Triboelectric nanogenerators (TENGs) as a clean energy-harvesting technology are experiencing significant growth in the pursuit of carbon neutrality, accompanied by the increasing use of environmentally friendly biomaterials. However, biomaterials exhibit inferior triboelectric properties compared with petro-materials, hindering the development of bio-based TENGs. Here, leveraging the crystal boundary-tuning strategy, we develop a chitosan aerogel-based TENG (CS-TENG) that is capable of delivering power density over 116 W m–2, beyond that of the previous reports for CS-TENG by an order of magnitude. With a high output voltage of 3200 V, the CS-TENG directly illuminated 1000 LEDs in series. Notably, the CS aerogel exhibits self-healing, waste recycling and gas-sensitive properties, ensuring the long-term durability, environmental benignity and sensing characteristics of the CS-TENG. Furthermore, a breath-activated mask-integrated CS-TENG ammonia monitoring system is engineered, which accurately detects changes in ammonia concentration within the range of 10–160 ppm, enabling real-time monitoring of ammonia in the environment. Our results set a record for the ultrahigh power density of CS-TENG, representing a significant advancement in the practical application of TENGs.
Ternary MAX phases, characterized by the chemical formula M₂AX, represent a group of layered materials with hexagonal lattices. These MAX phases have been the subject of extensive experimental and theoretical studies. Formation energy and thermodynamic calculations indicate that MAX phases containing late transition metals, such as Rh, Ru, Pt, Pd, Co, and Ni, are unlikely to form. Here, we introduce an alternative family of orthorhombic and monoclinic materials, the LAX phases, which exhibit similarities to MAX phases in terms of their layered structure and A and X elements. However, LAX materials incorporate late transition metals in place of the early transition metals. Advanced techniques for predicting the crystal structure of materials, coupled with data-driven materials research and machine learning algorithms, were employed to investigate the stable structures containing transition metals from the last groups of the d-block elements. The analyses revealed 207 ternary LAX systems that demonstrate robust stability against decomposition, with 100 of these systems showing dynamic stability. An in-depth examination of the top 10 structures revealed five LAX systems that are phase stable and exhibit superior mechanical properties, outperforming MAX phase counterparts in Young's modulus, stiffness, and hardness. These findings indicate that many LAX phase structures are viable candidates for future synthesis, highlighting the potential of heuristic-based structure searches in material discovery.
Dynamic detection is crucial for intelligent vision systems, enabling applications like autonomous vehicles and advanced surveillance. Event-based sensors, which convert illumination variations into sparse event spikes, are highly effective for dynamic detection with low data redundancy. However, current event-based vision sensors with simplified photosensitive capacitor structures face limitations, particularly in their spectral response, which hinders effective information acquisition in multispectral scenes. Here, we introduce a two-terminal thin-film event-based vision sensor that innovatively integrates an inorganic oxide p–n junction with the pyro-phototronic effect, synergistically combining the photovoltaic and pyroelectric mechanisms. This innovation enables spiking signals with a tenfold increase in responsivity, a dynamic range of 110 dB, and an extended spectral response from ultraviolet (UV) to near-infrared (NIR). With a thin-film sensor array, these spiking signals accurately extract fingerprint edge features even under low-light conditions, benefiting from high sensitivity to minor luminance variations. Additionally, the sensors' broadband spiking response captures richer information, achieving 99.25% accuracy in multispectral dynamic gesture recognition while reducing data processing by over 65%. This approach effectively eliminates redundant data while minimizing information loss, offering a promising alternative to current dynamic perception technologies.
The large-scale touch position sensor as a key human–machine interface toolkit holds immense significance in smart city and home construction. However, prior alternatives suffer from high power consumption, material limitations, and implementation costs. Herein, a self-powered and scalable touch position strategy that integrates contact electrification with a screen-printing technique is proposed. Simply, high-impedance electrodes with stagger patterns are screen-printed onto various substrates before being covered with a dielectric layer. The locating mechanism originates from the touch-generated triboelectric charge shunt effect in the electrodes. The screen-printing parameters that affect the positional accuracy are discussed in detail. Leveraging this strategy, we realize a tailorable and large-scale triboelectric touch position sensor (LTTPS) that offers flexibility, self-powered capability, and a minimized signal channel, making it suitable for various practical scenarios. Demonstrations include an intelligent bookshelf mat with book management functionality, a rollable and foldable film-like keyboard, and a 4 m2 walk-tracking carpet. The LTTPS in this work provides an appealing alternative for large-scale touch positioning and enriches human–machine interaction.
The performance of optoelectronic materials has been booming developed. Yet, the traditional solar cell manufacturing techniques, such as spin coating and screen printing, have significant limitations that seem to hinder the further development of solar cell technology. Compared with traditional manufacturing processes, additive manufacturing (AM) boasts advantages such as flexibility in the printing process, precise control over material deposition, and simpler procedures. These features provide a foundation for further enhancing solar cell performance and expanding their applications. This review outlines the superiority of AM compared with traditional solar cell manufacturing methods and highlights how AM has addressed specific challenges currently faced by solar cells. The most widely researched solar cell structures in recent years were briefly reviewed with summarizing their advantages and disadvantages. Then, a comprehensive overview of different manufacturing processes, including traditional printing methods and AM, is presented. Especially, their workflows, characteristics, and impressive innovative applications in solar cell manufacturing were discussed in detail. Finally, based on the current state of research, the review reflects on the future prospects of applying AM technology in space solar energy production, such as integrated printing with protective outer layers together with the solar cells, customized functional structure printing, flexible large-scale printing, and printing of high-performance novel materials with nanoscale and microscale structures.
As social networks and related data processes have grown exponentially in complexity, the efficient resolution of combinatorial optimization problems has become increasingly crucial. Recent advancements in probabilistic computing approaches have demonstrated significant potential for addressing these problems more efficiently than conventional deterministic computing methods. In this study, we demonstrate a highly durable probabilistic bit (p-bit) device utilizing two-dimensional materials, specifically hexagonal boron nitride (h-BN) and tin disulfide (SnS2) nanosheets. By leveraging the inherently stochastic nature of electron trapping and detrapping at the h-BN/SnS2 interface, the device achieves durable probabilistic fluctuations over 108 cycles with minimal energy consumption. To mitigate the static power consumption, we integrated an active switch in series with a p-bit device, replacing conventional resistors. Furthermore, employing the pulse width as the control variable for probabilistic switching significantly enhances noise immunity. We demonstrate the practical application of the proposed p-bit device in implementing invertible Boolean logic gates and subsequent integer factorization, highlighting its potential for solving complex combinatorial optimization problems and extending its applicability to real-world scenarios such as cryptographic systems.
Hydrogen stands as a promising energy carrier that plays a pivotal role in addressing global sustainability and achieving carbon neutrality. The conversion of hydrogen energy through fuel cells has emerged as a central technology in this pursuit. Notably, protonic ceramic fuel cells (PCFCs) hold potential for the future hydrogen energy ecosystem, owing to their impressive energy conversion efficiencies at low-to-intermediate temperatures (300–750°C). It is becoming increasingly evident that the development of PCFC technology relies on advancements in the cathode, as oxygen-involved reactions often exhibit sluggish kinetics. In this comprehensive review, we aim to provide an overview of the current state of knowledge concerning the design of advanced cathodes for PCFCs. This includes discussing key descriptors for cathodes, methods for characterizing material properties, and functionalization techniques to enhance electrode performance. Finally, we present insights into future research directions.
Nickel-rich layered oxides (LiNixCoyMnzO2, NCM) are among the most promising cathode materials for high-energy lithium-ion batteries, offering high specific capacity and output voltage at a relatively low cost. However, industrial-scale co-precipitation presents significant challenges, particularly in maintaining particle sphericity, ensuring a stable concentration gradient, and preserving production yield when transitioning from lab-scale compositions. This study addresses a critical issue in the large-scale synthesis of nickel-rich NCM (x = 0.8381): nickel leaching, which compromises particle uniformity and battery performance. To mitigate this, we optimize the reaction process and develop an artificial intelligence-driven defect prediction system that enhances precursor stability. Our domain adaptation based machine learning model, which accounts for equipment wear and environmental variations, achieves a defect detection accuracy of 97.8% based on machine data and process conditions. By implementing this approach, we successfully scale up NCM precursor production to over 2 tons, achieving 83% capacity retention after 500 cycles at a 1C rate. In addition, the proposed approach demonstrates the formation of a concentration gradient in the composition and a high sphericity of 0.951 (±0.0796). This work provides new insights into the stable mass production of NCM precursors, ensuring both high yield and performance reliability.