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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.

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.

Natural polyphenol-functionalized liquid metal to dissipate CPU interfacial heat for efficient thermal management systems

Universal van der Waals integrated metal electrode approach drives integrated circuit advanced process development

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.

The anomalous Hall effect (AHE) that associated with the Berry curvature of occupied electronic states in momentum-space is one of the intriguing aspects in condensed matter physics, and provides an alternative for potential applications in topological electronics. Previous experiments reported the tunable Berry curvature and the resulting magnetization switching process in the AHE based on strain engineering or chemical doping. However, the AHE modulation by these strategies are usually irreversible, making it difficult to realize switchable control of the AHE and the resultant spintronic applications. Here, we demonstrated the switchable control of the Berry-curvature-related AHE by electrical gating in itinerant ferromagnetic Cr₇Te₈ with excellent ambient stability. The gate-tunable sign reversal of the AHE can be attributed to the redistribution of the Berry curvature in the band structure of Cr₇Te₈ due to the intercalation-induced change in the Fermi level. Our work facilitates the applications of magnetic switchable devices based on gate-tunable Berry curvature.

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.

Transition metal dichalcogenides (TMDs) are a promising candidate for developing advanced sensors, particularly for day and night vision systems in vehicles, drones, and security surveillance. While traditional systems rely on separate sensors for different lighting conditions, TMDs can absorb light across a broad-spectrum range. In this study, a dual vision active pixel image sensor array based on bilayer WS₂ phototransistors was implemented. The bilayer WS₂ film was synthesized using a combined process of radio-frequency sputtering and chemical vapor deposition. The WS₂-based thin-film transistors (TFTs) exhibit high average mobility, excellent I_on/I_off, and uniform electrical properties. The optoelectronic properties of the TFTs array exhibited consistent behavior and can detect visible to near-infrared light with the highest responsivity of 1821 A W⁻¹ (at a wavelength of 405 nm) owing to the photogating effect. Finally, red, green, blue, and near-infrared image sensing capabilities of active pixel image sensor array utilizing light stencil projection were demonstrated. The proposed image sensor array utilizing WS₂ phototransistors has the potential to revolutionize the field of vision sensing, which could lead to a range of new opportunities in various applications, including night vision, pedestrian detection, various surveillance, and security systems.

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.

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.