Cu nanowire (NW)-based transparent conductors are considered to be highly promising constituents of next-generation flexible transparent electronics. However, the fast oxidation of copper under ambient conditions hinders the use of Cu NWs. Herein, we demonstrate a low-cost and scalable approach for preparing a ZnO shell on the surface of Cu NWs under ambient conditions. The covered ZnO shells enhance the oxidative stability of Cu NWs. The optical and electrical properties of ZnO@Cu NWs remain similar to the original performance of the Cu NWs (for example, before encapsulating: 13.5 Ω/sq. at 84.3%, after encapsulating: 19.2 Ω/sq. at 86.7%), which indicates that encapsulation with a ZnO shell enables the preservation of the transparency and conductivity of Cu NW networks. More importantly, the ZnO@Cu NWs exhibit excellent stability in terms of long-term storage, hot/humid environments, and strong oxidizing atmosphere/solution.

Aqueous zinc–iodine batteries have garnered extensive attention due to their low cost, high safety and favorable voltage characteristics. However, their performance is limited by the severe shuttle effect and side reactions, which impede operability, energy density and cycling stability. This perspective begins by outlining the configuration of zinc–iodine batteries, detailing their chemical redox mechanism and associated challenges. It then shifts focus to zinc salt-based eutectic electrolytes, exploring their fundamental properties and potential applications in zinc batteries. The role and functions of eutectic electrolytes in zinc–iodine batteries are emphasized in following, highlighting their achievements in enhancing stability and energy density. Given that research on eutectic electrolytes is still in its infancy, remaining challenges and future directions are also discussed, offering insights for the rational design of advanced eutectic electrolytes and zinc–iodine batteries.

Traditional electronic pianos mostly adopt a gantry type and a large number of rigid keys, and most keyboard sensors of the electronic piano require additional power supply during playing, which poses certain challenges for portable electronic products. Here, we demonstrated a fluorinated ethylene propylene (FEP)-based electret nanogenerator (ENG), and the output electrical performances of the ENG under different external pressures and frequencies were systematically characterized. At a fixed frequency of 4 Hz and force of 4 N with a matched load resistance of 200 MΩ, an output power density of 20.6 mW/cm² could be achieved. Though the implementation of a signal processing circuit, ENG-based, self-powered pressure sensors have been demonstrated for self-powered, flexible electronic pianos. This work provides a new strategy for electret nanogenerators for self-powered sensor networks and portable electronics.

Ferroelectric varactors should have high tunability at low permittivity and a working electric field to obtain better impedance matching and stable tunability. In this work, (Ba_0.91Ca_0.09)(Sn_xZr_0.2−xTi_0.8)O₃-2 mol% CuO-1 mol% Li₂CO₃ (abbreviated as BCSZT100x, x = 0.05, 0.10, 0.15 and 0.20, respectively) are prepared to achieve high tunability at low permittivity and a working electric field. The tunable mechanisms are investigated based on crystal structure, micro-morphology and the permittivity-temperature spectrum. The results show that the shrink of oxygen octahedron and weaker interaction force between Sn⁴⁺ and O²⁻ make BCSZT5 ceramic have a higher tunability value of 26.55% at low permittivity (1913) and a working electric field (7.3 kV/cm). The tunability value of BCSZT5 ceramic increases by 58%, while its permittivity decreases by 25%, compared with x = 0. Those advantages make BCSZT5 ceramic have substantial application prospects in varactors.

Sperm morphology detection technology has important research value in diagnosing male infertility. Traditional manual detection methods are time-consuming, labor-intensive, and highly subjective. The sperm morphology analysis method based on deep learning can objectively and efficiently complete the sperm detection task, and has the advantage of end-to-end learning. This study proposed a two-stage sperm morphology analysis method, which used the UNet + + network to segment individual sperm, and then used the Multi-Head Mobilevit Net model based on multi-output classification to achieve one-time detection of the head, midpiece, and principal piece of a single sperm, and integrated the Mixing Loss function into the classification model. Based on the data enhancement technology, the expanded 10,802 improved Papanicolaou stain sperm morphology classification dataset was tested. The results showed that the accuracy of the sperm head, midpiece, and principal piece reached 83.47%, 96.19%, and 94.99%, respectively. This study can quickly realize the automated detection of sperm morphology, providing strong technical support for the development of reproductive medicine.

For practical industrial production scenarios where collected vibration signals are easily interfered by environmental noise and bearing operating conditions are complex and variable, this paper proposes a Double Branch Lightweight Convolutional Neural Network (DBLCNN). The model adopts a dual-branch architecture: the one-dimensional branch enhances feature extraction capability under low signal-to-noise ratio conditions, while the two-dimensional branch improves feature representation while significantly reducing the number of parameters. The complementary fault features extracted by the dual branches effectively enhance the accuracy of fault diagnosis. Under varying operating conditions, the model achieves an average accuracy of 95.58%; with the addition of 0 dB Gaussian white noise, its average accuracy under varying conditions remains at 90.17%. This study demonstrates that, even based on raw vibration signals without cumbersome preprocessing, the model can achieve excellent diagnostic performance in noisy environments and under variable operating conditions.