Due to their unique photoelectric properties, nontoxic tin-based perovskites are emerging candidates for efficient near-infrared LEDs. However, the facile oxidation of Sn²⁺ and the rapid crystallization rate of tin-based perovskites result in suboptimal film quality, leading to inferior efficiencies of tin-based perovskite light-emitting diodes (Pero-LEDs). In this study, we investigate the influence of commonly used solvents on the quality of the CsSnI₃ films. Remarkably, DMSO exhibits a stronger interaction with SnI₂, forming a stable intermediate phase of SnI₂·3DMSO. This intermediate effectively inhibits the oxidation of Sn²⁺ and slows down the crystallization rate, bringing in lower defect state density and higher photoluminescence quantum yield of the prepared perovskite films. Consequently, the corresponding Pero-LEDs achieve a maximum external quantum efficiency (EQE) of 5.6%, among the most efficient near-infrared Pero-LEDs. In addition, the device processes ultra-low efficiency roll-off and high reproducibility. Our research underscores the crucial role of solvent-perovskite coordination in determining film quality. These findings offer valuable guidance for screening solvents to prepare highly efficient and stable tin-based perovskites.

Accurately forecasting the nonlinear degradation of lithium-ion batteries (LIBs) using early-cycle data can obviously shorten the battery test time, which accelerates battery optimization and production. In this work, a self-adaptive long short-term memory (SA-LSTM) method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data. Specifically, two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model. The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model. The proposed method achieved an average online prediction error of 6.00% and 6.74% for discharge capacity and end of life, respectively, when using the early-cycle discharge information until 90% capacity retention. Furthermore, the importance of temperature control was highlighted by correlating the features with the average temperature in each cycle. This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs, and unveils the underlying degradation mechanism and the importance of controlling environmental temperature.

The potential of three-dimensional (3D) printing technology in the fabrication of advanced polymer composites is becoming increasingly evident. This review discusses the latest research developments and applications of 3D printing in polymer composites. First, it focuses on the optimization of 3D printing technology, that is, by upgrading the equipment or components or adjusting the printing parameters, to make them more adaptable to the processing characteristics of polymer composites and to improve the comprehensive performance of the products. Second, it focuses on the 3D printable novel consumables for polymer composites, which mainly include the new printing filaments, printing inks, photosensitive resins, and printing powders, introducing the unique properties of the new consumables and different ways to apply them to 3D printing. Finally, the applications of 3D printing technology in the preparation of functional polymer composites (such as thermal conductivity, electromagnetic interference shielding, biomedicine, self-healing, and environmental responsiveness) are explored, with a focus on the distribution of the functional fillers and the influence of the topological shapes on the properties and functional characteristics of the 3D printed products. The aim of this review is to deepen the understanding of the convergence between 3D printing technology and polymer composites and to anticipate future trends and applications.

Near-infrared (NIR) luminescent metal halide (LMH) materials have attracted great attention in various optoelectronic applications due to their low-temperature solution-processable synthesis, abundant crystallographic/electronic structures, and unique optoelectronic properties. However, some challenges still remain in their luminescence design, performance improvement, and application assignments. This review systematically summarizes the development of NIR LMHs through classifying NIR luminescent origins into four major categories: band-edge emission, self-trapped exciton (STE) emission, ion emission, and defect-related emission. The luminescence mechanisms of different types of NIR LMHs are discussed in detail by analyzing typical examples. Reasonable strategies for designing and optimizing luminescence/optoelectronic properties of NIR LMHs are summarized, including bandgap engineering, self-trapping state engineering, chemical composition modification, energy transfer, and other auxiliary strategies such as improvement of synthesis scheme and post-processing. Furthermore, application prospects based on the optoelectronic devices are revealed, including phosphor-converted light-emitting diodes (LEDs), electroluminescent LEDs, photodetectors, solar cells, and x-ray scintillators, as well as demonstrations of some related practical applications. Finally, the existing challenges and future perspectives on the development of NIR LMH materials are critically proposed. This review aims to provide general understanding and guidance for the design of high-performance NIR LMHs materials.

The exceptional properties of two-dimensional (2D) magnet materials present a novel approach to fabricate functional magnetic tunnel junctions (MTJ) by constructing full van der Waals (vdW) heterostructures with atomically sharp and clean interfaces. The exploration of vdW MTJ devices with high working temperature and adjustable functionalities holds great potential for advancing the application of 2D materials in magnetic sensing and data storage. Here, we report the observation of highly tunable room-temperature tunneling magnetoresistance through electronic means in a full vdW Fe₃GaTe₂/WSe₂/Fe₃GaTe₂ MTJ. The spin valve effect of the MTJ can be detected even with the current below 1 nA, both at low and room temperatures, yielding a tunneling magnetoresistance (TMR) of 340% at 2 K and 50% at 300 K, respectively. Importantly, the magnitude and sign of TMR can be modulated by a DC bias current, even at room temperature, a capability that was previously unrealized in full vdW MTJs. This tunable TMR arises from the contribution of energy-dependent localized spin states in the metallic ferromagnet Fe₃GaTe₂ during tunnel transport when a finite electrical bias is applied. Our work offers a new perspective for designing and exploring room-temperature tunable spintronic devices based on vdW magnet heterostructures.

Over the last decade, perovskite solar cells (PSCs) have drawn extensive attention owing to their high power conversion efficiency (single junction: 26.1%, perovskite/silicon tandem: 33.9%) and low fabrication cost. However, the short lifespan of PSCs with initial efficiency still blocks their practical applications. This operational instability may originate from the intrinsic and extrinsic degradation of materials or devices. Although the lifetime of PSCs has been prolonged through component, crystal, defect, interface, encapsulation engineering, and so on, the systematic analysis of failure regularity for PSCs from the perspective of materials and devices against multiple operating stressors is indispensable. In this review, we start with elaboration of the predominant degradation pathways and mechanism for PSCs under working stressors. Then the strategies for improving long-term durability with respect to fundamental materials, interface designs, and device encapsulation have been summarized. Meanwhile, the key results have been discussed to understand the limitation of assessing PSCs stability, and the potential applications in indoor photovoltaics and wearable electronics are demonstrated. Finally, promising proposals, encompassing material processing, film formation, interface strengthening, structure designing, and device encapsulation, are provided to improve the operational stability of PSCs and promote their commercialization.

Protonic solid oxide electrolysis cells (P-SOECs) operating at intermediate temperatures, which have low costs, low environmental impact, and high theoretical electrolysis efficiency, are considered promising next-generation energy conversion devices for green hydrogen production. However, the developments and applications of P-SOECs are restricted by numerous material- and interface-related issues, including carrier mismatch between the anode and electrolyte, current leakage in the electrolyte, poor interfacial contact, and chemical stability. Over the past few decades, considerable attempts have been made to address these issues by improving the properties of P-SOECs. This review comprehensively explores the recent advances in the mechanisms governing steam electrolysis in P-SOECs, optimization strategies, specially designed components, electrochemical performance, and durability. In particular, given that the lack of suitable anode materials has significantly impeded P-SOEC development, the relationships between the transferred carriers and the cell performance, reaction models, and surface decoration approaches are meticulously probed. Finally, the challenges hindering P-SOEC development are discussed and recommendations for future research directions, including theoretical calculations and simulations, structural modification approaches, and large-scale single-cell fabrication, are proposed to stimulate research on P-SOECs and thereby realize efficient electricity-to-hydrogen conversion.

Quantitative analysis of gait parameters, such as stride frequency and step speed, is essential for optimizing physical exercise for the human body. However, the current electronic sensors used in human motion monitoring remain constrained by factors such as battery life and accuracy. This study developed a self-powered gait analysis system (SGAS) based on a triboelectric nanogenerator (TENG) fabricated electrospun composite nanofibers for motion monitoring and gait analysis for regulating exercise programs. The SGAS consists of a sensing module, a charging module, a data acquisition and processing module, and an Internet of Things (IoT) platform. Within the sensing module, two specialized sensing units, TENG-S1 and TENG-S2, are positioned at the forefoot and heel to generate synchronized signals in tandem with the user's footsteps. These signals are instrumental for real-time step count and step speed monitoring. The output of the two TENG units is significantly improved by systematically investigating and optimizing the electrospun composite nanofibers' composition, strength, and wear resistance. Additionally, a charge amplifier circuit is implemented to process the raw voltage signal, consequently bolstering the reliability of the sensing signal. This refined data is then ready for further reading and calculation by the micro-controller unit (MCU) during the signal transmission process. Finally, the well-conditioned signals are wirelessly transmitted to the IoT platform for data analysis, storage, and visualization, enhancing human motion monitoring.

Prussian blue analogs (PBAs) are potential contestants for aqueous Mg-ion batteries (AMIBs) on account of their high discharge voltage and three-dimensional open frameworks. However, the low capacity arising from single reaction site severely restricts PBAs' practical applications in high-energy-density AMIBs. Here, an organic acid co-coordination combined with etching method is reported to fabricate defect-rich potassium-free copper hexacyanoferrate with structural water on carbon nanotube fiber (D-CuHCF@CNTF). Benefiting from the high-valence-state reactive sites, arrayed structure and defect effect, the well-designed D-CuHCF@CNTF exhibits an extraordinary reversible capacity of 146.6 mAh g⁻¹ with two-electron reaction, nearly close to its theoretical capacity. It is interesting to unlock the reaction mechanism of the Fe²⁺/Fe³⁺ and Cu⁺/Cu²⁺ redox couples via x-ray photoelectron spectroscopy. Furthermore, density functional theory calculations reveal that Fe and Cu in potassium-free D-CuHCF participate in charge transfer during the Mg²⁺ insertion/extraction process. As a proof-of-concept demonstration, a rocking-chair fiber-shaped AMIBs was constructed via coupling with the NaTi₂(PO₄)₃/CNTF anode, achieving high energy density and impressive mechanical flexibility. This work provides new possibilities to develop potassium-free PBAs with dual-active sites as high-capacity cathodes for wearable AMIBs.

Solid-state batteries that employ solid-state electrolytes (SSEs) to replace routine liquid electrolytes are considered to be one of the most promising solutions for achieving high-safety lithium metal batteries. SSEs with high mechanical modulus, thermal stability, and non-flammability can not only inhibit the growth of lithium dendrites but also enhance the safety of lithium metal batteries. However, several internal materials/electrodes-related thermal hazards demonstrated by recent works show that solid-state lithium metal batteries (SSLMBs) are not impenetrable. Therefore, understanding the potential thermal hazards of SSLMBs is critical for their more secure and widespread applications. In this contribution, we provide a comprehensive overview of the thermal failure mechanism of SSLMBs from materials to devices. Also, strategies to improve the thermal safety performance of SSLMBs are included from the view of material enhancement, battery design, and external management. Consequently, the future directions are further provided. We hope that this work can shed bright insights into the path of constructing energy storage devices with high energy density and safety.

The exponentially increasing heat generation in electronic devices, induced by high power density and miniaturization, has become a dominant issue that affects carbon footprint, cost, performance, reliability, and lifespan. Liquid metals (LMs) with high thermal conductivity are promising candidates for effective thermal management yet are facing pump-out and surface-spreading issues. Confinement in the form of metallic particles can address these problems, but apparent alloying processes elevate the LM melting point, leading to severely deteriorated stability. Here, we propose a facile and sustainable approach to address these challenges by using a biogenic supramolecular network as an effective diffusion barrier at copper particle-LM (EGaIn/Cu@TA) interfaces to achieve superior thermal conduction. The supramolecular network promotes LM stability by reducing unfavorable alloying and fluidity transition. The EGaIn/Cu@TA exhibits a record-high metallic-mediated thermal conductivity (66.1 W m^-1 K^-1) and fluidic stability. Moreover, mechanistic studies suggest the enhanced heat flow path after the incorporation of copper particles, generating heat dissipation suitable for computer central processing units, exceeding that of commercial silicone. Our results highlight the prospects of renewable macromolecules isolated from biomass for the rational design of nanointerfaces based on metallic particles and LM, paving a new and sustainable avenue for high-performance thermal management.

Visible light-based human–machine interactive media is capable of transmitting electrical readouts to machines and providing intuitive feedback to users simultaneously. Currently, many inorganic mechanoluminescent (ML) materials-based interactive media, typically ZnS-loaded phosphors (ZLPs), have been successfully demonstrated. However, organic ML materials-based solutions were rarely exploited despite their huge merits of strong structural modification, abundant luminescence property, low cost, easy preparation, and so on. Here, we propose a novel interactive tactile display (ITD) based on organic ML materials (Cz-A6-dye) and triboelectric nanogenerator, with ultra-brightness (130% enhancement) and ultra-low threshold pressure (57% reduction) as compared to ZLPs. The proposed ITD achieves the conversion of weak mechanical stimuli into visible light and electrical signals simultaneously, without extra power supplies. Furthermore, the relationship between the luminous performance of organic ML materials and mechanical force is quantified, benefiting from the uniform ML layer prepared. Enabled by convolutional neural networks, the high-accuracy recognition (97.1%) for handwriting and identity of users is realized at the same time. Thus, the ITD has great potential for intelligent wearable electronics and classified military applications.

The regulation of carrier generation and transport by Schottky junctions enables effective optoelectronic conversion in optoelectronic devices. A simple and general strategy to spontaneously generate photocurrent is of great significance for self-powered photodetectors but is still being pursued. Here, we propose that a photocurrent can be induced at zero bias by the transmittance contrast of MXene electrodes in MXene/semiconductor Schottky junctions. Two MXene electrodes with a large transmittance contrast (84%) between the thin and thick zones were deposited on the surface of a semiconductor wafer using a simple and robust solution route. Kelvin probe force microscopy tests indicated that the photocurrent at zero bias could be attributed to asymmetric carrier generation and transport between the two Schottky junctions under illumination. As a demonstration, the MXene/GaN ultraviolet (UV) photodetector exhibits excellent performance superior to its counterpart without transmittance contrast, including high responsivity (81 mA W^–1), fast response speed (less than 31 and 29 ms) and ultrahigh on/off ratio (1.33 × 10⁶), and good UV imaging capability. Furthermore, this strategy has proven to be universal for first- to third-generation semiconductors such as Si and GaAs. These results provide a facile and cost-effective route for high-performance self-powered photodetectors and demonstrate the versatile and promising applications of MXene electrodes in optoelectronics.

High-voltage nickel (Ni)-rich layered oxide-based lithium metal batteries (LMBs) exhibit a great potential in advanced batteries due to the ultra-high energy density. However, it is still necessary to deal with the challenges in poor cyclic and thermal stability before realizing practical application where cycling life is considered. Among many improved strategies, mechanical and chemical stability for the electrode electrolyte interface plays a key role in addressing these challenges. Therefore, extensive effort has been made to address the challenges of electrode-electrolyte interface. In this progress, the failure mechanism of Ni-rich cathode, lithium metal anode and electrolytes are reviewed, and the latest breakthrough in stabilizing electrode-electrolyte interface is also summarized. Finally, the challenges and future research directions of Ni-rich LMBs are put forward.

Silver nanowire (AgNW) networks hold great promises as next-generation flexible transparent electrodes (FTEs) for high-performance flexible optoelectronic devices. However, achieving large-area flexible AgNW network electrodes with low sheet resistance, high optical transmittance, and a smooth surface remains a grand challenge. Here, we report a straightforward and cost-effective roll-to-roll method that includes interface assembly/wetting-induced climbing transfer, nanowelding, and washing processess to fabricate flexible ordered layered AgNW electrodes with high network uniformity. By manipulating the stacking number of the interfacially assembled AgNW monolayer, we can precisely tailor and balance the transparency and the conductivity of the electrodes, achieving an exceptional Figure of Merit (FoM) value of 862. Moreover, the ordered layered structure enhances surface smoothness, compared with randomly arranged structures. To highlight the potential of these ordered layered AgNW network electrodes in flexible optoelectronic devices, we successfully employ them as highly sensitive strain sensors, large-area flexible touch screens, and flexible smart windows. Overall, this work represents a substantial advance toward high-performance FTEs over large areas, opening up exciting opportunities for the development of advanced optoelectronic devices.

Layered two-dimensional (2D) materials have garnered marvelous attention in diverse fields, including sensors, capacitors, nanocomposites and transistors, owing to their distinctive structural morphologies and superior physicochemical properties. Recently, layered quasi-2D materials, especially layered bismuth oxyselenide (Bi₂O₂Se), are of particular interest, because of their different interlayer interactions from other layered 2D materials. On this basis, this material offers richer and more intriguing physics, including high electron mobility, sizeable bandgap, and remarkable thermal and chemical durability, rendering it an utterly prospective contender for use in advanced electronic and optoelectronic applications. Herein, this article reviews the recent advances related with Bi₂O₂Se. Initially, its structural characterization, band structure, and basic properties are briefly introduced. Further, the synthetic strategies for the preparation of Bi₂O₂Se are presented. Furthermore, the diverse applications of Bi₂O₂Se in the field of electronics and optoelectronics, photocatalytic, solar cells and sensing were summarized in detail. Ultimately, the challenges and future perspectives of Bi₂O₂Se are included.

The fast booming of wearable electronics provides great opportunities for intelligent gas detection with improved healthcare of mining workers, and a variety of gas sensors have been simultaneously developed. However, these sensing systems are always limited to single gas detection and are highly susceptible to the inference of ubiquitous moisture, resulting in less accuracy in the analysis of gas compositions in real mining conditions. To address these challenges, we propose a synergistic strategy based on sensor integration and machine learning algorithms to realize precise NH₃ and NO₂ gas detections under real mining conditions. A wearable sensing array based on the graphene and polyaniline composite is developed to largely enhance the sensitivity and selectivity under mixed gas conditions. Further introduction of backpropagation neural network (BP-NN) and partial least squares (PLS) algorithms could improve the accuracy of gas identification and concentration prediction and settle the inference of moisture, realizing over 99% theoretical prediction level on NH₃ and NO₂ concentrations within a wide relative humidity range, showing great promise in real mining detection. As proof of concept, a wireless wearable bracelet, integrated with sensing arrays and machine-learning algorithms, is developed for wireless real-time warning of hazardous gases in mines under different humidity conditions.

The inherent unpredictability of the maritime environment leads to low rates of survival during accidents. Life jackets serve as a crucial safety measure in underwater environments. Nonetheless, most conventional life jackets lack the capability to monitor the wearer's underwater body movements, impeding their effectiveness in rescue operations. Here, we present an intelligent self-powered life jacket system (SPLJ) composed of a wireless body area sensing network, a set of deep learning analytics, and a human condition detection platform. Six coaxial core-shell structure triboelectric fiber sensors with high sensitivity, stretchability, and flexibility are integrated into this system. Additionally, a portable integrated circuit module is incorporated into the SPLJ to facilitate real-time monitoring of the wearer's movement. Moreover, by leveraging the deep-learning-assisted data analytics and establishing a robust correlation between the wearer's movements and condition, we have developed a comprehensive system for monitoring drowning individuals, achieving an outstanding recognition accuracy of 100%. This groundbreaking work introduces a fresh approach to underwater intelligent survival devices, offering promising prospects for advancing underwater smart wearable devices in rescue operations and the development of ocean industry.

Optoelectronic logic gates have emerged as one of the key candidates for the creation of next generation logic devices. However, current optoelectronic logic gates can provide only one or two logic gates, severely limiting their applications. Here we report a self-powered and mechanically flexible device based on a BaTiO₃ ferroelectric film to produce multi-modal logic gates. By exploiting the photo-induced photovoltaic and pyroelectric effects of a Schottky junction which is created between BaTiO₃ and LaNiO₃, the device is able to provide five different optoelectronic logic gates, which can be operated using input lasers of different wavelength (405 or 785 nm). The mode of operation of the logic gate can be switched by controlling the wavelength and intensity of the input laser, where the switching process is both lossless and reversible. A logic gate array was designed to conduct the five logic operations, with 100% accuracy, thereby providing application potential for the Internet of Things, big data, and secure solutions for data processing and transmission.

The pursuit of designing superconductors with high T_c has been a long-standing endeavor. However, the widespread incorporation of doping in high T_c superconductors significantly impacts electronic structure, intricately influencing T_c. The complex interplay between the structural composition and material performance presents a formidable challenge in superconductor design. Based on a novel generative model, diffusion model, and doping adaptive representation: three-channel matrix, we have designed a high T_c superconductors inverse design model called Supercon-Diffusion. It has achieved remarkable success in accurately generating chemical formulas for doped high T_c superconductors. Supercon-Diffusion is capable of generating superconductors that exhibit high T_c and excels at identifying the optimal doping ratios that yield the peak T_c. The doping effectiveness (55%) and electrical neutrality (55%) of the generated doped superconductors exceed those of traditional GAN models by more than tenfold. Density of state calculations on the structures further confirm the validity of the generated superconductors. Additionally, we have proposed 200 potential high T_c superconductors that have not been documented yet. This groundbreaking contribution effectively reduces the search space for high T_c superconductors. Moreover, it successfully establishes a bridge between the interrelated aspects of composition, structure, and property in superconductors, providing a novel solution for designing other doped materials.

In the process of photocatalytic synthesis of ammonia, the kinetics of carrier separation and transport, adsorption of nitrogen, and activation of the N≡N triple bond are key factors that directly affect the efficiency of converting nitrogen to ammonia. Here, we report a new strategy for anchoring MXene quantum dots (MXene QDs) onto the surface of ZnIn₂S₄ by forming Ti—S bonds, which provide a channel for the rapid separation and transport of charge carriers and effectively extend the lifespan of photogenerated carriers. The unique charge distribution caused by the sulfurization of the MXene QDs further enhances the performance of the photocatalysts for the adsorption and activation of nitrogen. The photocatalytic ammonia synthesis efficiency of MXene QDs–ZnIn₂S₄ can reach up to 360.5 μmol g⁻¹ h⁻¹. Density functional theory calculations, various in situ techniques, and ultrafast spectroscopy are used to characterize the successful construction of Ti—S bonds and the dynamic nature of excited state charge carriers in MXene QDs–ZnIn₂S₄, as well as their impact on nitrogen adsorption activation and photocatalytic ammonia synthesis efficiency. This study provides a new example of how to improve nitrogen adsorption and activation in photocatalytic material systems and enhance charge carrier dynamics to achieve efficient photocatalytic nitrogen conversion.

The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces. Inspired by human multisensory signal generation and neuroplasticity-based signal processing, here, an artificial perceptual neuro array with visual-tactile sensing, processing, learning, and memory is demonstrated. The neuromorphic bimodal perception array compactly combines an artificial photoelectric synapse network and an integrated mechanoluminescent layer, endowing individual and synergistic plastic modulation of optical and mechanical information, including short-term memory, long-term memory, paired pulse facilitation, and “learning-experience” behavior. Sequential or superimposed visual and tactile stimuli inputs can efficiently simulate the associative learning process of “Pavlov's dog”. The fusion of visual and tactile modulation enables enhanced memory of the stimulation image during the learning process. A machine-learning algorithm is coupled with an artificial neural network for pattern recognition, achieving a recognition accuracy of 70% for bimodal training, which is higher than that obtained by unimodal training. In addition, the artificial perceptual neuron has a low energy consumption of ~20 pJ. With its mechanical compliance and simple architecture, the neuromorphic bimodal perception array has promising applications in large-scale cross-modal interactions and high-throughput intelligent perceptions.

The crystal-structure symmetry in real space can be inherited in the reciprocal space, making high-symmetry materials the top candidates for thermoelectrics due to their potential for significant electronic band degeneracy. A practical indicator that can quantitatively describe structural changes would help facilitate the advanced thermoelectric material design. In face-centered cubic structures, the spatial environment of the same crystallographic plane family is isotropic, such that the distances between the close-packed layers can be derived from the atomic distances within the layers. Inspired by this, the relationship between inter- and intra-layer geometric information can be used to compare crystal structures with their desired cubic symmetry. The close-packed layer spacing was found to be a practical guideline of crystal structure symmetry in IV-VI chalcogenides and I-V-VI₂ ternary semiconductors, both of which are historically important thermoelectrics. The continuous structural evolution toward high symmetry can be described by the layer spacing when temperature or/and composition change, which is demonstrated by a series of pristine and alloyed thermoelectric materials in this work. The layer-spacing-based guideline provides a quantitative pathway for manipulating crystal structures to improve the electrical and thermal properties of thermoelectric materials.

To overcome the intrinsic inefficiency of the von Neumann architecture, neuromorphic devices that perform analog vector-matrix multiplication have been highlighted for achieving power- and time-efficient data processing. In particular, artificial synapses, of which conductance should be programmed to represent the synaptic weights of the artificial neural network, have been intensively researched to realize neuromorphic devices. Here, inspired by excitatory and inhibitory synapses, we develop an artificial optoelectronic synapse that shows both potentiation and depression characteristics triggered only by optical inputs. The design of the artificial optoelectronic synapse, in which excitatory and inhibitory synaptic phototransistors are serially connected, enables these characteristics by spatiotemporally irradiating the phototransistor channels with optical pulses. Furthermore, a negative synaptic weight can be realized without the need for electronic components such as comparators. With such attributes, the artificial optoelectronic synapse is demonstrated to classify three digits with a high recognition rate (98.3%) and perform image preprocessing via analog vector-matrix multiplication.

The recent progress on the liquid crystalline (LC) dispersion of two-dimensional (2D) transition metal carbides (MXenes) has propelled this unique nanomaterial into a realm of high-performance architectures, such as films and fibers. Additionally, compared to architectures made from typical non-LC dispersions, those derived from LC MXene possess tunable ion transport routes and enhanced conductivity and physical properties, demonstrating great potential for a wide range of applications, such as electronic displays, smart glasses, and thermal camouflage devices. This review provides an overview of the progress achieved in the production and processing of LC MXenes, including critical discussions on satisfying the required conditions for LC formation. It also highlights how acquiring LC MXenes has broadened the current solution-based manufacturing paradigm of MXene-based architectures, resulting in unprecedented performances in their conventional applications (e.g., energy storage and strain sensing) and in their emerging uses (e.g., tribology). Opportunities for innovation and foreseen challenges are also discussed, offering future research directions on how to further benefit from the exciting potential of LC MXenes with the aim of promoting their widespread use in designing and manufacturing advanced materials and applications.

A wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse. However, few research has been conducted regarding conformal sensors for accurately measuring the human body circumferences for dynamic 3D human body reshaping. Here, we develop a stretchable spring-sheathed yarn sensor (SSYS) as a smart ruler, for precisely measuring the circumference of human bodies and long-term tracking the movement for the dynamic 3D body reconstruction. The SSYS has a robust property, high resilience, high stability (>18 000), and ultrafast response (12 ms) to external deformation. It is also washable, wearable, tailorable, and durable for long-time wearing. Moreover, geometric, and mechanical behaviors of the SSYS are systematically investigated both theoretically and experimentally. In addition, a transfer learning algorithm that bridges the discrepancy of real and virtual sensing performance is developed, enabling a small body circumference measurement error of 1.79%, noticeably lower than that of traditional learning algorithm. Furtherly, 3D human bodies that are numerically consistent with the actual bodies are reconstructed. The 3D dynamic human body reconstruction based on the wearing sensing system and transfer learning algorithm enables excellent virtual fitting and shirt customization in a smart and highly efficient manner. This wearable sensing technology shows great potential in human-computer interaction, intelligent fitting, specialized protection, sports activities, and human physiological health tracking.

Intelligent applications, with tactile sensors at their core, represent significant advancement in the field of artificial intelligence. However, achieving perception abilities in tactile sensors that match or exceed human skin remains a formidable challenge. Consequently, the design and implementation of hierarchical structural materials are considered the optimal solution to this challenge. In contrast to conventional methods, such as complicated lithography and three-dimensional printing, the cost-effective and scalable nature of advanced solution-synthesis methods makes them ideal for preparing diverse tactile sensors with hierarchical structural materials. However, the process and applicability of advanced solution synthesis methods have yet to form a seamless system. Accordingly, the development and intellectualization of tactile sensors based on advanced solution synthesis methods are still in their early stages, and require a comprehensive and systematic review to usher in progress. This study delves into the advantages and disadvantages of various advanced solution synthesis methods, providing detailed insights. Furthermore, the positive effects of hierarchical structural materials constructed using these methods in tactile sensors and their intelligent applications are also discussed in depth. Finally, the challenges and future opportunities faced by this emerging field are summarized.

Electrohydrodynamic (EHD) printing technique, which deposits micro/nanostructures through high electric force, has recently attracted significant research interest owing to their fascinating characteristics in high resolution (<1 μm), wide material applicability (ink viscosity 1-10 000 cps), tunable printing modes (electrospray, electrospinning, and EHD jet printing), and compatibility with flexible/wearable applications. Since the laboratory level of the EHD printed electronics' resolution and efficiency is gradually approaching the commercial application level, an urgent need for developing EHD technique from laboratory into industrialization have been put forward. Herein, we first discuss the EHD printing technique, including the ink design, droplet formation, and key technologies for promoting printing efficiency/accuracy. Then we summarize the recent progress of EHD printing in fabrication of displays, organic field-effect transistors (OFETs), transparent electrodes, and sensors and actuators. Finally, a brief summary and the outlook for future research effort are presented.

Magnesium-ion batteries (MIBs) have promising applications because of their high theoretical capacity and the natural abundance of magnesium Mg. However, the kinetic performance and cyclic stability of cathode materials are limited by the strong interactions between Mg ions and the crystal lattice. Here, we demonstrate the unique Mg²⁺-ion storage mechanism of a hierarchical accordion-like vanadium oxide/carbon heterointerface (V₂O₃@C), where the V₂O₃ crystalline structure is reconstructed into a MgV₃O₇∙H₂O phase through an anodic hydration reaction upon first cycle, for the improved kinetic and cyclic performances. As verified by in situ/ex situ spectroscopic and electrochemical analyses, the fast charge transfer kinetics of the V₂O₃@C cathode were due to the crystal-reconstruction and chemically coupled heterointerface. The V₂O₃@C demonstrated an ultrahigh rate capacity of 130.4 mAh g⁻¹ at 50 000 mA g⁻¹ and 1000 cycles, achieving a Coulombic efficiency of 99.6%. The high capacity of 381.0 mA h g⁻¹ can be attributed to the reversible Mg²⁺-ion intercalation mechanism observed in the MgV₃O₇∙H₂O phase using a 0.3 M Mg(TFSI)₂/ACN(H₂O) electrolyte. Additionally, within the voltage range of 2.25 V versus Mg/Mg²⁺, the V₂O₃@C exhibited a capacity of 245.1 mAh g⁻¹ when evaluated with magnesium metal in a 0.3 M Mg(TFSI)₂ + 0.25 M MgCl₂/DME electrolyte. These research findings have important implications for understanding the relationship between the Mg-ion storage mechanism and reconstructed crystal phase of vanadium oxides as well as the heterointerface reconstruction for the rational design of MIB cathode materials.

Organic solar cells (OSCs) have emerged as a promising solution for sustainable energy production, offering advantages such as a low carbon footprint, short energy payback period, and compatibility with eco-solvents. However, the use of hazardous solvents continues to dominate the best-performing OSCs, mainly because of the challenges of controlling phase separation and domain crystallinity in eco-solvents. In this study, we combined the solvent vapor treatment of CS₂ and thermal annealing to precisely control the phase separation and domain crystallinity in PM6:M-Cl and PM6:O-Cl systems processed with the eco-solvent o-xylene. This method resulted in a maximum power conversion efficiency (PCE) of 18.4%, which is among the highest values reported for sustainable binary OSCs. Furthermore, the fabrication techniques were transferred from spin coating in a nitrogen environment to blade printing in ambient air, retaining a PCE of 16.0%, showing its potential for high-throughput and scalable production. In addition, a comparative analysis of OSCs processed with hazardous and green solvents was conducted to reveal the differences in phase aggregation. This work not only underscores the significance of sustainability in OSCs but also lays the groundwork for unlocking the full potential of open-air-printable sustainable OSCs for commercialization.