Publications

Abstract Triboelectric nanogenerator (TENG) which harvests ubiquitous ambient mechanical energy is a promising power source that can meet the distributed energy demand in the internet of things, wearable electronics, etc. However, the available output of TENG is severely limited by the saturated polarized charge density, small intrinsic capacitance, and large matching impedance. Herein, an effective power management strategy is proposed that flips the free charges on the conductive layer through a controlled LC oscillating circuit composed of the diode, switch, inductor, and the intrinsic capacitor of TENG. In this way, the equivalent charge density reaches a level higher than the saturated polarized charge density. The simulation and experiments show that the limit of energy output can be exceeded under arbitrary load resistance, especially for the low‐impedance common electronics. It is believed that such a general, low‐cost, and highly effective strategy can further broaden the applications of TENG devices across the fields and probably be a new performance evaluation standard for TENG.

As a crucial component within the power industry, the hydraulic turbine speed control system significantly plays a vital role in the safe and stable operation of hydropower stations. The intelligent operation and maintenance of this system is a vital means to ensure the safety, stability, and economy of the unit. The hydropower plant has accumulated extensive fault text data related to the hydraulic turbine speed control system over the years, which has yet to be effectively mined and utilized. To address these issues, this paper proposes a novel method using BERTWWM-BiLSTM-MHA-CRF for constructing a fault knowledge graph of hydraulic turbine speed control system. Initially, the knowledge graph schema is designed, followed by an analysis of the recording characteristics of the hydraulic turbine speed control system fault text. This is accompanied by the cleaning and labeling of unstructured text. Subsequently, an entity extraction model utilizing the BERTWWM-BiLSTM-MHA-CRF framework is developed to facilitate the intelligent extraction of entities and relationships. Finally, the triples, consisting of entities and relationships, are stored in the Neo4j graph database to finalize the construction and visualization of the fault knowledge graph, along with the proposed application process for auxiliary decision-making. The data processing methodology outlined in this paper, based on the graph schema design, effectively produces high-quality datasets. Furthermore, compared to the traditional model and mainstream large language models, the BERTWWM-BiLSTM-MHA-CRF model demonstrates superior entity extraction performance. Finally, combining fault instance validation, it demonstrates that the knowledge graph provides effective support for fault diagnosis in the hydraulic turbine speed control system.

The microstructure of a magnetron sputtered Cr(20 nm)/Sm–Co(20 nm)/Fe(20 nm)/Cr(5 nm) exchange spring magnet was studied using an advanced analytical transmission electron microscopy to better understand the relationship between its chemistry and structure to enhance the energy product. It is shown that the Fe atoms diffuse into the well textured Sm–Co layer, causing a transition from the stoichiometric Sm2Co7 near the Sm–Co/Cr interface to a defected Sm(Co,Fe)5 in the Sm–Co–Fe intermixed area. The graded intermixed layer between Sm–Co and pure Fe gives rise to a compositionally enhanced energy product.

Triboelectric nanogenerator (TENG) has been proven to be efficient for harvesting water wave energy, which is one of the most promising renewable energy sources. In this work, a TENG with a silicone rubber/carbon black composite electrode was designed for converting the water wave energy into electricity. The silicone-based electrode with a soft texture provides a better contact with the dielectric film. Furthermore, a spring structure is introduced to transform low-frequency water wave motions into high-frequency vibrations. They together improve the output performance and efficiency of TENG. The output performances of TENGs are further enhanced by optimizing the triboelectric material pair and tribo-surface area. A spring-assisted TENG device with the segmented silicone rubber-based electrode structure was sealed into a waterproof box, which delivers a maximum power density of 2.40 W m–3, as triggered by the water waves. The present work provides a new strategy for fabricating high-performance TENG devices by coupling flexible electrodes and spring structure for harvesting water wave energy.

The kernmantle construction, a kind of braiding structure that is characterized by the kern absorbing most of the stress and the mantle protecting the kern, is widely employed in the field of loading and rescue services, but rarely in flexible electronics. Here, a novel kernmantle electronic braid (E-braid) for high-impact sports monitoring, is proposed. The as-fabricated E-braids not only demonstrate high strength (31 Mpa), customized elasticity, and nice machine washability (>500 washes) but also exhibit excellent electrical stability (>200 000 cycles) during stretching. For demonstration, the E-braids are mounted to different parts of the trampoline for athletes' locomotor behavior monitoring. Furthermore, the E-braids are proved to act as multifarious intelligent sports gear or wearable equipment such as electronic jump rope and respiration monitoring belt. This study expands the kernmantle structure to soft flexible electronics and then accelerates the development of quantitative analysis in modern sports industry and athletes' healthcare.

With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 (mAP@0.5) and 0.963 (mAP@0.5), respectively.

Harvesting mechanical energy via a triboelectric nanogenerator (TENG) is a promising strategy for solving energy problems. However, it is necessary to develop an effective and safe energy managing circuit for preventing high voltage breaking electronic devices. Here, a universal managing circuit is developed to optimize TENG's output performance, which for the first time allows the TENG to safely power various sensor systems with a safe and stable voltage. Based on the circuit, TENG's output can be transformed into a stable voltage with tunable amplitude, while an enhanced short-circuit current of 94 mA with an energy loss lower than 5% is achieved. For demonstrations, three different types of TENGs, respectively, targeting at ocean energy, wind energy, and walking energy have been prepared to reveal the capability of the circuit. This study offers a strategy to greatly enhance the output performance of TENGs to provide useful guidance for constructing self-powered and distributed sensor systems.