Underwater communication is a critical and challenging issue, on account of the complex underwater environment. This study introduces an underwater wireless communication approach via Maxwell’s displacement current generated by a triboelectric nanogenerator. Underwater electric field can be generated through a wire connected to a triboelectric nanogenerator, while current signal can be inducted in an underwater receiver certain distance away. The received current signals are basically immune to disturbances from salinity, turbidity and submerged obstacles. Even after passing through a 100 m long spiral water pipe, the electric signals are not distorted in waveform. By modulating and demodulating the current signals generated by a sound driven triboelectric nanogenerator, texts and images can be transmitted in a water tank at 16 bits/s. An underwater lighting system is operated by the triboelectric nanogenerator-based voice-activated controller wirelessly. This triboelectric nanogenerator-based approach can form the basis for an alternative wireless communication in complex underwater environments.

Pervasive and continuous energy solutions are highly desired in the era of the Internet of Things for powering wide-range distributed devices/sensors. Wind energy has been widely regarded as an ideal energy source for distributed devices/sensors due to the advantages of being sustainable and renewable. Herein, we propose a high-performance flag-type triboelectric nanogenerator (HF-TENG) to efficiently harvest widely distributed and highly available wind energy. The HF-TENG is composed of one piece of polytetrafluoroethylene (PTFE) membrane and two carbon-coated polyethylene terephthalate (PET) membranes with their edges sealed up. Two ingenious internal-structure designs significantly improve the output performance. One is to place the supporting sponge strips between the PTFE and the carbon electrodes, and the other is to divide the PTFE into multiple pieces to obtain a multi-degree of freedom. Both methods can improve the degree of contact and separation between the two triboelectric materials while working. When the pair number of supporting sponge strips is two and the degree of freedom is five, the maximum voltage and current of HF-TENG can reach 78 V and 7.5 μA, respectively, which are both four times that of the untreated flag-type TENG. Additionally, the HF-TENG was demonstrated to power the LEDs, capacitors, and temperature sensors. The reported HF-TENG significantly promotes the utilization of the ambient wind energy and sheds some light on providing a pervasive and sustainable energy solution to the distributed devices/sensors in the era of the Internet of Things.

With the development of the smart ocean which contains a large number of wireless sensor nodes, it is a great demand to develop high‐performance marine energy harvesters for powering those sensors. In this work, a highly adaptive hybrid nanogenerator based on triboelectric nanogenerator and electromagnetic generator is proposed. The hybrid nanogenerator can be used for scavenging both wind energy and ocean current energy. The peak power of the hybrid nanogenerator can reach 449.74 mW, which can recharge a 50 mAh‐3.7 V Lithium battery. In addition, it is found that there is a linear relationship between the voltage frequency of the triboelectric nanogenerator and the rotation speed, indicating the hybrid nanogenerator can serve as a flow velocity sensor. A fully self‐powered marine wireless sensor node is fabricated based on the hybrid nanogenerator and management circuit. The demonstrations show that the present hybrid nanogenerator has great potential applications for marine wireless sensing in the scenarios of nearshore, offshore, and underwater.

This study aims to introduce and discuss the recent research, development and application of wave energy marine buoys. The topic becomes increasingly appealing after the observation that wave energy technologies have been evolving in the recent decades, yet have not reached convergence. The power supply is usually the bottleneck for marine distributed systems such as buoys. Wave energy technologies are especially useful in this sense, as they can capture and convert the promising “native” renewable energy in the ocean (i.e., wave energy) into electricity. The paper enumerates the recent developments in wave energy capture (e.g., oscillating bodies) and power take-off (e.g., nanogenerators). The study also introduces the typical marine buoys and discusses the applicability of wave energy technologies on them. It is concluded that the wave energy technologies could be implemented as a critical addition to the comprehensive power solution of marine distributed systems. Wave energy buoys are likely to differentiate into “wave energy converter buoys” and “wave-energy-powered buoys”, which is indicated by the ratio of the generated power to the load power.

The rational assessment of regional energy distribution provides a scientific basis for the selection and siting of power generation units. This study, which focused on the Bohai Sea, set 31 research coordinate points in the Bohai sea for assessing the potential/trends of wave energy flux (WEF). We applied a point-to-point time series prediction method which modelled the different geographical coordinate points separately. Subsequently, we evaluated the performance of three traditional machine learning methods and three automated machine learning methods. To estimate WEF, the best model was applied to each research coordinate points, respectively. Then, the WEF was calculated and predicted based on the data of MWP, SWH, and water depth. The results indicate that, for all coordinates in the Bohai Sea, the H2O-AutoML algorithm is superior to the other five algorithms. Gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and stacked ensemble models yielded the best performance out of the H2O algorithms. The significant wave height (SWH), the mean wave period (MWP), and the WEF in the Bohai Sea tended to be concentrated in the center of the sea and dispersed in the nearshore areas. In the year 2000, 2010, 2020, and 2030, the maximum annual average WEF at each research coordinate in the Bohai Sea is around 1.5 kW/m, with a higher flux in autumn and winter. In summary, the results provide ocean parameter characterization for the design and deployment of wave energy harvesting devices. Moreover, the automated machine learning introduced herein has potential for use in more applications in ocean engineering.