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A self-powered and low pressure loss gas flowmeter is presently proposed and developed based on a membrane’s flutter driven triboelectric nanogenerator (TENG). Such a flowmeter, herein named “TENG flowmeter”, is made of a circular pipe in which two copper electrodes are symmetrically fixed and a nonconductive, thin membrane is placed in the middle plane of the pipe. When a gas flows through the pipe at a sufficiently high speed, the membrane will continuously oscillate between the two electrodes, generating a periodically fluctuating electric voltage whose frequency can be easily measured. As demonstrated experimentally, the fluctuation frequency (fF) relates linearly with the pipe flow mean velocity (Um), i.e., fF ∝ Um; therefore, the volume flow rate Q (=Um × A) = C1fF + C2, where C1 and C2 are experimental constants and A is the pipe cross-sectional area. That is, by the TENG flowmeter, the pipe flow rate Q can be obtained by measuring the frequency fF. Notably, the TENG flowmeter has several advantages over some commercial flowmeters (e.g., vortex flowmeter), such as considerable lower pressure loss, higher sensitiveness of the measured flow rate, and self-powering. In addition, the effects of membrane material and geometry as well as flow moisture on the flowmeter are investigated. Finally, the performance of the TENG flowmeter is demonstrated.

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The particle concentration and the mass flow rate are the most important parameters describing the gas–solid two‐phase flow. Herein, a novel method based on triboelectric nanogenerator is proposed for measuring a particle concentration and the mass flow rate in a gas–solid two‐phase pipe flow. The as‐fabricated gas–solid two‐phase flow triboelectric nanogenerator (GS‐TENG) consists of one acrylic base plate, one copper electrode, and one stripe of polytetrafluoroethylene (PTFE) membrane. Different materials can be detected by the GS‐TENG, including organic material, such as flour, and inorganic materials, such as copper and soil. PTFE surface morphology is modified to improve the output performance of the GS‐TENG in the experiments in order to detect the output electrical signal more efficiently. The peak output current generated by the gas–solid two‐phase flow in the GS‐TENG shows a mostly linear relationship with the concentration and mass flow rate. In addition, the transferred charge in the process of the flow also shows a highly linear relationship with the concentration and the mass flow rate, which is consistent with the theoretical derivation for the single electrode TENG. The experimental results demonstrate that the measurement error of the GS‐TENG is less than 2%.

Monitoring the crew of a ship can be performed by combining sensors and artificial intelligence methods to process sensing data. In this study, we developed a deep learning (DL)-assisted minimalist structure triboelectric smart mat system for obtaining abundant crew information without the privacy concerns of taking video. The smart mat system is fabricated using a conductive sponge with different filling rates and a fluorinated ethylene propylene membrane. The proposed dual-channel measurement method improves the stability of the generated signal. Comprehensive crew and cargo monitoring, including personnel and status identification, as well as positioning and counting functions are realized by the DL-assisted triboelectric smart mat system according to the analysis of instant sensory data. Real-time monitoring of crews through fixed and mobile devices improves the ability and efficiency of handling emergencies. The smart mat system provides privacy concerns and an effective way to build ship Internet of Things and ensure personnel safety.

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