In recent years, the internet of things (IoT) has been progressing rapidly with the integration of technologies in various fields. At this stage, triboelectric nanogenerator (TENG) technology based on the...

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A novel hybridization scheme is proposed with electromagnetic transduction to improve the power density of piezoelectric energy harvester (PEH) in this paper. Based on the basic cantilever piezoelectric energy harvester (BC-PEH) composed of a mass block, a piezoelectric patch, and a cantilever beam, we replaced the mass block by a magnet array and added a coil array to form the hybrid energy harvester. To enhance the output power of the electromagnetic energy harvester (EMEH), we utilized an alternating magnet array. Then, to compare the power density of the hybrid harvester and BC-PEH, the experiments of output power were conducted. According to the experimental results, the power densities of the hybrid harvester and BC-PEH are, respectively, 3.53 mW/cm3 and 5.14 μW/cm3 under the conditions of 18.6 Hz and 0.3 g. Therefore, the power density of the hybrid harvester is 686 times as high as that of the BC-PEH, which verified the power density improvement of PEH via a hybridization scheme with EMEH. Additionally, the hybrid harvester exhibits better performance for charging capacitors, such as charging a 2.2 mF capacitor to 8 V within 17 s. It is of great significance to further develop self-powered devices.

Due to the low output power of triboelectric nanogenerators (TENGs) in harvesting wind energy, this work proposes a hybridization scheme with electromagnetic generators (EMGs) to improve the power density of TENGs. Then, a novel configuration is designed and experiments of impedance matching and output power are conducted to compare the power density of triboelectric nanogenerators with/without electromagnetic generators under a wind speed of 5.5 m s−1. According to the experimental results, the power density of the hybrid generator is 29.8 times higher than that of the triboelectric nanogenerator without electromagnetic generators (TENG‐WEMGs). To further demonstrate the output performance of the hybrid generator, experiments of charging capacitors and powering electronics are implemented at the same wind speed. Based on the experimental results, a capacitor of 2.2 mF is charged to 25.7 V within 20 s, 170 LEDs are lit, and the Bluetooth tracker is driven to transmit signals in real time. In addition, this work investigates the influences of different resistant loads of EMG on the average power of TENGs. This work can be of great significance to further develop self‐powered sensors.

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To provide a robust working environment for TENGs, most TENGs are designed as sealed structures that isolate TENGs from the external environment, and thus their operating conditions cannot be directly monitored. Here, for the first time, we propose an artificial neural network for interface defect detection and identification of triboelectric nanogenerators via training voltage waveforms. First, interface defects of TENGs are classified and their causes are discussed in detail. Then we build a lightweight artificial neural network model which shows high sensitivity to voltage waveforms and low time complexity. The model takes 2.1 s for training one epoch, and the recognition rate of defect detection is 98.9% after 100 epochs. Meanwhile, the model successfully demonstrates the learning ability for low-resolution samples (100 × 75 pixels), which can identify six types of TENG defects, such as edge fracture, adhesion, and abnormal vibration, with a high recognition rate of 93.6%. This work provides a new strategy for the fault diagnosis and intelligent application of TENGs.

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