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Minimizing surface defects in perovskite films is crucial for suppressing non-radiative recombination and enhancing device performance. Herein, we propose the use of N-bromosuccinimide (NBS), a small molecule containing Lewis base carbonyl groups (C=O), to improve the quality of RbCsMAFA mixed-cation perovskite films. This surface treatment effectively reduces non-radiative charge-carrier recombination, in particular through the passivation of surface defects related to undercoordinated Pb2+ ions and halide vacancies, and significantly accelerates charge extraction from the perovskite into the Spiro-OMeTAD hole transporter. Consequently, NBS-treated PerSCs achieve a power conversion efficiency (PCE) of 18.24%, representing an 11% relative increase over the control device (16.48%). This enhancement is mainly attributed to a Voc gain of up to 40 mV and modifications in the recombination dynamics. Supporting evidence from impedance spectroscopic analyses further confirms enhanced energy-level alignment and reduced interfacial losses, improved charge transport as well as prolonged charge lifetimes within the devices. This work provides a simple yet effective approach to reduce the non-radiative recombination losses towards more efficient and stable PerSCs.

In recent years, perovskite has been widely adopted in series-connected monolithic tandem solar cells (TSCs) to overcome the Shockley–Queisser limit of single-junction solar cells. Perovskite–organic TSCs, comprising a wide-bandgap (WBG) perovskite solar cell (pero-SC) as the front cell and a narrow-bandgap organic solar cell (OSC) as the rear cell, have recently drawn attention owing to the good stability and potential high power conversion efficiency (PCE) 1 – 4 . However, WBG pero-SCs usually exhibit higher voltage losses than regular pero-SCs, which limits the performance of TSCs 5 , 6 . One of the main obstacles comes from interfacial recombination at the perovskite–C 60 interface, and it is important to develop effective surface passivation strategies to pursue higher PCE of perovskite–organic TSCs 7 . Here we exploit a new surface passivator cyclohexane 1,4-diammonium diiodide (CyDAI 2 ), which naturally contains two isomeric structures with ammonium groups on the same or opposite sides of the hexane ring (denoted as cis -CyDAI 2 and trans -CyDAI 2 , respectively), and the two isomers demonstrate completely different surface interaction behaviours. The cis -CyDAI 2 passivation treatment reduces the quasi-Fermi-level splitting–open circuit voltage ( V oc ) mismatch of the WBG pero-SCs with a bandgap of 1.88 eV and enhanced its V oc to 1.36 V. Combining the cis -CyDAI 2 -treated perovskite and the organic active layer with a narrow bandgap of 1.27 eV, the constructed monolithic perovskite–organic TSC demonstrates a PCE of 26.4% (certified as 25.7%). A new surface passivator cyclohexane 1,4-diammonium diiodide naturally contains two isomeric structures with ammonium groups on the same or opposite sides of the hexane ring, and the two isomers demonstrate completely different surface interaction behaviours.

Regulating the composition and valence states of layered O3‐phase sodium‐ion battery cathode materials can effectively mitigate issues related to complex phase transitions and poor air stability. However, further research is needed to optimize the controllable design of these structures and to better understand the transition mechanisms between different hierarchical phases. Herein, precise regulation of radial sodium‐ion concentration, phase structure, and transition metal average valence of P/O cathode was realized through precursor‐based secondary heterogeneous coprecipitation and solid‐state sintering. Radial scanning transmission electron microscopy and electron energy loss spectroscopy characterization confirmed elemental migration during sintering, resulting in a gradient distribution of sodium content, phase structure, and transition metal valence states. This radially gradient continuous P2/O3–O3 composite without obvious phase interface reduces the barrier of sodium‐ion transport at the phase interface to mitigates volume changes from O3–O3′ phase transitions, inhibits Na + /H + exchange and acid erosion, and enhances moisture/carbon dioxide resistance, kinetic performance, and cycling stability. Consequently, after 10 h of exposure to 82% humidity and 3330 ppm CO 2 concentration, the first‐cycle charge capacity of designed NM + 0.4 μm was 103.8 mAh g −1 , while the capacity loss reduced from 50.12% to 12.35%. This study presents a novel approach to enhancing the stability of layered cathode materials for sodium‐ion batteries.

Accurate identification of coal mine safety risks is a crucial foundation for mitigating coal mine disasters. This study integrates social network analysis (SNA), the bow-tie model, and association rule mining to systematically analyze safety accident data from a coal mine. A total of 85 causative factors were extracted from 72 accidents and assessed through frequency, marginal influence, and centrality indicators to identify key risk contributors. The bow-tie model was employed to structure these causes into a safety risk control framework based on preventive and mitigation measures. Furthermore, the Apriori algorithm was applied to uncover hidden associations among gas safety risk factors, revealing critical compound relationships among factors such as inadequate safety management, insufficient inspections, high incidence of “three violations”, and poor safety education. The findings indicate that management and human-related factors, particularly the absence of effective safety management systems, safety violations, and inadequate training, are the primary contributors to accidents in coal mines. Consequently, it is imperative to address these issues collectively to ensure effective risk prevention in such environments. The coal mine safety risk causality control model established in conjunction with the butterfly diagram model holds significant theoretical and practical value for coal mine safety production.

Deep multimodal learning has excelled in tasks involving vision, language, and audio modalities. Nevertheless, their performance on tail classes exhibits significant degradation under the long-tailed distributions common in real-world data, meanwhile related fusion schemes often provide only limited treatment of modality-specific uncertainty and rarely incorporate explicit mechanisms for class-level fairness. To address these information discrepancies, we present a framework that integrates evidential reasoning with deep learning–Uncertainty-Quantified Multimodal Learning for Long-Tailed Classification (UMuLT). The framework includes: (i) an uncertainty-gated evidential fusion module that adaptively down-weights unreliable modalities; (ii) an exponential moving average (EMA) fairness regularizer that dynamically amplifies tail-class gradients; and (iii) a cross-modal consistency regularizer optimized in two stages: tail specialization with lightweight adapters on tail-class data to obtain a balanced initialization, followed by end-to-end fine-tuning. The effectiveness and practicality of our method are verified on three long-tailed benchmarks for multimodal classification. Experiments show consistent gains over strong baselines in overall metrics, calibration, and tail subset performance. Statistical significance tests confirm the superiority of the proposed framework.

Magnetoelastic effect characterizes the change of materials’ magnetic properties under mechanical deformation, which is conventionally observed in some rigid metals or metal alloys. Here we show magnetoelastic effect can also exist in 1D soft fibers with stronger magnetomechanical coupling than that in traditional rigid counterparts. This effect is explained by a wavy chain model based on the magnetic dipole-dipole interaction and demagnetizing factor. To facilitate practical applications, we further invented a textile magnetoelastic generator (MEG), weaving the 1D soft fibers with conductive yarns to couple the observed magnetoelastic effect with magnetic induction, which paves a new way for biomechanical-to-electrical energy conversion with short-circuit current density of 0.63 mA cm −2 , internal impedance of 180 Ω, and intrinsic waterproofness. Textile MEG was demonstrated to convert the arterial pulse into electrical signals with a low detection limit of 0.05 kPa,  even with heavy perspiration or in underwater situations without encapsulations. The authors invented a textile magnetoelastic generator, weaving 1D soft fibers with conductive yarns to couple the observed magnetoelastic effect with magnetic induction, which paves a new way for biomechanical-to-electrical energy conversion.

Arterial pulse waves are regarded as vital diagnostic tools in the assessment of cardiovascular disease (CVD). Because of their high sensitivity, rapid response time, wearability, and low cost, textile triboelectric nanogenerators (TENGs) are emerging as a compelling biotechnology for wearable pulse wave monitoring. We discuss sensing mechanisms for pulse-to-electricity conversion, analytical models for calculating cardiovascular parameters, and application scenarios for textile TENGs. We provide a prospective on the challenges that limit the wider application of this technology and suggest some future research directions. In the future, textile TENGs are expected to make an impact in the fields of wearable pulse wave monitoring and CVD diagnosis. Cardiovascular disease (CVD) is the main cause of death globally, and arterial pulse waves are vital diagnostic tools for assessing CVD.Textile triboelectric nanogenerators (TENGs) provide a highly sensitive, self-powered, and low-cost biotechnology for pulse-to-electricity conversion while maintaining the advantageous features of textiles such as superior wearing comfort and stability.Clinical parameters, such as heart rate, pulse wave velocity, and blood pressure, can be calculated from pulse waveforms acquired by textile TENGs for CVD diagnosis in a continuous, timely, and accurate manner.Textile TENGs can be further connected with terminals to enable real-time personalized healthcare monitoring and clinical information exchange with telemedicine in a non-clinical environment.

The magnetoelastic effect—the variation of the magnetic properties of a material under mechanical stress—is usually observed in rigid alloys, whose mechanical modulus is significantly different from that of human tissues, thus limiting their use in bioelectronics applications. Here, we observed a giant magnetoelastic effect in a soft system based on micromagnets dispersed in a silicone matrix, reaching a magnetomechanical coupling factor indicating up to four times more enhancement than in rigid counterparts. The results are interpreted using a wavy chain model, showing how mechanical stress changes the micromagnets’ spacing and dipole alignment, thus altering the magnetic field generated by the composite. Combined with liquid-metal coils patterned on polydimethylsiloxane working as a magnetic induction layer, the soft magnetoelastic composite is used for stretchable and water-resistant magnetoelastic generators adhering conformably to human skin. Such devices can be used as wearable or implantable power generators and biomedical sensors, opening alternative avenues for human-body-centred applications. Micromagnets dispersed in a polymer matrix are used to realize a soft magnetoelastic generator with high magnetomechanical coupling factor, used for wearable and implantable power generation and sensing applications.

Lithium metal batteries hold promise for pushing cell-level energy densities beyond 300 Wh kg −1 while operating at ultra-low temperatures (below −30 °C). Batteries capable of both charging and discharging at these temperature extremes are highly desirable due to their inherent reduction in the need for external warming. Here we demonstrate that the local solvation structure of the electrolyte defines the charge-transfer behaviour at ultra-low temperature, which is crucial for achieving high Li metal Coulombic efficiency and avoiding dendritic growth. These insights were applied to Li metal full-cells, where a high-loading 3.5 mAh cm −2 sulfurized polyacrylonitrile (SPAN) cathode was paired with a onefold excess Li metal anode. The cell retained 84% and 76% of its room temperature capacity when cycled at −40 and −60 °C, respectively, which presented stable performance over 50 cycles. This work provides design criteria for ultra-low-temperature lithium metal battery electrolytes, and represents a defining step for the performance of low-temperature batteries. Charging and discharging Li-metal batteries (LMBs) at low temperatures is problematic due to the sluggish charge-transfer process. Here the authors discuss the roles of solvation structures of Li-ions in the charge-transfer kinetics and design an electrolyte to enable low-temperature operations of LMBs.

This study considered the role of coal as China’s basic energy source and examines the development of the coal industry. We focused on the intelligent development of coal mines, and introduced the “Chinese mode” of intelligent mining in underground coal mines, which uses complete sets of technical equipment to propose classification and grading standards. In view of the basic characteristics and technical requirements of intelligent coal mine systems, we established a digital logic model and propose an information entity and knowledge map construction method. This involves an active information push strategy based on a knowledge demand model and an intelligent portfolio modeling and distribution method for collaborative control of coal mines. The top-level architecture of 5G+ intelligent coal mine systems combines intelligent applications such as autonomous intelligent mining, human–machine collaborative rapid tunneling, unmanned auxiliary transportation, closed-loop safety control, lean collaborative operation, and intelligent ecology. Progress in intelligent mining technology was described in terms of a dynamic modified geological model, underground 5G network and positioning technology, intelligent control of the mining height and straightness of the longwall working face, and intelligent mining equipment. The development of intelligent coal mines was analyzed in terms of its imbalances, bottlenecks, and the compatibility of large-scale systems. Implementation ideas for promoting the development of intelligent coal mines were proposed, such as establishing construction standards and technical specifications, implementing classification and grading standards according to mining policy, accelerating key technology research, and building a new management and control model.