A wind vector detecting system based on triboelectric and photoelectric sensors for simultaneously monitoring wind speed and direction

Xu, Qiu; Lu, Yu-Ting; Zhao, Shiyu; Hu, Ning; Jiang, Yawei; Li, Hang; Wang, Yue; Gao, Haiqi; Li, Yang; Yuan, Mengwei; Chu, Liang; Li, Jia-hui; Xie, Yannan

doi:10.1016/j.nanoen.2021.106382

Cited by 57 publications

(19 citation statements)

References 44 publications

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“…[7][8][9][10][11][12][13][14][15][16] TENG can convert mechanical motion into electrical signals, allowing it to function as a self-powered sensor. [17][18][19][20] However, the data generated by TENGs present challenges for analysis, including large volume, high noise and poor continuity. Attempts to process TENG data using traditional analysis methods have met with long operation cycles and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator‐Based Self‐Powered Sensor with Deep Learning Technique

Yao

Wang

et al. 2022

Adv Funct Materials

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Urban sound management is required in a variety of fields such as transportation, security, water conservancy and construction, among others. Given the diverse array of available noise sensors and the widespread opportunity to connect these sensors via mobile broadband Internet access, many researchers are eager to apply sound-sensor networks for urban sound management. Existing sensing networks typically consist of expensive information-sensing devices, the cost and maintenance of which limit their large-scale, ubiquitous deployment, thus narrowing their functional measurement range. Herein, an innovative, low-cost, sound-driven triboelectric nanogenerator (SDTENG)-based self-powered sensor is proposed, from which the SDTENG is primarily comprised of fluorinated ethylene propylene membranes, conductive fabrics, acrylic shells, and Kapton spacers. The SDTENG-based sensor has been integrated with a deep learning technique in the present study to construct an intelligent sound monitoring and identification system, which is capable of recognizing a suite of common road and traffic sounds with high classification accuracies of 99% in most cases. The novel SDTENG-based self-powered sensor combined with deep learning technique demonstrates a tremendous application potential in urban sound management, which will show the excellent application prospects in the field of ubiquitous sensor networks.

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Section: Introductionmentioning

confidence: 99%

Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator‐Based Self‐Powered Sensor with Deep Learning Technique

Yao

Wang

et al. 2022

Adv Funct Materials

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“…In 2012, Wang’s group first proposed the triboelectric nanogenerator (TENG), which attracted the attention of researchers worldwide because it can convert mechanical energy from nature into output electrical energy. − TENGs have characteristics that include small size, , low cost, , and ease of manufacturing, , and they are particularly useful in harvesting of low-frequency mechanical energy with high efficiency. − Therefore, TENGs are widely used in fields such as micronano energy harvesting, − self-driven sensing, − and high voltage power supplies. − In particular, TENGs offer numerous advantages for applications in the harvesting of ocean energy, which is known as blue energy. − Additionally, if multiple TENG units are integrated into a network structure to enable large-scale wave energy harvesting in the ocean, they can provide a technological approach to harness a wide range of blue energy sources. − Since the TENG was first proposed, researchers in various countries have designed multiple types of TENG for wave energy harvesting. − However, due to the random nature of the direction of waves, there is a certain bottleneck in the multidirectional harvesting technology of ocean energy. Most of the harvesting schemes use the ball-sleeve or multi-ball structure. , Although multidirectional harvesting can be realized, the real-time contact area of the generation unit is small.…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Structured Triboelectric Nanogenerator for Harvesting Multidirectional Ocean Wave Energy

Gao

Yang

et al. 2022

ACS Nano

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Wave motion in the ocean can generate plentiful energy, but it is difficult to harvest wave energy for practical use because of the low frequency and random directional characteristics of wave motion. In this paper, a gyroscope-structured triboelectric nanogenerator (GS-TENG) is proposed for harvesting multidirectional ocean wave energy. Its inner and outer generation units can operate independently in different directions, and they all adopt the friction mode of surface contact. While realizing noninterference multidirectional energy harvesting, the power generation area is increased. In the experiments, under acceleration of 6 m/s2 with variations in excitation angle, the GS-TENG can output direct currents of 0.8–3.2 μA, and the open-circuit voltages of the inner and outer generation units can reach 730 and 160 V, respectively. When the devices are networked and placed in the water, the electrical energy generated by the GS-TENGs can enable commercial thermometers to operate normally. The attenuation of direct-current output by the GS-TENG in the experiment of 30 days in water is about 8%, which verifies the good durability of the device in the water environment. Therefore, the GS-TENG has excellent application prospects in the wave energy harvesting field.

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“…These devices’ numbers can reach billions in the Internet of Things (IoT) era [ 6 ]. In addition to energy harvesting functionality, the TENG can also be used as a self-powered sensor, which can be used for sport [ 7 ], pulmonary [ 8 ], configurational [ 7 ], wind [ 9 ], and motion [ 10 ] sensing applications.…”

Section: Introductionmentioning

confidence: 99%

An Automated Power Evaluation Workbench for Triboelectric Nanogenerators

Yuan

Zhang³

et al. 2022

Micromachines

Self Cite

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Triboelectric nanogenerators (TENGs) have high potential in self-powered sensing and energy harvesting applications. In general, TENGs’ internal source resistance is high, and their output power varies under different load resistance values. Therefore, a resistance box is required to evaluate their energy harvesting performance and obtain the power curve under different load values. The load tuning process is usually performed by hand. This repetitive process is time-consuming and error-prone. Consequently, an Automated Power Evaluation Workbench (APEW) is developed, making the resistance switching and power measuring process program-controlled. The resistance value is resolved using the Octal decomposition principle. In addition, a resistance synthesis algorithm is proposed to alter the resistance value with a minimum step of 1 Ohm. The target resistance value is physically synthesized by relay switching, while digital lines control the relays. The proposed APEW is then evaluated experimentally, and the obtained results are compared with those of the traditional manual switching approach. It is deduced that the two power curves are almost identical. Therefore, it is believed that the proposed APEW will play a crucial role in TENG’s further development.

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A wind vector detecting system based on triboelectric and photoelectric sensors for simultaneously monitoring wind speed and direction

Cited by 57 publications

References 44 publications

Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator‐Based Self‐Powered Sensor with Deep Learning Technique

Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator‐Based Self‐Powered Sensor with Deep Learning Technique

Gyroscope-Structured Triboelectric Nanogenerator for Harvesting Multidirectional Ocean Wave Energy

An Automated Power Evaluation Workbench for Triboelectric Nanogenerators

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