ABSTRACT Brain‐inspired neuromorphic computing paves an alternative to the Von Neumann bottleneck on implementing highly efficient and parallel processing hardware. However, in a crossbar array architecture, crosstalk occurs between devices. To address this issue, in this study, a Pt/WO 3 /FTO self‐rectifying memristor was fabricated using pulsed laser deposition, exhibiting a rectification ratio ≈10 3 , endurance of 5 × 10 3 cycles, negligible device‐to‐device variability, highly stable up to 25 cycles, energy consumption of 1.1 pJ/µm 2 in writing operation, and excellent synaptic behavior without any additional material engineering. This study comprehensively investigates the neuromorphic computing characteristics of the fabricated Pt/WO 3 /FTO structure. The conduction mechanism was explained using charge trapping/detrapping and Schottky barrier formation at the Pt/WO 3 interface, resulting in excellent rectification behavior. Interestingly, the fabricated structure exhibited second‐order memristive behavior, attributed to internal ion dynamics. The neuromorphic computing characteristics were comprehensively assessed using artificial neural network simulations, and fabricated devices achieved 90% accuracy on the MNIST handwritten digits dataset and 75% accuracy on the Fashion‐MNIST dataset, thus emulating synaptic functionality and showing excellent potential of the fabricated devices in real‐world artificial intelligence applications.

ABSTRACT Flexible electronic devices are increasingly being utilized in a wide range of applications in healthcare, information technology, and energy because of their flexibility, stretchability, and enhanced resistance to bending fatigue. The development of flexible electronics is closely related to material selection, structural design, and manufacturing processes. Among these, the manufacturing processes applied serve as primary driving forces that directly affect the functionality, scalability, and cost of devices. Compared with traditional manufacturing technologies, inkjet printing has the unique advantages of high‐efficiency, low‐cost, exceptional flexibility, and superior material utilization rate. Consequently, inkjet printing has become the key technology in accelerating the development of flexible electronics. This review systematically discusses recent advancements in inkjet printing technology for flexible electronics. First, the working principles of several inkjet printing technologies are introduced. Next, the characteristics of various functional ink materials are summarized. This review also discusses recent advances in the manufacturing and performance optimization of typical flexible electronics via inkjet printing. Subsequently, the emerging applications of inkjet‐based flexible electronics, such as clinical medicine, intelligent human‐machine interaction, and energy are presented. Finally, the development prospects and major challenges of inkjet‐based flexible electronics are analyzed and summarized.

ABSTRACT Artificial neurons are considered an effective approach to alleviating the challenges of large data throughput and high‐power consumption in electronic tactile sensory systems. However, emerging artificial neuron implementations beyond conventional silicon circuits are still limited in enabling low‐power operation and large‐scale scalability. Oxide semiconductors provide an energy‐efficient platform for neuromorphic circuits, owing to their low leakage current, while offering fast, gate‐controlled switching. Here, we report an Axon‐Hillock (A‐H) artificial neuron circuit based on solution‐processed indium oxide (In 2 O 3 ) transistors. The neuron circuit contains a two‐stage high‐gain (∼180 V/V) inverter composed of four In 2 O 3 transistors, a leakage transistor, and a capacitor, exhibiting >20 kHz frequency response, tunable output spike frequency up to 500 Hz, and ∼62 nW power consumption. Integrated with a pressure sensor, the neuron effectively converts tactile pressure into spike trains at the sensing interface. We further demonstrate in‐sensor spike encoding of tactile stimuli and validate texture recognition with a spiking reservoir network (SRN), achieving ∼88% test accuracy over 20 fabric classes. These results demonstrate the feasibility of implementing solution‐processed oxide materials in energy‐efficient neuromorphic electronics.

ABSTRACT Achieving both broad bandwidth and high frequency in a single sensor is challenging but essential for high‐resolution heterostructure photoacoustic diagnostic imaging. Such capabilities can provide early‐stage disease detection signatures and eliminate need for multiple sensors. While promising, we present the first fabrication and demonstration of nanofiber‐based photoacoustic sensors, enabling high‐frequency broad‐bandwidth photoacoustic (PA) signal detection due to the flexible nanofibers' network that allows the absorption of acoustic energy over large frequency range and conversion of mechanical vibrations into electrical signals efficiently. A customized electrospinning system is designed for synthesizing nanofibers on a heated substrate using composite solution of polymer PVDF‐TrFE and cubic single‐crystal nanoparticles. Synthesized nanofibers provide a large surface area, with BTO around the surface of nanofibers, resulting in higher polarization, crystallinity, and charge generation while making proper electrical contact using loaded high pressure. These composite nanofibers are characterized and optimized for sensor fabrication. Fabricated sensors are used to detect photoacoustic signals from various sources including eumelanin and hemoglobin. Photoacoustic signals of central frequencies (1.5–33.2 MHz) and bandwidth (209%) are recorded. The sensors are further used for PA imaging with a human subject and a chicken phantom, showing potential applications of the developed sensors in clinical diagnostics.

ABSTRACT Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high‐dimensional print parameter space makes tuning print parameters time‐intensive. We present a Bayesian optimization study that constructs a digital twin from printed‐silver data to benchmark surrogate models, acquisition functions, and batch sizes head‐to‐head to achieve user‐specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Overall, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.

ABSTRACT The development of robust and ultrasensitive biosensing technologies is crucial for advancing life science research and point‐of‐care diagnostics. Here, we report an integrated electrochemiluminescence (ECL) sensing platform that achieves exceptional sensitivity by incorporating a programmable DNA nanomachine as a signal amplification module with an efficient nanoparticle‐based ECL resonance energy transfer (RET) pair. The platform features a luminol‐functionalized Au/ZIF‐67 nanocomposite as a catalytic emitter and Ag@CuS nanospheres as quenchers. Target recognition triggers the autonomous walking of an Exonuclease III‐powered DNA nanomachine on the electrode surface, driving the cyclic assembly of quenchers and resulting in pronounced ECL quenching via RET. This engineered technology demonstrates ultrasensitive detection of the model protein, brain‐derived neurotrophic factor (BDNF), with a limit of detection of 9.0 fg mL −1 , a wide linear range over six orders of magnitude, and excellent accuracy in human serum. The platform exhibits high reproducibility and stability, underscoring its potential as a versatile and reliable technological foundation for next‐generation diagnostic devices.

ABSTRACT Advancements in flexible pressure sensors have been substantial. However, traditional configurations are restricted in pressure‑response scope owing to structural stiffening. Inspired by biological structures, we present an iontronic flexible pressure sensor featuring a microstructured electrode to amplify contact area variation under pressure. This architecture enhances the device's deformability while minimizing initial contact area, leading to high sensitivity. In addition, the sensor employs a composite ionic dielectric layer composed of polyvinyl alcohol and [BMIM]BF 4 , thereby enhancing ion transport efficiency, and silver nanowires serve as the conductive electrode component to maintain reliable electrical conduction during mechanical deformation. In the operational range of 0–335 kPa, the device demonstrates a highest sensitivity of 10.856 ± 0.114 kPa −1 , coupled with a response duration of 62.5 ± 8.3 ms and a recovery period of 75 ± 10.2 ms, as supported by experimental measurements. After 3000 consecutive cycles under external loads of 2.5 and 250 kPa, the sensor showed no significant signal attenuation, indicating its excellent mechanical durability. In wearable applications, this sensor reliably detects gestures and sign language through capacitive sensing, thanks to its highly repeatable performance and stable dynamic response, demonstrating great potential for use in motion detection.