavior of the newly designed non-equiatomic FeCoCrNiMo medium entropy alloys with Cr/Mo ratios of 1.9 similar to 7.5 was investigated by immersion measurements, electrochemical techniques, and surface analyses. The results showed that corrosion resistance decreases with decreasing Cr/Mo ratio for the FeCoCrNiMo medium entropy alloys. The composition of the passive film formed on the FeCoCrNiMo medium entropy alloys mainly consisted of Cr2O3 and Cr(OH)(3) with incorporated Mo-4(+) and Mo6+ species and doped Fe and Co oxides. The optimized synergistic effect of Cr and Mo alloying elements generated a compact passive film with a high oxide content and a thick film thickness, resulting in excellent corrosion resistance of M1 (Fe10Co20Cr30Ni35Mo4Cu1) medium entropy alloy in a high-temperature dilute sulfuric acid environment.

Memristors have emerged as a transformative technology in the realm of electronic devices, offering unique advantages such as fast switching speeds, low power consumption, and the ability to sensor-memory-compute. The applications span across non-volatile memory, neuromorphic computing, hardware security, and beyond, prompting memristors to become a versatile solution for next-generation computing and data storage systems. Despite enormous potential of memristors, the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability, device reproducibility, and array scalability. This review systematically explores recent advancements in high-performance memristor technologies, focusing on performance enhancement strategies through material engineering, structural design, pulse protocol optimization, and algorithm control. We provide an in-depth analysis of key performance metrics tailored to specific applications, including non-volatile memory, neuromorphic computing, and hardware security. Furthermore, we propose a co-design framework that integrates device-level optimizations with operational-level improvements, aiming to bridge the gap between theoretical models and practical implementations.

Metal oxide thin-film transistors (TFTs) have garnered considerable attention as promising alternatives to liquid-crystal displays and organic light-emitting diode displays. Among different metal oxide semiconductors, zinc tin oxide (ZnSnO, ZTO) stands out as a promising option owing to its environmental friendliness, affordability, and suitability for low-temperature fabrication. Nevertheless, pristine ZTO TFTs exhibit considerable drawbacks in both performance and reliability, particularly when exposed to negative bias illumination (NBIS) and thermal stress, leading to pronounced threshold voltage variations. Here, we report an in-situ sulfur anion doping strategy to modulate the defect landscape of ZTO, yielding high-performance and highly stable S-doped ZTO (S-ZTO) TFTs. The incorporation of S anion at oxygen lattice sites effectively suppresses oxygen-vacancy–related trap states, leading to marked improvements in both carrier transport and operational stability. The optimized S-ZTO TFT (10 at% S) exhibits a field-effect mobility of 12.7 cm 2 (V·s -1 , a threshold voltage of 3.8 V, a subthreshold swing of 0.31 V dec −1 , and an ultralow leakage current of 7 × 10 −12 A. More importantly, in-situ sulfur incorporation significantly mitigates threshold voltage shifts under prolonged positive bias, NBIS, and thermal stress, outperforming pristine ZTO devices. This work demonstrates that in-situ sulfur doping offers a practical route to high-performance ZTO-based oxide TFTs that meet the reliability requirements of AMOLED and AM-LCD backplanes.

Surface electromyogram (sEMG) signals are valuable in healthcare and human-machine interaction. However, sEMG signals are inherently weak and unstable bioelectrical signals, rendering them highly susceptible to perturbations from various external factors. In this work, we firstly proposed utilizing the industrially producible Gen-4.5 heterogeneous integration technology to design an active 16-channel microelectrode array (MEA) based on amorphous indium–gallium–zinc oxide thin-film transistors (a-IGZO TFTs) capable of capturing and decoding sEMG signals. The a-IGZO TFTs demonstrate exceptional stability under bias (±20 V), temperature (200 °C), and bending (6 mm, 30 000 cycles), with a threshold voltage shift of less than 0.1 V and a standard deviation under 0.07 V for 100 randomly selected devices. Our state-of-the-art 16-channel active MEAs can collect sEMG signals from various hand gestures and analysis of motor unit action potential trains, expanding possibilities for human-machine interaction and electronic healthcare applications. The signal-to-noise ratio of sEMG signals reaches 85 dB, enabling a high average hand gesture recognition accuracy of 96.2%. This work highlights the potential of the scalable sEMG arrays with exceptional stability for multi-channel sEMG signal acquisition, representing a significant advancement in wearable health monitoring and interactive systems.

Complementary Field-Effect Transistor (CFET) technology is emerging as a critical point to extending Moore's Law by transitioning device scaling and integration from 2- to 3D architectures. Recent advancements, including silicon-based homogeneous CFETs and van der Waals-based heterogeneous CFETs, have demonstrated significant progress, yet a comprehensive and up-to-date review is absent to further advance the field. This work explores CFET fabrication methodologies, comparing the advantages and challenges of Monolithic and Sequential integration approaches, with a focus on thermal management, process complexity, and material compatibility. We highlight the critical role of layered van der Waals materials in addressing thermal constraints and enhancing gate control, leveraging their atomic-scale thickness and unique electronic properties. Furthermore, we discuss strategies to overcome key challenges such as achieving balanced electrical characteristics, optimizing thermal management, and minimizing parasitic capacitance through innovative channel engineering, gate-dielectric design, and structural optimization. The co-design principles of CFET architectures are also examined, showcasing their potential in logic circuits, memory units, and computing-in-memory systems. This review provides a forward-looking perspective on CFET technology, emphasizing the need for continued innovation in material-processing-structure co-design and co-optimization to unlock new frontiers in semiconductor technology.

Designing and developing a simple and reliable method for detecting disease biomarkers is crucial for early disease prevention and diagnosis. Fe-functionalized carbon quantum dot (Fe-CQD), synthesized from natural dandelion biomass through a controlled process, were developed as nanozymes with outstanding peroxidase-like activity. Uricase decomposes uric acid to release hydrogen peroxide (H2O2), which the Fe-CQD nanozymes catalyze to produce reactive oxygen species (ROS) such as singlet oxygen (1O2) and hydroxyl radicals (•OH). These ROS then oxidize the colorless substrate o-phenylenediamine (OPD) into yellow 2,3-diaminophenazine (DAP), enabling visual detection of uric acid. Building on this principle, we have developed, for the first time, a visual sensing platform using Fe-CQD nanozymes. This platform integrates absorption spectra, a microplate reader, and smartphone-assisted visible-light imaging to detect uric acid, achieving detection limits as low as 0.31, 0.36, and 0.83 μM, respectively. The platform demonstrated excellent accuracy in clinical blood sample analysis, yielding results highly consistent with standard hospital methods and the UA Assay Kit-based colorimetric method. It also exhibited robust anti-interference capabilities and practicality, making it a reliable tool for UA detection in real-world clinical settings. This innovative platform highlights the potential of nanozymes in biomarker detection and lays a theoretical foundation for integrating intelligent, portable sensing technologies into clinical diagnostics.

The advancement of flexible wearable technology has greatly improved health monitoring and medical diagnostics, particularly in bioelectrical signal detection. While traditional materials such as gold exhibit high conductivity and chemical inertness in bioelectrical signal sensor applications, they are constrained by factors including high costs, limited mechanical durability, and inadequate flexibility. This study proposes a novel multichannel bioelectrical signal sensor comprising silver nanoparticles (0D), carbon nanotubes (1D), and graphene nanosheets (2D). The bioelectrical signal sensor is patterned onto an adhesive polyurethane (PU) substrate, which ensures stability during complex movements, and incorporates a serpentine structure to effectively dissipate stress. Comprehensive electromyography (EMG) and electrocardiography (ECG) assessments confirm the bioelectrical signal sensor's superior signal quality, achieving a signal-to-noise ratio (SNR) of 41.63 dB during EMG acquisition and a gesture recognition accuracy of up to 96 %. Additionally, the bioelectrical signal sensor demonstrates consistent signal stability and accuracy under both static and dynamic conditions, establishing a robust basis for the further development of wearable health monitoring electronics.

Human visual recognition is profoundly affected by ambient relative humidity, yet current bionic and neuromorphic systems lack the ability to adapt to environmental variability, resulting in mismatches between human and robotic perception. In this work, a stable humidity-sensitive synaptic transistor featuring a broad detection window is designed and fabricated to bridge the gap between human and robotic sensory capabilities. The proposed humidity sensory neuron integrates a humidity sensing unit with a synaptic transistor in a separation device structure, enabling independent optimization of sensing and neuromorphic functions. This ensures excellent operational stability with negligible transfer characteristics degradation over 90 days. More importantly, the device exhibits robust humidity-dependent synaptic behaviors, including tunable excitatory postsynaptic currents, paired-pulse facilitation index, pulse-number dependent plasticity and high-pass filter coefficient under various relative humidity. Additionally, an artificial neural network is further constructed, which can accurately simulate human visual recognition performance under varying humidity conditions, highlighting its potential for applications in next-generation neuromorphic robotics, advanced sensing platforms, and cyborg technologies.

This study proposes an active-matrix self-capacitive touch-fingerprint sensor array using three standard staggered amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) and two capacitors (3T-2C) pixel circuit architecture. TCAD simulations demonstrate that touch-induced self-capacitance modulates the gate potential of the sensing TFT, producing contact-area dependent output current. Moreover, SPICE circuit simulations confirm the pixel circuit's transient response to touch events. Finally, a 256 x 256 -pixel sensor array with a pixel size of 85 mu m x85 mu m (298 ppi) is manufactured, achieving an exceptionally high signal-to-noise ratio (SNR) exceeding 114.40 dB. Furthermore, multi-identity recognition is demonstrated by combing the full-area sensor array with a specific signal processing algorithm. This work provides a manufacturable high-performance touch-fingerprint pixel circuit design for large-scale human-machine interaction interfaces with enhanced security.