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.
Electrochemical water splitting represents a promising technology for green hydrogen production. To design advanced electrocatalysts, it is crucial to identify their active sites and interpret the relationship between their structures and performance. Materials extensively studied as electrocatalysts include noble-metal-based (e.g., Ru, Ir, and Pt) and non-noble-metal-based (e.g., 3d transition metals) compounds. Recently, advancements in characterization techniques and theoretical calculations have revealed novel and unusual active sites. The present review highlights the latest achievements in the discovery and identification of various unconventional active sites for electrochemical water splitting, with a focus on state-of-the-art strategies for determining true active sites and establishing structure-activity relationships. Furthermore, we discuss the remaining challenges and future perspectives for the development of next-generation electrocatalysts with unusual active sites. By presenting a fresh perspective on the unconventional reaction sites involved in electrochemical water splitting, this review aims to provide valuable guidance for the future study of electrocatalysts in industrial applications.
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.
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.
Due to its non-invasive nature, ultrasound has been widely used for neuromodulation in biological systems, where its application influences the synaptic weights and the process of neurotransmitter delivery. However, such modulation has not been emulated in physical devices. Memristors are ideal electrical components for artificial synapses, but up till now they are hardly reported to respond to ultrasound signals. Here we design and fabricate a HfOx-based memristor on 64°Y-X LiNbO3 single crystal substrate, and successfully realize artificial synapses modulation by shear-horizontal surface acoustic wave (SH-SAW). It is a prominent short-term resistance modulation, where ultrasound has been shown to cause resistance drop for various resistance states, which could fully recover after the ultrasound is shut off. The physical mechanism illustrates that ultrasound induced polarization potential in the HfOx dielectric layer acts on the Schottky barrier, leading to the resistance drop. The emulation of neuron firing frequency modulation through ultrasound signals is demonstrated. Moreover, the joint application of ultrasound and electric voltage yields fruitful functionalities, such as the enhancement of resistance window and synaptic plasticity through ultrasound application. All these promising results provide a new strategy for artificial synapses modulation, and also further advance neuromorphic devices toward system applications.
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.
The pursuit of designing superconductors with high Tc has been a long-standing endeavor. However, the widespread incorporation of doping in high Tc superconductors significantly impacts electronic structure, intricately influencing Tc. 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 Tc superconductors inverse design model called Supercon-Diffusion. It has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors. Supercon-Diffusion is capable of generating superconductors that exhibit high Tc and excels at identifying the optimal doping ratios that yield the peak Tc. 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 Tc superconductors that have not been documented yet. This groundbreaking contribution effectively reduces the search space for high Tc 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.
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.
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.
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.
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 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.
Pathogenic and corrosive bacteria pose a significant risk to human health or economic well-being. The specific, sensitive, and on-site detection of these bacteria is thus of paramount significance but remains challenging. Taking inspiration from immunoassays with primary and secondary antibodies, we describe here a rational design of microbial sensor (MS) under a dual-specificity recognition strategy using Pseudomonas aeruginosa (P. aeruginosa) as the detection model. In the MS, engineered aptamers are served as the primary recognition element, while polydopamine-N-acetyl-D-galactosamine (PDA-Gal NAc) nanoparticles are employed as the secondary recognition element, which will also generate and amplify changes in the output voltage signal. To achieve self-powering capability, the MS is constructed based on a triboelectric nanogenerator (TENG) with the specific aptamers immobilized on the TENG electrode surface. The as-prepared MS-TENG system exhibits good stability in output performance under external forces, and high specificity toward P. aeruginosa, with no cross-reactivity observed. A linear relationship (R2 = 0.995) between the output voltage and P. aeruginosa concentration is established, with a limit of detection estimated at around 8.7 × 103 CFU mL−1. The utilization of PDA-Gal NAc nanoparticles is found to play an important role in enhancing the specific and reliability of detection, and the underlying mechanisms are further clarified by computational simulations. In addition, the MS-TENG integrates a wireless communication module, enabling real-time monitoring of bacterial concentration on mobile devices. This work introduces a pioneering approach to designing self-powered smart microbial sensors with high specificity, using a double recognition strategy applicable to various bacteria beyond P. aeruginosa.
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.
Solar-blind ultraviolet (UV) photodetectors based on p-organic/n-Ga2O3 hybrid heterojunctions have attracted extensive attention recently. Herein, the multifunctional solar-blind photodetector based on p-type poly[N-9′-heptadecanyl-2,7-carbazole-alt-5,5-(4′,7′-di-2-thienyl-2′,1′,3′-benzothiadiazole)] (PCDTBT)/n-type amorphous Ga2O3 (a-Ga2O3) is fabricated and investigated, which can work in the phototransistor mode coupling with self-powered mode. With the introduction of PCDTBT, the dark current of such the a-Ga2O3-based photodetector is decreased to 0.48 pA. Meanwhile, the photoresponse parameters of the a-Ga2O3-based photodetector in the phototransistor mode to solar-blind UV light are further increased, that is, responsivity (R), photo-detectivity (D*), and external quantum efficiency (EQE) enhanced to 187 A W-1, 1.3 × 1016 Jones and 9.1 × 104 % under the weak light intensity of 11 μW cm-2, respectively. Thanks to the formation of the built-in field in the p-PCDTBT/n-Ga2O3 type-II heterojunction, the PCDTBT/Ga2O3 multifunctional photodetector shows self-powered behavior. The responsivity of p-PCDTBT/n-Ga2O3 multifunctional photodetector is 57.5 mA W-1 at zero bias. Such multifunctional p-n hybrid heterojunction-based photodetectors set the stage for realizing high-performance amorphous Ga2O3 heterojunction-based photodetectors.
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.
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 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.
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.