Photoelectric memristors have shown great potential for future machine visions, via integrating sensing, memory, and computing (namely “all-in-one”) functions in a single device. However, their hard-to-tune photoresponse behavior necessitates extra function modules for signal encoding and modality conversion, impeding such integration. Here, we report an all-in-one memristor with Cs₂AgBiBr₆ perovskite, where the Br vacancy doping-endowed tunable energy band enables tunable photoresponsivity (TPR) behavior. As a result, the memristor showed a large tunable ratio of 35.9 dB, while its photoresponsivity presented a maximum of 2.7 × 10³ mA W^–1 and a long-term memory behavior with over 10⁴ s, making it suitable for realizing all-in-one processing tasks. By mapping the algorithm parameters onto the photoresponsivity, we successfully performed both recognition and processing tasks based on the TPR memristor array. Remarkably, compared with conventional complementary metal–oxide–semiconductor counterparts, our demonstrations provided comparable performance but had ~133-fold and ~299-fold reductions in energy consumption, respectively. Our work could facilitate the development of all-in-one smart devices for next-generation machine visions.

Triboelectric nanogenerator (TENG) is an emerging wave energy harvesting technology with excellent potential. However, due to issues with sealing, anchoring, and difficult deployment over large areas, TENG still cannot achieve large-scale wave energy capture. Here, a submerged and completely open solid–liquid TENG (SOSL-TENG) is developed for ocean wave energy harvesting. The SOSL-TENG is adapted to various water environments. Due to its simple structure, it is easy to deploy into various marine engineering facilities in service. Importantly, this not only solves the problem of difficult construction of TENG networks at present, but also effectively utilizes high-quality wave energy resources. The working mechanism and output performance of the SOSL-TENG are systematically investigated. With optimal triggering conditions, the transferred charge (Q_tr) and short-circuit current (I_sc) of SOSL-TENG are 2.58 μC and 85.9 μA, respectively. The wave tank experiment is taken for fully demonstrating the superiority of the SOSL-TENG network in large-scale collection and conversion of wave energy. Due to the excellent output performance, TENG can harvest wave energy to provide power for various commercial electronic devices such as LED beads, hygrothermograph, and warning lights. Importantly, the SOSL-TENG networks realizes self-powered for electrochemical systems, which provides a direction for energy cleanliness in industrial systems. This work provides a prospective strategy for large-scale deployment of TENG applications, especially for harvesting wave energy in spray splash zones or at the surface of the water.

Muscles, the fundamental components supporting all human movement, exhibit various signals upon contraction, including mechanical signals indicating tremors or mechanical deformation and electrical signals responsive to muscle fiber activation. For noninvasive wearable devices, these signals can be measured using surface electromyography (sEMG) and force myography (FMG) techniques, respectively. However, relying on a single source of information is insufficient for a comprehensive evaluation of muscle condition. In order to accurately and effectively evaluate the various states of muscles, it is necessary to integrate sEMG and FMG in a spatiotemporally synchronized manner. This study presents a flexible sensor for multimodal muscle state monitoring, integrating serpentine-structured sEMG electrodes with fingerprint-like FMG sensors into a patch approximately 250 μm thick. This design achieves a multimodal assessment of muscle conditions while maintaining a compact form factor. A thermo-responsive adhesive hydrogel is incorporated to enhance skin adhesion, improving the signal-to-noise ratio of the sEMG signals (33.07 dB) and ensuring the stability of the FMG sensor during mechanical deformation and tremors. The patterned coupled sensing patch demonstrates its utility in tracking muscular strength, assessing fatigue levels, and discerning features of muscle dysfunction by analyzing the time-domain and frequency-domain characteristics of the mechanical–electrical coupled signals, highlighting its potential application in sports training and rehabilitation monitoring.

Biologically inspired neuromorphic perceptual systems have great potential for efficient processing of multisensory signals from the physical world. Recently, artificial neurons constructed by memristor have been developed with good biological plausibility and density, but the filament-type memristor is limited by undesirable temporal and spatial variations, high electroforming voltage and limited reproducibility and the Mott insulator type memristor suffer from large driving current. Here, we propose a novel antiferroelectric artificial neuron (AFEAN) based on the intrinsic polarization and depolarization of AgNbO₃ (ANO) antiferroelectric (AFE) films to address these challenges. The antiferroelectric memristor exhibits low power consumption (8.99 nW), excellent durability (~10⁵) and high stability. Using such an AFEAN, a spike-based antiferroelectric neuromorphic perception system (AFENPS) has been designed, which can encode light level and temperature signals into spikes, and further construct a spiking neural network (SNN) (784 × 196 × 10) for optical image classification and thermal imaging classification, achieving 95.34% and 95.76% recognition accuracy on the MNIST dataset, respectively. This work paves the way for the simulation of spiking neurons using antiferroelectric materials and promising a promising method for the development of highly efficient hardware for neuromorphic perception systems.

Chemical vapor deposition (CVD)-gown graphene has tremendous potential as a transparent electrode for the next generation of flexible optoelectronics such as organic light-emitting diodes (OLEDs). A semiconductor coating is critical to improve the work function but usually makes graphene rougher and more conductive, which increases leakage, and then significantly restrict device efficiency improvement and worsens reliability. Here an insulating polyimide bearing carbazole-substituted triphenylamine units and bis(trifluoromethyl)phenyl groups (CzTPA PI/2CF₃) with high thermal stability is synthesized to passivate graphene. The similar surface free energy allows the uniform coating of CzTPA PI/2CF₃/N-methylpyrrolidone on graphene. Despite of a slight decrease in conductivity, CzTPA PI/2CF₃ passivation enables a substantial reduction in surface roughness and improvement in work function. By using such CzTPA PI/2CF₃-passivated graphene as anode, a flexible green OLED is demonstrated with a maximum current, power, and external quantum efficiencies of 88.4 cd A^–1, 115.7 lm W^–1, and 24.8%, respectively, which are among the best of the reported results. Moreover, the CzTPA PI/2CF₃ passivation enhances the device reliability with extending half-life and reducing dispersion coefficient of efficiency. The study promotes the practical use of graphene transparent electrodes for flexible optoelectronics.

Multicolor photodetection, essential for applications in infrared imaging, environmental monitoring, and spectral analysis, is often limited by the narrow bandgaps of conventional materials, which struggle with speed, sensitivity, and room-temperature operation. We address these issues with a multicolor uncooled photodetector based on an asymmetric Au/SnS/Gr vertical heterojunction with inversion-symmetry breaking. This design utilizes the complementary bandgaps of SnS and graphene to enhance the efficiency of carriers' transport through consistently oriented built-in electric fields, achieving significant advancements in directional photoresponse. The device demonstrates highly sensitive photoelectric detection performance, such as a responsivity (R) of 55.4–89.7 A W^–1 with rapid response times of approximately 104 μs, and exceptional detectivity (D*) of 2.38 × 10¹⁰ Jones ~8.19 × 10¹³ Jones from visible (520 nm) to infrared (2000 nm) light, making it suitable for applications demanding an imaging resolution of ~0.5 mm. Additionally, the comparative analysis reveals that the asymmetric vertical heterojunction outperforms its counterparts, exhibiting approximately 9-fold the photoresponse of symmetric vertical heterojunction and almost 100-fold that of symmetric horizontal heterojunction. This highly sensitive multicolor detector holds significant promise for applications in advanced versatile object detection and imaging recognition systems.

Artificial intelligence (AI) advancements are driving the need for highly parallel and energy-efficient computing analogous to the human brain and visual system. Inspired by the human brain, resistive random-access memories (ReRAMs) have recently emerged as an essential component of the intelligent circuitry architecture for developing high-performance neuromorphic computing systems. This occurs due to their fast switching with ultralow power consumption, high ON/OFF ratio, excellent data retention, good endurance, and even great possibilities for altering resistance analogous to their biological counterparts for neuromorphic computing applications. Additionally, with the advantages of photoelectric dual modulation of resistive switching, ReRAMs allow optically inspired artificial neural networks and reconfigurable logic operations, promoting innovative in-memory computing technology for neuromorphic computing and image recognition tasks. Optoelectronic neuromorphic computing architectured ReRAMs can simulate neural functionalities, such as light-triggered long-term/short-term plasticity. They can be used in intelligent robotics and bionic neurological optoelectronic systems. Metal oxide (MOx)–polymer hybrid nanocomposites can be beneficial as an active layer of the bistable metal–insulator–metal ReRAM devices, which hold promise for developing high-performance memory technology. This review explores the state of the art for developing memory storage, advancement in materials, and switching mechanisms for selecting the appropriate materials as active layers of ReRAMs to boost the ON/OFF ratio, flexibility, and memory density while lowering programming voltage. Furthermore, material design cum-synthesis strategies that greatly influence the overall performance of MOx–polymer hybrid nanocomposite ReRAMs and their performances are highlighted. Additionally, the recent progress of multifunctional optoelectronic MOx–polymer hybrid composites-based ReRAMs are explored as artificial synapses for neural networks to emulate neuromorphic visualization and memorize information. Finally, the challenges, limitations, and future outlooks of the fabrication of MOx–polymer hybrid composite ReRAMs over the conventional von Neumann computing systems are discussed.

Covalent organic frameworks (COFs) feature π-conjugated structure, high porosity, structural regularity, large specific surface area, and good stability, being considered as ideal platform for photocatalytic application. Although single COFs have achieved significant progress in photocatalysis benefiting from their distinctive properties, the COFs-based hybrids provide an extraordinary opportunity to achieve superior photocatalytic performance. From the perspective of carrier transfer mechanism, a systematic summary of hybrids based on COFs and other functional materials (metal single atoms, metal clusters/nanoparticles, inorganic semiconductors, metal–organic frameworks, and other polymers) can offer valuable guidance for the design of COFs-based hybrids. In this review, the photocatalytic mechanism for hybrid materials (such as Schottky junction, type II heterojunction, Z-scheme heterojunction, and S-scheme heterojunction) is briefly introduced. Subsequently, the performance of COFs-based hybrids in photocatalytic water splitting, CO₂ reduction, and pollutant degradation are comprehensively reviewed. Specifically, the carrier separation and transfer in different types of hybrids are highlighted. Finally, the challenges and prospects of COFs-based hybrids for photocatalysis are envisaged. The insights presented in this review are expected to be helpful in the rational design of COFs-based hybrids to obtain outstanding photocatalytic activity.