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With the advent of global warming and energy crisis, the search for renewable energy to reduce carbon emissions has become one of the most urgent challenges. Ithas become a research hotspot to collect or harvest various mechanical energy in nature and convert it into electric energy. Vibration is a common form of mechanical movement in our daily life. It is visible both on most working machines and in nature and is a type of potential energy. There are several methods that can convert such mechanical energy into electric energy. Triboelectric nanogenerator (TENG) based on the principle of contact electrification and electrostatic induction which first appeared in 2012 by Zhonglin Wang provides a feasible method of efficiently collecting the vibrational energy with different vibrating frequencies. In this paper, a contact-separation mode of TENG is designed and implemented. The voltage- quantity of charge- distance(V-Q-x)relation of TENG is calculated. During the experiment, the factors such as load resistance, vibration frequency, etc. which affect the output performance, are considered and analyzed. An electrically driven crank-connecting rod mechanism is employed to provide the vibration source with adjustable frequency in a range of 1-6 Hz. The result shows that the amount of charge transfer in each working cycle remains almost unchanged, while the voltage and current increase with frequency increasing. When the frequency is 5 Hz, the best power matching resistance of the TENG is about 33 MΩ and the maximum output power reaches 0.5 mW. For a further study, a COMSOL software is used to simulate the distribution rule and variation rule of the electric potential in the contact-separation process, then the theoretical charge density and the experimental charge density on the polymer surface are compared and analyzed in order to provide theoretical and practical support for the design of TENG with collected vibration energy and self-powered vibration sensor. The result shows that the electric potential is proportional to the distance between two friction layers. While as the distance between two friction layers increases, the electric potential and the charge density both show a tendency to concentrate in the middle of the friction layer. The huge difference between experimental result and the simulation predicts thatmuch work should be done continually to improve the output of the TENG. Finally, the obtained results conduce to understanding the contact electrification and electrostatic induction mechanism and also provide a new method of harvesting the vibration energy.
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Keywords:
- triboelectric nanogenerator /
- output performance /
- vibration energy harvesting /
- contact separation /
- metal-polymer
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图 1 垂直接触摩擦纳米发电机3D示意图
Figure 1. 3D schematic of vertical contact-separation TENG
图 2 摩擦纳米发电机输出性能测试装置示意图
Figure 2. Schematic diagram of the output performance test system
图 3 摩擦纳米发电机工作原理
Figure 3. Working principle of friction generator
图 4 TENG三维模型示意图
Figure 4. Schematic diagram of the TENGs 3D model
图 5 不同分离距离的电势分布图
Figure 5. The potential distribution picture with different distance
图 6 不同距离的电能密度分布图
Figure 6. The energy density distribution picture with different distance
图 7 垂直接触TENG的V-Q-x模型
Figure 7. V-Q-x model of vertical contact TENGs
图 8 振动频率为5 Hz时, TENG的输出特性 (a)开路电压; (b)短路电流; (c)一个周期中电荷转移量
Figure 8. Out performance of the TENG when the vibration frequency is 5 Hz: (a) The open circuit voltage; (b) the short circuit current of the TEGs; (c) the amount of the charge transferred in one cycle
图 9 振动频率为5 Hz时TENG的输出性能随外负载的变化曲线 (a)摩擦纳米发电机的输出电压; (b)输出功率、电流
Figure 9. The output performance curves with external load at a frequency of 5 Hz: (a) The output voltage; (b) the output power and current of TENG
图 10 不同测试频率下的输出性能 (a)输出电压; (b)输出电流; (c)单个周期内电荷转移量
Figure 10. The output performance of the TENG under different testing frequency: (a) Output voltage; (b) output current; (c) the amount of transferred charge in a single cycle at different test frequencies
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[1] Haque R I, Farine P A, Briand D 2018 Sens. Actuators 271 88
Google Scholar
[2] Lin Z M, Yang J, Li X S, Wu Y F, Wei W, Liu J, Chen J, Yang J 2018 Adv. Funct. Mater. 28 4280
[3] Chen X, Song Y, Chen H, Zhang J, Zhang H 2017 J. Mater. Chem. A 5 508
[4] Qian J, Kim D S, Lee D W 2018 Nano Energy 49 126
Google Scholar
[5] Zi Y, Guo H, Wen Z, Yeh M H, Hu C, Wang Z L 2016 ACS Nano 10 4797
Google Scholar
[6] Jiang T, Tang W, Chen X, Han C B, Lin L, Zi Y, Wang Z L 2016 Adv. Mater. Technol. 1 1600017
Google Scholar
[7] Tang W, Jiang T, Fan F R, Yu A F, Zhang C, Cao X, Wang Z L 2015 Adv. Funct. Mater. 25 3718
Google Scholar
[8] Feng Y, Zheng Y, Ma S, Wang D, Zhou F, Liu W 2016 Nano Energy 19 48
Google Scholar
[9] 王中林 2013 中国科学: 化学 43 759
Wang Z L 2013 Sci. Sin. Chim. 43 759
[10] Xu L, Jiang T, Lin P, Shao J, Wang Z L 2018 ACS Nano 12 1849
Google Scholar
[11] Zhao X J, Kuang S Y, Wang Z L, Zhu G 2018 ACS Nano 12 4280
Google Scholar
[12] Pan L, Wang J, Wang P, Gao R, Wang Y C, Zhang X 2018 Nano Res. 1−12
[13] Dudem B, Huynh N D, Kim W, Kim D H, Hwang H J, Choi D, Yu J S 2017 Nano Energy 42 269
Google Scholar
[14] Cheng G G, Jiang S Y, Li K, Zhang Z Q, Wang Y, Yuan N Y, Ding J N, Zhang W 2017 Appl. Surf. Sci. 412 350
Google Scholar
[15] Wang S, Wang X, Wang Z L, Yang Y 2016 ACS Nano 10 5696
Google Scholar
[16] Wang M, Zhang J H, Tang Y, Li J, Zhang B S, Liang E J, Mao Y C, Wang X D 2018 ACS Nano 12 6156
Google Scholar
[17] Wu C, Kima T W, Sung S, Park J H, Li F 2018 Nano Energy 44 279
Google Scholar
[18] Mao Y C, Geng D L, Liang E J, Wang X D 2015 Nano Energy 15 227
Google Scholar
[19] 程广贵, 张伟, 方俊, 蒋诗宇, 丁建宁, Noshir S P, 张忠强, 郭立强, 王莹 2016 物理学报 65 060201
Google Scholar
Cheng G G, Zhang W, Fang J, Jiang S Y, Ding J N, Noshir S P, Zhang Z Q, Guo L Q, Wang Y 2016 Acta Phys. Sin. 65 060201
Google Scholar
[20] Wu Y, Hu Y, Huang Z, Hu Y, Peng Z, Li X, Wang F 2018 Sens. Actuators 271 364
Google Scholar
[21] Chen S N, Chen C H, Lin Z H, Tsao Y H, Liu C P 2018 Nano Energy 45 311
Google Scholar
[22] 温涛, 何剑, 张增星, 田竹梅, 穆继亮, 韩建强, 丑修建, 薛晨阳 2017 物理学报 66 228401
Google Scholar
Wen T, He J, Zhang Z X, Tian Z M, Mu J L, Han J Q, Chou X J, Xue C Y 2017 Acta Phys. Sin. 66 228401
Google Scholar
[23] Ren X, Fan H, Wang C, Ma J, Lei S, Zhao Y 2017 Nano Energy 35 233
Google Scholar
[24] Shao H Y, Wen Z, Cheng P, Sun N, Shen Q Q, Zhou C J, Peng M F, Yang Y Q, Xie X K, Su X H 2017 Nano Energy 39 608
Google Scholar
[25] Yang J, Yang F, Zhao L, Shang W, Qin H, Wang S, Jiang X, Cheng G, Du Z 2018 Nano Energy 46 220
Google Scholar
[26] Cheng G, Zheng H, Yang F, Zhao L, Zheng M, Yang J, Qin H, Du Z, Wang Z L 2018 Nano Energy 44 208
Google Scholar
[27] Wu Y, Jing Q S, Chen J, Bai P, Bai J J, Zhu G, Su Y J, Wang Z L 2015 Adv. Funct. Mater. 25 2166
Google Scholar
[28] Wang J, Ding W, Pan L, Wu C, Yu H, Yang L, Liao R, Wang Z L 2018 ACS Nano 12 3954
Google Scholar
[29] Wu Z, Ding W, Dai Y, Dong K, Wu C, Zhang L, Lin Z, Cheng J, Wang Z L 2018 ACS Nano 12 5726
Google Scholar
[30] Wang P H, Liu R Y, Ding W B, Zhang P, Pan L, Dai G Z, Zou H Y, Dong K, Xu C, Wang Z L 2018 Adv. Funct. Mater. 28 1705808
Google Scholar
[31] Ren Z W, Nie J H, Shao J J, Lai Q S, Wang L F, Chen J, Chen X Y, Wang Z L 2018 Adv. Funct. Mater. 28 1802989
Google Scholar
[32] Zheng Q, Shi B J, Fan F R, Wang X X, Yan L, Yuan W W, Wang S H, Liu H, Li Z, Wang Z L 2014 Adv. Mater. 26 5851
Google Scholar
[33] Zhong J W, Zhang Y, Zhong Q, Hu Q Y, Hu B, Wang Z L, Zhou J 2014 ACS Nano 8 6273
Google Scholar
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