Congzhen Xie

Passive radiative cooling (PRC) technology, characterized by zero-energy cooling capability, has shown great promise for thermal management of high-voltage electrical equipment. However, such application scenarios impose stringent multifunctional requirements on PRC coatings, including superhydrophobicity to resist surface contamination, as well as high electrical insulation strength to prevent surface flashover. Here, we report a co-assembly strategy for constructing multifunctional PRC coatings through the in situ integration of a metal-organic framework (MOF) with a silica precursor. The appropriately tailored bandgap (Eg = 4.25 eV) of the MOF, slightly exceeding the upper photon-energy bound of the measured solar band (4.13 eV), enables suppressed solar absorption while affording a strong mid-infrared emissivity. Meanwhile, the high-Eg SiO2 shell effectively passivates intrinsic insulation defects and the high surface energy associated with MOFs, thereby enhancing both dielectric reliability and superhydrophobicity. Consequently, the optimized PRC coating delivered a temperature drop of 10.6 ± 0.5 °C during outdoor testing, together with robust superhydrophobicity (153.7°) and high wet flashover strength (15.6 kV/cm). This work advances PRC coatings beyond conventional thermal regulation by integrating radiative cooling, surface self-cleaning, and electrical insulation into a unified material platform, opening new opportunities for passive thermal management of high-voltage electrical equipment.

Molecular-level perfluorination simultaneously weakens donor–acceptor interactions and promotes compact chain packing in polyetherimide, enabling superior high-temperature insulation and capacitive energy-storage performance.

High-voltage dielectric polymers are widely employed as insulating materials in power electrical equipment. However, electrical treeing induced by insulation defects poses a serious threat to their operational safety. Herein, a dual-phase field model is developed to characterize electrical tree breakdown in polymers, considering the coupled effects of partial discharge (PD) and electro-thermal-mechanical energy. In the model, the complex morphology, including the conductive and non-conductive tree structures, is simulated by dual-phase field variables, and electron avalanche theory is incorporated to describe PD events within the tree channels. Consequently, an energy-driven calculation theory for electrical tree growth is proposed by correlating the growth rate at tree tips with energy dissipation. To validate the model, three typical electrical trees, i.e., pine, pine-bush mixed, and bush, are simulated, and the results are compared with experimental measurements in terms of the tree length, the fractal dimension, and PD intensity. The comparisons achieve favorable consistency between simulation and experiment in reproducing tree evolution. According to the energy-driven growth theory, the phenomenon where electro-thermal synergy initiates the tree growth and thermal breakdown is the dominant factor at the insulation failure stage is further explained. Furthermore, the electric field modulation by spatial PD charge demonstrates that PD events smooth the distorted electric field at existing branches while concentrating energy at the branch tips, thereby promoting fractal growth of the tree. This model presents a crucial physical framework for simulating electrical tree and advancing a more rational interpretation of its growth mechanism.

A recyclable boroxine-ring polymer dielectric breaks the insulation–thermal tolerance trade-off, delivering 6.35 J cm ⁻³ at 200 °C with >90% efficiency. Dynamic rings enable reprocessing while retaining 97.8% performance after recycling.

The existing polymer dielectrics as insulating packaging media can no longer meet the insulation demands in highly integrated power electronic devices. Self-adaptive dielectrics with nonlinear dielectric response have been explored to eliminate electric field distortion caused by charge accumulation, but traditional strategies based on Schottky barriers result in interface defects. Here, we report polymer dielectric composites with customizable potential wells in recycled melamine foam-derived graphitic carbon nitride frameworks that overcome concerns about interface defects. We demonstrate that potential wells can efficiently capture low-energy charge carriers and release them for rapid transport under high electric fields. Notably, by doping donor or acceptor states into the frameworks, precise control over potential well depth and distribution was achieved, allowing customization of both nonlinear conductivity and threshold electric field strength. This work establishes a generalizable strategy for engineering next-generation self-adaptive dielectrics, enabling intelligent insulation behavior and enhanced reliability in high-field, high-temperature electronic packaging environments.

Polymers, as the preferred material for dielectric capacitors, are required to have the ability to endure both thermal and electrical‐extremes in mainstream pulse systems and renewable energy applications. Nevertheless, the aromatic structural units in high‐temperature resistant polymers inevitably trigger significant conduction losses at high temperatures, limiting breakthroughs in capacitive performance. Here, we report a cross‑linked structure polymer, featuring an interwoven network of aromatic and aliphatic chains. We found that the aliphatic chain from polystyrene‐maleic anhydride can reduce the large interchain spacing in disordered aromatic polymers, alleviating the adverse effect of free volume on conduction loss. Meanwhile, the interlaced structure of aromatic and aliphatic chains can intercept free charges that are easily conducted along the aromatic chain. Consequently, the optimized polymer exhibits discharged energy densities of 7.24 and 5.28 J/cm ³ at 150°C and 200°C, respectively, with the charge–discharge efficiency above 90%. This work reports a scalable and cost‐effective design strategy for high‐performance dielectric polymers, with promising prospects for large‐scale production and practical applications.

The pursuit of power density and integration in advanced electronic devices poses a risk of electrostatic discharge to traditional packaging materials. Albeit optimizing the insulation performance of packaging materials seems to be an effective strategy, long-term charge accumulation will seriously threaten the reliability of devices. Here, we fabricate an adaptive field grading material that can transition between insulating and conductive states, enabling the rapid dissipation of charges during electrostatic discharge events. A silicon carbide-derived nanofiber mat with a “step-by-step” double Schottky barrier was synthesized using electrospinning. The coordination between the insulating epoxy-based matrix and the oriented semiconductor fiber mat achieves desirable electric field grading performance at ultra-low content (0.3 vol%), and the switching electric field of the insulation-conductive transition can be precisely customized through the nanofiber mat content. Given the diversity between insulating polymers and metal oxides, these findings have prominent guiding significance for electrostatic protection of numerous electronic devices.

Data-driven intelligent fault diagnosis breaks conventional rules and achieves advancements in transformer fault diagnosis. However, intelligent models exhibit diagnostic preferences for the normal status due to the scarcity of fault class samples in the field. Thus, this paper proposes a novel transformer fault diagnosis method, which enhances learning efforts for minority classes while accounting for recognition accuracy of the majority, to address the imbalanced sample sizes between normal and fault statuses. Specifically, prioritizing minority classes in imbalanced learning may lead to a decline in diagnostic performance, particularly for the majority. Hence, this paper constructs a generalized class-specific cost-sensitive mechanism based on class-effective sample size (GCS-EN) to adjust learning efforts across classes. Additionally, stochastic configuration networks with direct links (SCNs-wd) is introduced. This model enables structural adaptation and incorporates the proposed cost-sensitive mechanism to improve performance for classes requiring varying levels of learning attention. Experimental results demonstrate that the proposed model achieves superior diagnostic performance for both fault and normal statuses compared to conventional imbalanced learning models, validated on published datasets and field data. The model attains an accuracy (Acc) of 95.34%, average accuracy (Avg-acc) of 93.63%, G-mean of 0.93, and MAUC of 0.96. This work provides a promising solution for power transformer fault diagnosis in practical field applications.