Issue

Vol. 15 (2023)

Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things

https://doi.org/10.1007/s40820-022-00981-8
Xiaole Cao
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, People’s Republic of China
Yao Xiong
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, People’s Republic of China
Jia Sun
School of Physics and Electronics, Central South University, Changsha 410083, People’s Republic of China
Xiaoyin Xie
School of Chemistry and Chemical Engineering, Hubei Polytechnic University, Huangshi 435003, People’s Republic of China
Qijun Sun
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, People’s Republic of China
Zhong Lin Wang
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, People’s Republic of China

Corresponding Author: Zhong Lin Wang

zhong.wang@mse.gatech.edu

Nano-Micro Letters, Vol. 15 (2023), Article Number: 14

Abstract

In the era of 5G and the Internet of things (IoTs), various human–computer interaction systems based on the integration of triboelectric nanogenerators (TENGs) and IoTs technologies demonstrate the feasibility of sustainable and self-powered functional systems. The rapid development of intelligent applications of IoTs based on TENGs mainly relies on supplying the harvested mechanical energy from surroundings and implementing active sensing, which have greatly changed the way of human production and daily life. This review mainly introduced the TENG applications in multidiscipline scenarios of IoTs, including smart agriculture, smart industry, smart city, emergency monitoring, and machine learning-assisted artificial intelligence applications. The challenges and future research directions of TENG toward IoTs have also been proposed. The extensive developments and applications of TENG will push forward the IoTs into an energy autonomy fashion.


Highlights:


1 Multidiscipline application of triboelectric nanogenerators (TENGs) for intelligent Internet of Things (IoTs) are summarized from the aspects of agriculture, industry, city, emergency monitoring, and artificial intelligence.
2 Perspectives on the challenges and future research directions of TENGs in IoTs have been proposed.

Keywords

Triboelectric nanogenerator Self-powered sensor Internet of things Artificial intelligence Machine learning
Cao, Xiaole, Yao Xiong, Jia Sun, Xiaoyin Xie, Qijun Sun, and Zhong Lin Wang. 2022. “Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things”. Nano-Micro Letters 15 (December):14. https://doi.org/10.1007/s40820-022-00981-8.
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References
  1. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surveys Tutorials 17, 2347–2376 (2015). https://doi.org/10.1109/comst.2015.2444095
  2. J. Han, N. Xu, J. Yu, Y. Wang, Y. Xiong et al., Energy autonomous paper module and functional circuits. Energy Environ. Sci. (2022). https://doi.org/10.1039/D2EE02557D
  3. H. Zhu, X. Wang, J. Liang, H. Lv, H. Tong et al., Versatile electronic skins for motion detection of joints enabled by aligned few-walled carbon nanotubes in flexible polymer composites. Adv. Funct. Mater. 27(21), 1606604 (2017). https://doi.org/10.1002/adfm.201606604
  4. L. Meng, L. Li, Recent research progress on operational stability of metal oxide/sulfide photoanodes in photoelectrochemical cells. Nano Res. Energy 1, e9120020 (2022). https://doi.org/10.26599/NRE.2022.9120020
  5. J. Yu, Y. Wang, S. Qin, G. Gao, C. Xu et al., Bioinspired interactive neuromorphic devices. Mater. Today (2022). https://doi.org/10.1016/j.mattod.2022.09.012
  6. F.R. Fan, Z.Q. Tian, Z.L. Wang, Flexible triboelectric generator. Nano Energy 1, 328–334 (2012). https://doi.org/10.1016/j.nanoen.2012.01.004
  7. H. Xue, H. Gong, Y. Yamauchi, T. Sasaki, R. Ma, Photo-enhanced rechargeable high-energy-density metal batteries for solar energy conversion and storage. Nano Res. Energy 1, e9120007 (2022). https://doi.org/10.26599/NRE.2022.9120007
  8. C. Ye, S. Dong, J. Ren, S. Ling, Ultrastable and high-performance silk energy harvesting textiles. Nano-Micro Lett. 12, 12 (2020). https://doi.org/10.1007/s40820-019-0348-z
  9. J. Bae, J. Lee, S. Kim, J. Ha, B.S. Lee et al., Flutter-driven triboelectrification for harvesting wind energy. Nat. Commun. 5, 4929 (2014). https://doi.org/10.1038/ncomms5929
  10. Y. Yang, G. Zhu, H. Zhang, J. Chen, X. Zhong et al., Triboelectric nanogenerator for harvesting wind energy and as self-powered wind vector sensor system. ACS Nano 7(10), 9461–9468 (2013). https://doi.org/10.1021/nn4043157
  11. Y. Xie, S. Wang, L. Lin, Q. Jing, Z.H. Lin et al., Rotary triboelectric nanogenerator based on a hybridized mechanism for harvesting wind energy. ACS Nano 7(8), 7119–7125 (2013). https://doi.org/10.1021/nn402477h
  12. Q. Jiang, B. Chen, K. Zhang, Y. Yang, Ag nanop-based triboelectric nanogenerator to scavenge wind energy for a self-charging power unit. ACS Appl. Mater. Interfaces 9(50), 43716–43723 (2017). https://doi.org/10.1021/acsami.7b14618
  13. B. Chen, Y. Yang, Z.L. Wang, Scavenging wind energy by triboelectric nanogenerators. Adv. Energy Mater. 8(10), 1702649 (2018). https://doi.org/10.1002/aenm.201702649
  14. W. Xie, L. Gao, L. Wu, X. Chen, F. Wang et al., A nonresonant hybridized electromagnetic-triboelectric nanogenerator for irregular and ultralow frequency blue energy harvesting. Research 2021, 5963293 (2021). https://doi.org/10.34133/2021/5963293
  15. S. Fu, W. He, H. Wu, C. Shan, Y. Du et al., High output performance and ultra-durable DC output for triboelectric nanogenerator inspired by primary cell. Nano-Micro Lett. 14, 155 (2022). https://doi.org/10.1007/s40820-022-00898-2
  16. Z.L. Wang, T. Jiang, L. Xu, Toward the blue energy dream by triboelectric nanogenerator networks. Nano Energy 39, 9–23 (2017). https://doi.org/10.1016/j.nanoen.2017.06.035
  17. L. Zhang, J. Liang, L. Yue, K. Dong, J. Li et al., Benzoate anions-intercalated NiFe-layered double hydroxide nanosheet array with enhanced stability for electrochemical seawater oxidation. Nano Res. Energy 1(3), 9120028 (2022). https://doi.org/10.26599/NRE.2022.9120028
  18. L. Liu, Q. Shi, C. Lee, A novel hybridized blue energy harvester aiming at all-weather IoT applications. Nano Energy 76, 105052 (2020). https://doi.org/10.1016/j.nanoen.2020.105052
  19. L. Liu, Q. Shi, J.S. Ho, C. Lee, Study of thin film blue energy harvester based on triboelectric nanogenerator and seashore IoT applications. Nano Energy 66, 104167 (2019). https://doi.org/10.1016/j.nanoen.2019.104167
  20. D. Tan, Q. Zeng, X. Wang, S. Yuan, Y. Luo et al., Anti-overturning fully symmetrical triboelectric nanogenerator based on an elliptic cylindrical structure for all-weather blue energy harvesting. Nano-Micro Lett. 14, 124 (2022). https://doi.org/10.1007/s40820-022-00866-w
  21. Y. Yang, H. Zhang, Z.H. Lin, Y.S. Zhou, Q. Jing et al., Human skin based triboelectric nanogenerators for harvesting biomechanical energy and as self-powered active tactile sensor system. ACS Nano 7(10), 9213–9222 (2013). https://doi.org/10.1021/nn403838y
  22. T. He, X. Guo, C. Lee, Flourishing energy harvesters for future body sensor network: from single to multiple energy sources. iScience 24, 101934 (2021). https://doi.org/10.1016/j.isci.2020.101934
  23. S. Niu, X. Wang, F. Yi, Y.S. Zhou, Z.L. Wang, A universal self-charging system driven by random biomechanical energy for sustainable operation of mobile electronics. Nat. Commun. 6, 8975 (2015). https://doi.org/10.1038/ncomms9975
  24. K. Zhang, X. Liang, L. Wang, K. Sun, Y. Wang et al., Status and perspectives of key materials for PEM electrolyzer. Nano Res. Energy 1(3), 9120032 (2022). https://doi.org/10.26599/NRE.2022.9120032
  25. X. Cao, Y. Xiong, J. Sun, X. Zhu, Q. Sun et al., Piezoelectric nanogenerators derived self-powered sensors for multifunctional applications and artificial intelligence. Adv. Funct. Mater. 31(33), 2102983 (2021). https://doi.org/10.1002/adfm.202102983
  26. H.F. Nweke, Y.W. Teh, M.A. Al-garadi, U.R. Alo, Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges. Expert Syst. Appl. 105, 233–261 (2018). https://doi.org/10.1016/j.eswa.2018.03.056
  27. Y. Zhou, M. Shen, X. Cui, Y. Shao, L. Li et al., Triboelectric nanogenerator based self-powered sensor for artificial intelligence. Nano Energy 84, 105887 (2021). https://doi.org/10.1016/j.nanoen.2021.105887
  28. S. Berman, H. Stern, Sensors for gesture recognition systems. IEEE Transac. Syst. Man Cybernet. Part C 42, 277–290 (2012). https://doi.org/10.1109/tsmcc.2011.2161077
  29. X. Zhao, Z. Zhang, L. Xu, F. Gao, B. Zhao et al., Fingerprint-inspired electronic skin based on triboelectric nanogenerator for fine texture recognition. Nano Energy 85, 106001 (2021). https://doi.org/10.1016/j.nanoen.2021.106001
  30. J. Hughes, A. Spielberg, M. Chounlakone, G. Chang, W. Matusik et al., A simple, inexpensive, wearable glove with hybrid resistive-pressure sensors for computational sensing, proprioception, and task identification. Adv. Intell. Syst. 2, 2000002 (2020). https://doi.org/10.1002/aisy.202000002
  31. G. Li, S. Liu, L. Wang, R. Zhu, Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition. Sci. Robot. 5, abc8134 (2020). https://doi.org/10.1126/scirobotics.abc8134
  32. Z. Zhang, T. He, M. Zhu, Z. Sun, Q. Shi et al., Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications. NPG Flex. Electron. 4, 29 (2020). https://doi.org/10.1038/s41528-020-00092-7
  33. T. Jin, Z. Sun, L. Li, Q. Zhang, M. Zhu et al., Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications. Nat. Commun. 11, 5381 (2020). https://doi.org/10.1038/s41467-020-19059-3
  34. J. Yu, G. Gao, J. Huang, X. Yang, J. Han et al., Contact-electrification-activated artificial afferents at femtojoule energy. Nat. Commun. 12, 1581 (2021). https://doi.org/10.1038/s41467-021-21890-1
  35. M.H. Syu, Y.J. Guan, W.C. Lo, Y.K. Fuh, Biomimetic and porous nanofiber-based hybrid sensor for multifunctional pressure sensing and human gesture identification via deep learning method. Nano Energy 76, 105029 (2020). https://doi.org/10.1016/j.nanoen.2020.105029
  36. J. Yu, X. Yang, G. Gao, Y. Xiong, Y. Wang et al., Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure. Sci. Adv. 7, eabd9117 (2021). https://doi.org/10.1126/sciadv.abd9117
  37. W. Zhang, L. Deng, L. Yang, P. Yang, D. Diao et al., Multilanguage-handwriting self-powered recognition based on triboelectric nanogenerator enabled machine learning. Nano Energy 77, 105174 (2020). https://doi.org/10.1016/j.nanoen.2020.105174
  38. L. Liu, X. Guo, C. Lee, Promoting smart cities into the 5G era with multi-field internet of things (IoT). applications powered with advanced mechanical energy harvesters. Nano Energy 88, 106304 (2021). https://doi.org/10.1016/j.nanoen.2021.106304
  39. Z. Sun, M. Zhu, C. Lee, Progress in the triboelectric human-machine interfaces (HMIs)-moving from smart gloves to AI/haptic enabled HMI in the 5G/IoT era. Nanoenergy Adv. 1, 81–121 (2021). https://doi.org/10.3390/nanoenergyadv1010005
  40. D. Zhang, D. Wang, Z. Xu, X. Zhang, Y. Yang et al., Diversiform sensors and sensing systems driven by triboelectric and piezoelectric nanogenerators. Coord. Chem. Rev. 427, 213597 (2021). https://doi.org/10.1016/j.ccr.2020.213597
  41. Z.L. Wang, On Maxwell’s displacement current for energy and sensors: the origin of nanogenerators. Mater. Today 20, 74–82 (2017). https://doi.org/10.1016/j.mattod.2016.12.001
  42. Z.L. Wang, On the expanded Maxwell’s equations for moving charged media system - general theory, mathematical solutions and applications in TENG. Mater. Today 52, 348–363 (2022). https://doi.org/10.1016/j.mattod.2021.10.027
  43. Z.L. Wang, On the first principle theory of nanogenerators from Maxwell’s equations. Nano Energy 68, 104272 (2020). https://doi.org/10.1016/j.nanoen.2019.104272
  44. J. Zhao, D. Wang, F. Zhang, J. Pan, P. Claesson et al., Self-powered, long-durable, and highly selective oil-solid triboelectric nanogenerator for energy harvesting and intelligent monitoring. Nano-Micro Lett. 14, 160 (2022). https://doi.org/10.1007/s40820-022-00903-8
  45. Y. Yun, S. Jang, S. Cho, S.H. Lee, H.J. Hwang et al., Exo-shoe triboelectric nanogenerator: toward high-performance wearable biomechanical energy harvester. Nano Energy 80, 105525 (2021). https://doi.org/10.1016/j.nanoen.2020.105525
  46. S.A. Graham, S.C. Chandrarathna, H. Patnam, P. Manchi, J.W. Lee et al., Harsh environment-tolerant and robust triboelectric nanogenerators for mechanical-energy harvesting, sensing, and energy storage in a smart home. Nano Energy 80, 105547 (2021). https://doi.org/10.1016/j.nanoen.2020.105547
  47. C. Qiu, F. Wu, C. Lee, M.R. Yuce, Self-powered control interface based on Gray code with hybrid triboelectric and photovoltaics energy harvesting for IoT smart home and access control applications. Nano Energy 70, 104456 (2020). https://doi.org/10.1016/j.nanoen.2020.104456
  48. F. Wen, Z. Sun, T. He, Q. Shi, M. Zhu et al., Machine learning glove using self-powered conductive superhydrophobic triboelectric textile for gesture recognition in VR/AR applications. Adv. Sci. 7, 2000261 (2020). https://doi.org/10.1002/advs.202000261
  49. J. Jiang, Y. Zhang, Q. Shen, Q. Zhu, X. Ge et al., A self-powered hydrogen leakage sensor based on impedance adjustable windmill-like triboelectric nanogenerator. Nano Energy 89, 106453 (2021). https://doi.org/10.1016/j.nanoen.2021.106453
  50. H. Yang, Y. Pang, T. Bu, W. Liu, J. Luo et al., Triboelectric micromotors actuated by ultralow frequency mechanical stimuli. Nat. Commun. 10, 2309 (2019). https://doi.org/10.1038/s41467-019-10298-7
  51. J. Yu, S. Qin, H. Zhang, Y. Wei, X. Zhu et al., Fiber-shaped triboiontronic electrochemical transistor. Research 2021, 9840918 (2021). https://doi.org/10.34133/2021/9840918
  52. Y. Xiong, J. Han, Y. Wang, Z.L. Wang, Q. Sun, Emerging iontronic sensing: materials, mechanisms, and applications. Research 2022, 9867378 (2022). https://doi.org/10.34133/2022/9867378
  53. L. Luo, J. Han, Y. Xiong, Z. Huo, X. Dan et al., Kirigami interactive triboelectric mechanologic. Nano Energy 99, 107345 (2022). https://doi.org/10.1016/j.nanoen.2022.107345
  54. C. Wu, A.C. Wang, W. Ding, H. Guo, Z.L. Wang, Triboelectric nanogenerator: a foundation of the energy for the new era. Adv. Energy Mater. 9, 1802906 (2019). https://doi.org/10.1002/aenm.201802906
  55. Y. Zi, C. Wu, W. Ding, Z.L. Wang, Maximized effective energy output of contact-separation-triggered triboelectric nanogenerators as limited by air breakdown. Adv. Funct. Mater. 27(24), 1700049 (2017). https://doi.org/10.1002/adfm.201700049
  56. Q. Tang, M.H. Yeh, G. Liu, S. Li, J. Chen et al., Whirligig-inspired triboelectric nanogenerator with ultrahigh specific output as reliable portable instant power supply for personal health monitoring devices. Nano Energy 47, 74–80 (2018). https://doi.org/10.1016/j.nanoen.2018.02.039
  57. S. Wang, Y. Xie, S. Niu, L. Lin, C. Liu et al., Maximum surface charge density for triboelectric nanogenerators achieved by ionized-air injection: methodology and theoretical understanding. Adv. Mater. 26(39), 6720–6728 (2014). https://doi.org/10.1002/adma.201402491
  58. L. Cheng, Q. Xu, Y. Zheng, X. Jia, Y. Qin, A self-improving triboelectric nanogenerator with improved charge density and increased charge accumulation speed. Nat. Commun. 9, 3773 (2018). https://doi.org/10.1038/s41467-018-06045-z
  59. Y. Liu, W. Liu, Z. Wang, W. He, Q. Tang et al., Quantifying contact status and the air-breakdown model of charge-excitation triboelectric nanogenerators to maximize charge density. Nat. Commun. 11, 1599 (2020). https://doi.org/10.1038/s41467-020-15368-9
  60. W. He, W. Liu, S. Fu, H. Wu, C. Shan et al., Ultrahigh performance triboelectric nanogenerator enabled by charge transmission in interfacial lubrication and potential decentralization design. Research 2022, 9812865 (2022). https://doi.org/10.34133/2022/9812865
  61. K. Tao, Z. Chen, H. Yi, R. Zhang, Q. Shen et al., Hierarchical honeycomb-structured electret/triboelectric nanogenerator for biomechanical and morphing wing energy harvesting. Nano-Micro Lett. 13, 123 (2021). https://doi.org/10.1007/s40820-021-00644-0
  62. X. Li, Y. Cao, X. Yu, Y. Xu, Y. Yang et al., Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture. Appl. Energy 306, 117977 (2022). https://doi.org/10.1016/j.apenergy.2021.117977
  63. A. Adesipo, O. Fadeyi, K. Kuca, O. Krejcar, P. Maresova et al., Smart and climate-smart agricultural trends as core aspects of smart village functions. Sensors 20(21), 5977 (2020). https://doi.org/10.3390/s20215977
  64. M. Gupta, M. Abdelsalam, S. Khorsandroo, S. Mittal, Security and privacy in smart farming: challenges and opportunities. IEEE Acc. 8, 34564–34584 (2020). https://doi.org/10.1109/access.2020.2975142
  65. W. He, W. Liu, J. Chen, Z. Wang, Y. Liu et al., Boosting output performance of sliding mode triboelectric nanogenerator by charge space-accumulation effect. Nat. Commun. 11, 4277 (2020). https://doi.org/10.1038/s41467-020-18086-4
  66. P. Chen, J. An, R. Cheng, S. Shu, A. Berbille et al., Rationally segmented triboelectric nanogenerator with a constant direct-current output and low crest factor. Energy Environ. Sci. 14, 4523–4532 (2021). https://doi.org/10.1039/d1ee01382c
  67. P. Chen, J. An, S. Shu, R. Cheng, J. Nie et al., Super-durable, low-wear, and high-performance fur-brush triboelectric nanogenerator for wind and water energy harvesting for smart agriculture. Adv. Energy Mater. 11(9), 2003066 (2021). https://doi.org/10.1002/aenm.202003066
  68. J. Han, Y. Feng, P. Chen, X. Liang, H. Pang et al., Wind-driven soft-contact rotary triboelectric nanogenerator based on rabbit fur with high performance and durability for smart farming. Adv. Funct. Mater. 32(2), 2108580 (2021). https://doi.org/10.1002/adfm.202108580
  69. J. Tian, X. Chen, Z.L. Wang, Environmental energy harvesting based on triboelectric nanogenerators. Nanotechnology 31, 242001 (2020). https://doi.org/10.1088/1361-6528/ab793e
  70. Y. Huang, W. Qiu, W. Liu, C. Jin, J. Sun et al., Non-volatile In-Ga-Zn-O transistors for neuromorphic computing. Appl. Phys. A Mater. Sci. Proc. 127, 1–10 (2021). https://doi.org/10.1007/s00339-021-04512-x
  71. X. Fan, J. He, J. Mu, J. Qian, N. Zhang et al., Triboelectric-electromagnetic hybrid nanogenerator driven by wind for self-powered wireless transmission in internet of things and self-powered wind speed sensor. Nano Energy 68, 104319 (2020). https://doi.org/10.1016/j.nanoen.2019.104319
  72. M.T. Rahman, M. Salauddin, P. Maharjan, M.S. Rasel, H. Cho et al., Natural wind-driven ultra-compact and highly efficient hybridized nanogenerator for self-sustained wireless environmental monitoring system. Nano Energy 57, 256–268 (2019). https://doi.org/10.1016/j.nanoen.2018.12.052
  73. R. Cao, T. Zhou, B. Wang, Y. Yin, Z. Yuan et al., Rotating-sleeve triboelectric-electromagnetic hybrid nanogenerator for high efficiency of harvesting mechanical energy. ACS Nano 11(8), 8370–8378 (2017). https://doi.org/10.1021/acsnano.7b03683
  74. Y. Xi, H. Guo, Y. Zi, X. Li, J. Wang et al., Multifunctional TENG for blue energy scavenging and self-powered wind-speed sensor. Adv. Energy Mater. 7(12), 1602397 (2017). https://doi.org/10.1002/aenm.201602397
  75. M.T. Rahman, S.M.S. Rana, P. Maharjan, M. Salauddin, T. Bhatta et al., Ultra-robust and broadband rotary hybridized nanogenerator for self-sustained smart-farming applications. Nano Energy 85, 105974 (2021). https://doi.org/10.1016/j.nanoen.2021.105974
  76. A.P. Krueger, J.C. Beckett, P.C. Andriese, S. Kotaka, Studies on the effects of gaseous ions on plant growth: II. The construction and operation of an air purification unit for use in studies on the biological effects of gaseous ions. J. Gen. Physiol. 45, 897–904 (1962). https://doi.org/10.1085/jgp.45.5.897
  77. E. Costanzo, The influence of an electric field on the growth of soy seedlings. J. Electrostatics 66, 417–420 (2008). https://doi.org/10.1016/j.elstat.2008.04.002
  78. G. Acosta-Santoyo, R.A. Herrada, S.D. Folter, E. Bustos, Stimulation of the germination and growth of different plant species using an electric field treatment with IrO2-Ta2O5|Ti electrodes. J. Chem. Technol. Biotechnol. 93, 1488–1494 (2018). https://doi.org/10.1002/jctb.5517
  79. X. Li, J. Luo, K. Han, X. Shi, Z. Ren et al., Stimulation of ambient energy generated electric field on crop plant growth. Nat. Food 3, 133–142 (2022). https://doi.org/10.1038/s43016-021-00449-9
  80. J. Yun, I. Kim, D. Kim, Hybrid energy harvesting system based on Stirling engine towards next-generation heat recovery system in industrial fields. Nano Energy 90, 106508 (2021). https://doi.org/10.1016/j.nanoen.2021.106508
  81. J. Zhang, Y. Sun, J. Yang, T. Jiang, W. Tang et al., Irregular wind energy harvesting by a turbine vent triboelectric nanogenerator and its application in a self-powered on-site industrial monitoring system. ACS Appl. Mater. Interfaces 13(46), 55136–55144 (2021). https://doi.org/10.1021/acsami.1c16680
  82. L. Ma, R. Wu, A. Patil, J. Yi, D. Liu et al., Acid and alkali-resistant textile triboelectric nanogenerator as a smart protective suit for liquid energy harvesting and self-powered monitoring in high-risk environments. Adv. Funct. Mater. 31(35), 2102963 (2021). https://doi.org/10.1002/adfm.202102963
  83. G.Q. Gu, C.B. Han, Y. Bai, T. Jiang, C. He et al., P transport-based triboelectric nanogenerator for self-powered mass-flow detection and explosion early warning. Adv. Mater. Technol. 3(6), 1800009 (2018). https://doi.org/10.1002/admt.201800009
  84. W. Alquraishi, J. Sun, W. Qiu, W. Liu, Y. Huang et al., Mimicking optoelectronic synaptic functions in solution-processed In-Ga-Zn-O phototransistors. Appl. Phys. A Mater. Sci. Proc. 126, 431 (2020). https://doi.org/10.1007/s00339-020-03614-2
  85. Y. Chen, J. Sun, W. Qiu, X. Wang, W. Liu et al., Deep-ultraviolet SnO2 nanowire phototransistors with an ultrahigh responsivity. Appl. Phys. A Mater. Sci. Proc. 125, 691 (2019). https://doi.org/10.1007/s00339-019-2997-7
  86. J. Chen, Z. Liao, Y. Wu, H. Zhou, W. Xuan et al., Self-powered pumping switched TENG enabled real-time wireless metal tin height and position recognition and counting for production line management. Nano Energy 90, 106544 (2021). https://doi.org/10.1016/j.nanoen.2021.106544
  87. Z. Zhu, H. Xiang, Y. Zeng, J. Zhu, X. Cao et al., Continuously harvesting energy from water and wind by pulsed triboelectric nanogenerator for self-powered seawater electrolysis. Nano Energy 93, 106776 (2022). https://doi.org/10.1016/j.nanoen.2021.106776
  88. Y. Chen, Y.C. Wang, Y. Zhang, H. Zou, Z. Lin et al., Elastic-beam triboelectric nanogenerator for high-performance multifunctional applications: sensitive scale, acceleration/force/vibration sensor, and intelligent keyboard. Adv. Energy Mater. 8(29), 1802159 (2018). https://doi.org/10.1002/aenm.201802159
  89. C. Wu, Q. Zhou, G. Wen, Research on self-powered rotation speed sensor for drill pipe based on triboelectric-electromagnetic hybrid nanogeneratorh. Sens. Actuat. A Phys. 326, 112723 (2021). https://doi.org/10.1016/j.sna.2021.112723
  90. I.W. Tcho, S.B. Jeon, S.J. Park, W.G. Kim, I.K. Jin et al., Disk-based triboelectric nanogenerator operated by rotational force converted from linear force by a gear system. Nano Energy 50, 489–496 (2018). https://doi.org/10.1016/j.nanoen.2018.05.067
  91. W. Kim, H.J. Hwang, D. Bhatia, Y. Lee, J.M. Baik et al., Kinematic design for high performance triboelectric nanogenerators with enhanced working frequency. Nano Energy 21, 19–25 (2016). https://doi.org/10.1016/j.nanoen.2015.12.017
  92. S. Lin, L. Zhu, Y. Qiu, Z. Jiang, Y. Wang et al., A self-powered multi-functional sensor based on triboelectric nanogenerator for monitoring states of rotating motion. Nano Energy 83, 105857 (2021). https://doi.org/10.1016/j.nanoen.2021.105857
  93. Y. Ra, S. Oh, J. Lee, Y. Yun, S. Cho et al., Triboelectric signal generation and its versatile utilization during gear-based ordinary power transmission. Nano Energy 73, 104745 (2020). https://doi.org/10.1016/j.nanoen.2020.104745
  94. Y. Zhang, J. Cao, H. Zhu, Y. Lei, Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion. Energy Convers. Manage. 180, 811–821 (2019). https://doi.org/10.1016/j.enconman.2018.11.037
  95. X.S. Meng, H.Y. Li, G. Zhu, Z.L. Wang, Fully enclosed bearing-structured self-powered rotation sensor based on electrification at rolling interfaces for multi-tasking motion measurement. Nano Energy 12, 606–611 (2015). https://doi.org/10.1016/j.nanoen.2015.01.015
  96. X.H. Li, C.B. Han, T. Jiang, C. Zhang, Z.L. Wang, A ball-bearing structured triboelectric nanogenerator for nondestructive damage and rotating speed measurement. Nanotechnology 27, 085401 (2016). https://doi.org/10.1088/0957-4484/27/8/085401
  97. D. Choi, T. Sung, J.Y. Kwon, A self-powered smart roller-bearing based on a triboelectric nanogenerator for measurement of rotation movement. Adv. Mater. Technol. 3(12), 1800219 (2018). https://doi.org/10.1002/admt.201800219
  98. Z. Xie, J. Dong, Y. Li, L. Gu, B. Song et al., Triboelectric rotational speed sensor integrated into a bearing: a solid step to industrial applicationh. Extreme Mech. Lett. 34, 100595 (2020). https://doi.org/10.1016/j.eml.2019.100595
  99. X. Zhang, Q. Gao, Q. Gao, X. Yu, T. Cheng et al., Triboelectric rotary motion sensor for industrial-grade speed and angle monitoring. Sensors 21, 1713 (2021). https://doi.org/10.3390/s21051713
  100. M. Song, J. Chung, S.H. Chung, K. Cha, D. Heo et al., Semisolid-lubricant-based ball-bearing triboelectric nanogenerator for current amplification, enhanced mechanical lifespan, and thermal stabilization. Nano Energy 93, 106816 (2022). https://doi.org/10.1016/j.nanoen.2021.106816
  101. W. Liu, J. Sun, W. Qiu, Y. Chen, Y. Huang et al., Sub-60 mV per decade switching in ion-gel-gated In-Sn-O transistors with a nano-thick charge trapping layer. Nanoscale 11, 21740–21747 (2019). https://doi.org/10.1039/C9NR06641A
  102. J. Yan, Y. Chen, X. Wang, Y. Fu, J. Wang et al., High-performance solar-blind SnO2 nanowire photodetectors assembled using optical tweezers. Nanoscale 11, 2162–2169 (2019). https://doi.org/10.1039/C8NR07382A
  103. H. Li, H. Liu, H. Li, S. Qi, Y. Liu et al., Effect of cage-pocket wear on the dynamic characteristics of ball bearing. Ind. Lubric. Tribol. 72, 905–912 (2020). https://doi.org/10.1108/ilt-12-2019-0535
  104. J. Wang, Y. Chen, L.A. Kong, Y. Fu, Y. Gao et al., Deep-ultraviolet-triggered neuromorphic functions in In-Zn-O phototransistors. Appl. Phys. Lett. 113, 151101 (2018). https://doi.org/10.1063/1.5039544
  105. Z. Xie, Y. Wang, R. Wu, J. Yin, D. Yu et al., A high-speed and long-life triboelectric sensor with charge supplement for monitoring the speed and skidding of rolling bearing. Nano Energy 92, 106747 (2022). https://doi.org/10.1016/j.nanoen.2021.106747
  106. Q. Han, Z. Jiang, X. Xu, Z. Ding, F. Chu, Self-powered fault diagnosis of rolling bearings based on triboelectric effect. Mechan. Syst. Signal Proc. 166, 108382 (2022). https://doi.org/10.1016/j.ymssp.2021.108382
  107. A.R. Al-Ali, Internet of things role in the renewable energy resources. Energy Proc. 100, 34–38 (2016). https://doi.org/10.1016/j.egypro.2016.10.144
  108. C. Qian, L. Kong, J. Yang, Y. Gao, J. Sun, Multi-gate organic neuron transistors for spatiotemporal information processing. Appl. Phys. Lett. 110, 083302 (2017). https://doi.org/10.1063/1.4977069
  109. P. Panthongsy, D. Isarakorn, P. Janphuang, K. Hamamoto, Fabrication and evaluation of energy harvesting floor using piezoelectric frequency up-converting mechanism. Sens. Actuat. A Phys. 279, 321–330 (2018). https://doi.org/10.1016/j.sna.2018.06.035
  110. S. Hao, J. Jiao, Y. Chen, Z.L. Wang, X. Cao, Natural wood-based triboelectric nanogenerator as self-powered sensing for smart homes and floors. Nano Energy 75, 104957 (2020). https://doi.org/10.1016/j.nanoen.2020.104957
  111. C. He, W. Zhu, B. Chen, L. Xu, T. Jiang et al., Smart floor with integrated triboelectric nanogenerator as energy harvester and motion sensor. ACS Appl. Mater. Interfaces 9(31), 26126–26133 (2017). https://doi.org/10.1021/acsami.7b08526
  112. J. Ma, Y. Jie, J. Bian, T. Li, X. Cao et al., From triboelectric nanogenerator to self-powered smart floor: a minimalist design. Nano Energy 39, 192–199 (2017). https://doi.org/10.1016/j.nanoen.2017.06.025
  113. J. Sun, K. Tu, S. Büchele, S.M. Koch, Y. Ding et al., Functionalized wood with tunable tribopolarity for efficient triboelectric nanogenerators. Matter 4, 3049–3066 (2021). https://doi.org/10.1016/j.matt.2021.07.022
  114. J. Wang, C. Meng, Q. Gu, M.C. Tseng, S.T. Tang et al., Normally transparent tribo-induced smart window. ACS Nano 14(3), 3630–3639 (2020). https://doi.org/10.1021/acsnano.0c00107
  115. J. Wang, C. Meng, C.T. Wang, C.H. Liu, Y.H. Chang et al., A fully self-powered, ultra-stable cholesteric smart window triggered by instantaneous mechanical stimuli. Nano Energy 85, 105976 (2021). https://doi.org/10.1016/j.nanoen.2021.105976
  116. M.H. Yeh, L. Lin, P.K. Yang, Z.L. Wang, Motion-driven electrochromic reactions for self-powered smart window system. ACS Nano 9(5), 4757–4765 (2015). https://doi.org/10.1021/acsnano.5b00706
  117. J. Tan, P. Tian, M. Sun, H. Wang, N. Sun et al., A transparent electrowetting-on-dielectric device driven by triboelectric nanogenerator for extremely fast anti-fogging. Nano Energy 92, 106697 (2022). https://doi.org/10.1016/j.nanoen.2021.106697
  118. L. Jin, B. Zhang, L. Zhang, W. Yang, Nanogenerator as new energy technology for self-powered intelligent transportation system. Nano Energy 66, 104086 (2019). https://doi.org/10.1016/j.nanoen.2019.104086
  119. H. Wang, A. Jasim, X. Chen, Energy harvesting technologies in roadway and bridge for different applications - a comprehensive review. Appl. Energy 212, 1083–1094 (2018). https://doi.org/10.1016/j.apenergy.2017.12.125
  120. M. Gholikhani, H. Roshani, S. Dessouky, A.T. Papagiannakis, A critical review of roadway energy harvesting technologies. Appl. Energy 261, 114388 (2020). https://doi.org/10.1016/j.apenergy.2019.114388
  121. J.P. Batista, A real-time driver visual attention monitoring system. Lecture Notes in Computer Science, 200–208 (2005). https://doi.org/10.1007/11492429_25
  122. Q. Ji, Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging 8, 357–377 (2002). https://doi.org/10.1006/rtim.2002.0279
  123. S. Hu, G. Zheng, B. Peters, Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal. IET Intell. Transp. Syst. 7, 105–113 (2013). https://doi.org/10.1049/iet-its.2012.0045
  124. X. Meng, Q. Cheng, X. Jiang, Z. Fang, X. Chen et al., Triboelectric nanogenerator as a highly sensitive self-powered sensor for driver behavior monitoring. Nano Energy 51, 721–727 (2018). https://doi.org/10.1016/j.nanoen.2018.07.026
  125. X. Lu, L. Zheng, H. Zhang, W. Wang, Z.L. Wang et al., Stretchable, transparent triboelectric nanogenerator as a highly sensitive self-powered sensor for driver fatigue and distraction monitoring. Nano Energy 78, 105359 (2020). https://doi.org/10.1016/j.nanoen.2020.105359
  126. Y. Feng, X. Huang, S. Liu, W. Guo, Y. Li et al., A self-powered smart safety belt enabled by triboelectric nanogenerators for driving status monitoring. Nano Energy 62, 197–204 (2019). https://doi.org/10.1016/j.nanoen.2019.05.043
  127. Z. Xie, Z. Zeng, Y. Wang, W. Yang, Y. Xu et al., Novel sweep-type triboelectric nanogenerator utilizing single freewheel for random triggering motion energy harvesting and driver habits monitoring. Nano Energy 68, 104360 (2020). https://doi.org/10.1016/j.nanoen.2019.104360
  128. Y. Xu, W. Yang, X. Yu, H. Li, T. Cheng et al., Real-time monitoring system of automobile driver status and intelligent fatigue warning based on triboelectric nanogenerator. ACS Nano 15(4), 7271–7278 (2021). https://doi.org/10.1021/acsnano.1c00536
  129. J. Yang, Y. Sun, J. Zhang, B. Chen, Z.L. Wang, 3D-printed bearing structural triboelectric nanogenerator for intelligent vehicle monitoring. Cell Rep. Phys. Sci. 2, 100666 (2021). https://doi.org/10.1016/j.xcrp.2021.100666
  130. J. Qian, D.S. Kim, D.W. Lee, On-vehicle triboelectric nanogenerator enabled self-powered sensor for tire pressure monitoring. Nano Energy 49, 126–136 (2018). https://doi.org/10.1016/j.nanoen.2018.04.022
  131. Y.C. Yang, W.L. Chen, A nonlinear inverse problem in estimating the heat flux of the disc in a disc brake system. Appl. Therm. Eng. 31, 2439–2448 (2011). https://doi.org/10.1016/j.applthermaleng.2011.04.008
  132. M. Kim, Y. Ra, S. Cho, S. Jang, D. Kam et al., Geometric gradient assisted control of the triboelectric effect in a smart brake system for self-powered mechanical abrasion monitoring. Nano Energy 89, 106448 (2021). https://doi.org/10.1016/j.nanoen.2021.106448
  133. Y. Tang, W. Xuan, C. Zhang, L. Xu, F. Liu et al., Fully self-powered instantaneous wireless traffic monitoring system based on triboelectric nanogenerator and magnetic resonance coupling. Nano Energy 89, 106429 (2021). https://doi.org/10.1016/j.nanoen.2021.106429
  134. X. Yang, G. Liu, Q. Guo, H. Wen, R. Huang et al., Triboelectric sensor array for internet of things based smart traffic monitoring and management system. Nano Energy 92, 106757 (2022). https://doi.org/10.1016/j.nanoen.2021.106757
  135. C. Shan, W. Liu, Z. Wang, X. Pu, W. He et al., An inverting TENG to realize the AC mode based on the coupling of triboelectrification and air-breakdown. Energy Environ. Sci. 14, 5395–5405 (2021). https://doi.org/10.1039/d1ee01641e
  136. C. Zhang, Y. Liu, B. Zhang, O. Yang, W. Yuan et al., Harvesting wind energy by a triboelectric nanogenerator for an intelligent high-speed train system. ACS Energy Lett. 6(4), 1490–1499 (2021). https://doi.org/10.1021/acsenergylett.1c00368
  137. Y. Du, Q. Tang, W. He, W. Liu, Z. Wang et al., Harvesting ambient mechanical energy by multiple mode triboelectric nanogenerator with charge excitation for self-powered freight train monitoring. Nano Energy 90, 106543 (2021). https://doi.org/10.1016/j.nanoen.2021.106543
  138. L. Jin, W. Deng, Y. Su, Z. Xu, H. Meng et al., Self-powered wireless smart sensor based on maglev porous nanogenerator for train monitoring system. Nano Energy 38, 185–192 (2017). https://doi.org/10.1016/j.nanoen.2017.05.018
  139. C. Zhang, L. Liu, L. Zhou, X. Yin, X. Wei et al., Self-powered sensor for quantifying ocean surface water waves based on triboelectric nanogenerator. ACS Nano 14(6), 7092–7100 (2020). https://doi.org/10.1021/acsnano.0c01827
  140. Z. Wang, Y. Yu, Y. Wang, X. Lu, T. Cheng et al., Magnetic flap-type difunctional sensor for detecting pneumatic flow and liquid level based on triboelectric nanogenerator. ACS Nano 14(5), 5981–5987 (2020). https://doi.org/10.1021/acsnano.0c01436
  141. Z. Ren, X. Liang, D. Liu, X. Li, J. Ping et al., Water-wave driven route avoidance warning system for wireless ocean navigation. Adv. Energy Mater. 11(31), 2101116 (2021). https://doi.org/10.1002/aenm.202101116
  142. J. An, Z. Wang, T. Jiang, P. Chen, X. Liang et al., Reliable mechatronic indicator for self-powered liquid sensing toward smart manufacture and safe transportation. Mater. Today 41, 10–20 (2020). https://doi.org/10.1016/j.mattod.2020.06.003
  143. S. Wang, Y. Wang, D. Liu, Z. Zhang, W. Li et al., A robust and self-powered tilt sensor based on annular liquid-solid interfacing triboelectric nanogenerator for ship attitude sensing. Sens. Actuat. A Phys. 317, 112459 (2021). https://doi.org/10.1016/j.sna.2020.112459
  144. Z. Lin, Q. He, Y. Xiao, T. Zhu, J. Yang et al., Flexible timbo-like triboelectric nanogenerator as self-powered force and bend sensor for wireless and distributed landslide monitoring. Adv. Mater. Technol. 3(11), 1800144 (2018). https://doi.org/10.1002/admt.201800144
  145. S. Li, D. Liu, Z. Zhao, L. Zhou, X. Yin et al., A fully self-powered vibration monitoring system driven by dual-mode triboelectric nanogenerators. ACS Nano 14(2), 2475–2482 (2020). https://doi.org/10.1021/acsnano.9b10142
  146. B. Zhang, J. Chen, L. Jin, W. Deng, L. Zhang et al., Rotating-disk-based hybridized electromagnetic-triboelectric nanogenerator for sustainably powering wireless traffic volume sensors. ACS Nano 10(6), 6241–6247 (2016). https://doi.org/10.1021/acsnano.6b02384
  147. C. Sukumaran, V. Vivekananthan, V. Mohan, Z.C. Alex, A. Chandrasekhar et al., Triboelectric nanogenerators from reused plastic: an approach for vehicle security alarming and tire motion monitoring in rover. Appl. Mater. Today 19, 100625 (2020). https://doi.org/10.1016/j.apmt.2020.100625
  148. Y. Pang, S. Chen, J. An, K. Wang, Y. Deng et al., Multilayered cylindrical triboelectric nanogenerator to harvest kinetic energy of tree branches for monitoring environment condition and forest fire. Adv. Funct. Mater. 30(32), 2003598 (2020). https://doi.org/10.1002/adfm.202003598
  149. X. Zhang, J. Hu, Q. Yang, H. Yang, H. Yang et al., Harvesting multidirectional breeze energy and self-powered intelligent fire detection systems based on triboelectric nanogenerator and fluid-dynamic modeling. Adv. Funct. Mater. 31(50), 2106527 (2021). https://doi.org/10.1002/adfm.202106527
  150. X. Gao, F. Xing, F. Guo, Y. Yang, Y. Hao et al., A turbine disk-type triboelectric nanogenerator for wind energy harvesting and self-powered wildfire pre-warning. Mater. Today Energy 22, 100867 (2021). https://doi.org/10.1016/j.mtener.2021.100867
  151. Y. Zhang, X. Gao, Y. Zhang, J. Gui, C. Sun et al., High-efficiency self-charging power systems based on performance-enhanced hybrid nanogenerators and asymmetric supercapacitors for outdoor search and rescue. Nano Energy 92, 106788 (2022). https://doi.org/10.1016/j.nanoen.2021.106788
  152. S.B. Atitallah, M. Driss, W. Boulila, H.B. Ghézala, Leveraging deep learning and IoT big data analytics to support the smart cities development: review and future directions. Comput. Sci. Rev. 38, 100303 (2020). https://doi.org/10.1016/j.cosrev.2020.100303
  153. M. Fahmideh, D. Zowghi, An exploration of IoT platform development. Inf. Syst. 87, 101409 (2020). https://doi.org/10.1016/j.is.2019.06.005
  154. W.R. Ali, M. Prasad, Piezoelectric MEMS based acoustic sensors: a review. Sens. Actuat. A Phys. 301, 111756 (2020). https://doi.org/10.1016/j.sna.2019.111756
  155. S. Wang, J. Ma, X. Shi, Y. Zhu, Z.S. Wu, Recent status and future perspectives of ultracompact and customizable micro-supercapacitors. Nano Res. Energy 1, e9120018 (2022). https://doi.org/10.26599/NRE.2022.9120018
  156. J. Yu, X. Yang, G. Gao, Y. Xiong, Y. Wang et al., Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure. Sci. Adv. 7, eabd9117 (2022). https://doi.org/10.1126/sciadv.abd9117
  157. M.C. Chiu, W.M. Yan, S.A. Bhat, N.F. Huang, Development of smart aquaculture farm management system using IoT and AI-based surrogate models. J. Agric. Food Res. 9, 100357 (2022). https://doi.org/10.1016/j.jafr.2022.100357
  158. M. Zhu, T. He, C. Lee, Technologies toward next generation human machine interfaces: from machine learning enhanced tactile sensing to neuromorphic sensory systems. Appl. Phys. Rev. 7, 031305 (2020). https://doi.org/10.1063/5.0016485
  159. R. Yin, D. Wang, S. Zhao, Z. Lou, G. Shen, Wearable sensors-enabled human-machine interaction systems: from design to application. Adv. Funct. Mater. 31(11), 2008936 (2020). https://doi.org/10.1002/adfm.202008936
  160. H. Wang, X. Ma, Y. Hao, Electronic devices for human-machine interfaces. Adv. Mater. Interfaces 4(4), 1600709 (2017). https://doi.org/10.1002/admi.201600709
  161. E. Principi, S. Squartini, R. Bonfigli, G. Ferroni, F. Piazza, An integrated system for voice command recognition and emergency detection based on audio signals. Exp. Syst. Appl. 42, 5668–5683 (2015). https://doi.org/10.1016/j.eswa.2015.02.036
  162. Q. Shi, B. Dong, T. He, Z. Sun, J. Zhu et al., Progress in wearable electronics/photonics—moving toward the era of artificial intelligence and internet of things. InfoMat 2, 1131–1162 (2020). https://doi.org/10.1002/inf2.12122
  163. B. Dong, Q. Shi, Y. Yang, F. Wen, Z. Zhang et al., Technology evolution from self-powered sensors to AIoT enabled smart homes. Nano Energy 79, 105414 (2021). https://doi.org/10.1016/j.nanoen.2020.105414
  164. J. Li, Z. Ma, H. Wang, X. Gao, Z. Zhou et al., Skin-inspired electronics and its applications in advanced intelligent systems. Adv. Intell. Syst. 1(6), 1970060 (2019). https://doi.org/10.1002/aisy.201970060
  165. C. Wang, L. Dong, D. Peng, C. Pan, Tactile sensors for advanced intelligent systems. Adv. Intell. Syst. 1(8), 1900090 (2019). https://doi.org/10.1002/aisy.201900090
  166. Y. Han, F. Yi, C. Jiang, K. Dai, Y. Xu et al., Self-powered gait pattern-based identity recognition by a soft and stretchable triboelectric band. Nano Energy 56, 516–523 (2019). https://doi.org/10.1016/j.nanoen.2018.11.078
  167. J. Chen, G. Zhu, J. Yang, Q. Jing, P. Bai et al., Personalized keystroke dynamics for self-powered human-machine interfacing. ACS Nano 9(1), 105–116 (2015). https://doi.org/10.1021/nn506832w
  168. A.P. Plageras, K.E. Psannis, C. Stergiou, H. Wang, B.B. Gupta, Efficient IoT-based sensor big data collection-processing and analysis in smart buildings. Future Generation Comput. Syst. 82, 349–357 (2018). https://doi.org/10.1016/j.future.2017.09.082
  169. M. Syafrudin, G. Alfian, N.L. Fitriyani, J. Rhee, Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18(9), 2946 (2018). https://doi.org/10.3390/s18092946
  170. H. Liu, W. Dong, Y. Li, F. Li, J. Geng et al., An epidermal sEMG tattoo-like patch as a new human-machine interface for patients with loss of voice. Microsyst. Nanoeng. 6, 16 (2020). https://doi.org/10.1038/s41378-019-0127-5
  171. H. Zhang, Q. Cheng, X. Lu, W. Wang, Z.L. Wang et al., Detection of driving actions on steering wheel using triboelectric nanogenerator via machine learning. Nano Energy 79, 105455 (2021). https://doi.org/10.1016/j.nanoen.2020.105455
  172. J. Yun, N. Jayababu, D. Kim, Self-powered transparent and flexible touchpad based on triboelectricity towards artificial intelligence. Nano Energy 78, 105325 (2020). https://doi.org/10.1016/j.nanoen.2020.105325
  173. P. Maharjan, K. Shrestha, T. Bhatta, H. Cho, C. Park et al., Keystroke dynamics based hybrid nanogenerators for biometric authentication and identification using artificial intelligence. Adv. Sci. 8, e2100711 (2021). https://doi.org/10.1002/advs.202100711
  174. I.W. Tcho, W.G. Kim, Y.K. Choi, A self-powered character recognition device based on a triboelectric nanogenerator. Nano Energy 70, 104534 (2020). https://doi.org/10.1016/j.nanoen.2020.104534
  175. G. Zhao, J. Yang, J. Chen, G. Zhu, Z. Jiang et al., Keystroke dynamics identification based on triboelectric nanogenerator for intelligent keyboard using deep learning method. Adv. Mater. Technol. 4(1), 1800167 (2019). https://doi.org/10.1002/admt.201800167
  176. F. Monrose, A.D. Rubin, Keystroke dynamics as a biometric for authentication. Future Generation Comput. Syst. 16, 351–359 (2000). https://doi.org/10.1016/s0167-739x(99)00059-x
  177. C. Wu, W. Ding, R. Liu, J. Wang, A.C. Wang et al., Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array. Mater. Today 21, 216–222 (2018). https://doi.org/10.1016/j.mattod.2018.01.006
  178. Z. Zhang, Q. Shi, T. He, X. Guo, B. Dong et al., Artificial intelligence of toilet (AI-Toilet). for an integrated health monitoring system (IHMS). using smart triboelectric pressure sensors and image sensor. Nano Energy 90, 106517 (2021). https://doi.org/10.1016/j.nanoen.2021.106517
  179. Z. Wen, M.H. Yeh, H. Guo, J. Wang, Y. Zi et al., Self-powered textile for wearable electronics by hybridizing fiber-shaped nanogenerators, solar cells, and supercapacitors. Sci. Adv. 2, e1600097 (2016). https://doi.org/10.1126/sciadv.1600097
  180. B. Dong, Q. Shi, T. He, S. Zhu, Z. Zhang et al., Wearable triboelectric/aluminum nitride nano-energy-nano-system with self-sustainable photonic modulation and continuous force sensing. Adv. Sci. 7, 1903636 (2020). https://doi.org/10.1002/advs.201903636
  181. T. He, Q. Shi, H. Wang, F. Wen, T. Chen et al., Beyond energy harvesting - multi-functional triboelectric nanosensors on a textile. Nano Energy 57, 338–352 (2019). https://doi.org/10.1016/j.nanoen.2018.12.032
  182. L. Chen, Q. Shi, Y. Sun, T. Nguyen, C. Lee et al., Controlling surface charge generated by contact electrification: strategies and applications. Adv. Mater. 30(47), e1802405 (2018). https://doi.org/10.1002/adma.201802405
  183. G. Cai, J. Wang, K. Qian, J. Chen, S. Li et al., Extremely stretchable strain sensors based on conductive self-healing dynamic cross-links hydrogels for human-motion detection. Adv. Sci. 4(2), 1600190 (2017). https://doi.org/10.1002/advs.201600190
  184. R. Caldas, M. Mundt, W. Potthast, F.B.L. Neto, B. Markert, A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms. Gait Posture 57, 204–210 (2017). https://doi.org/10.1016/j.gaitpost.2017.06.019
  185. Q. Zhang, T. Jin, J. Cai, L. Xu, T. He et al., Wearable triboelectric sensors enabled gait analysis and waist motion capture for IoT-based smart healthcare applications. Adv. Sci. 9, e2103694 (2022). https://doi.org/10.1002/advs.202103694
  186. Q. Shi, Z. Zhang, T. He, Z. Sun, B. Wang et al., Deep learning enabled smart mats as a scalable floor monitoring system. Nat. Commun. 11, 4609 (2020). https://doi.org/10.1038/s41467-020-18471-z
  187. Z. Sun, M. Zhu, Z. Zhang, Z. Chen, Q. Shi et al., Artificial intelligence of things (AIoT). enabled virtual shop applications using self-powered sensor enhanced soft robotic manipulator. Adv. Sci. 8, e2100230 (2021). https://doi.org/10.1002/advs.202100230
  188. T. Yan, D. Xie, Z. Chen, R. Yang, K. Zhu et al., Initial oxidation of U3Si2 studied by in-situ XPS analysis. J. Nucl. Mater. 520, 1–5 (2019). https://doi.org/10.1016/j.jnucmat.2019.04.005
  189. Z. Yuan, G. Shen, C. Pan, Z.L. Wang, Flexible sliding sensor for simultaneous monitoring deformation and displacement on a robotic hand/arm. Nano Energy 73, 104764 (2020). https://doi.org/10.1016/j.nanoen.2020.104764
  190. G.Q. Gu, C.B. Han, C.X. Lu, C. He, T. Jiang et al., Triboelectric nanogenerator enhanced nanofiber air filters for efficient particulate matter removal. ACS Nano 11(6), 6211–6217 (2017). https://doi.org/10.1021/acsnano.7b02321
  191. C. Zhang, W. Tang, C. Han, F. Fan, Z.L. Wang, Theoretical comparison, equivalent transformation, and conjunction operations of electromagnetic induction generator and triboelectric nanogenerator for harvesting mechanical energy. Adv. Mater. 26(22), 3580–3591 (2014). https://doi.org/10.1002/adma.201400207
  192. H. Qin, G. Gu, W. Shang, H. Luo, W. Zhang et al., A universal and passive power management circuit with high efficiency for pulsed triboelectric nanogenerator. Nano Energy 68, 104372 (2020). https://doi.org/10.1016/j.nanoen.2019.104372
  193. F. Xi, Y. Pang, W. Li, T. Jiang, L. Zhang et al., Universal power management strategy for triboelectric nanogenerator. Nano Energy 37, 168–176 (2017). https://doi.org/10.1016/j.nanoen.2017.05.027
  194. L. Xia, T. Long, W. Li, F. Zhong, M. Ding et al., Highly stable vanadium redox-flow battery assisted with redox-mediated catalysis. Small 16(38), 2003321 (2020). https://doi.org/10.1002/smll.202003321
  195. S. Qin, Q. Zhang, X. Yang, M. Liu, Q. Sun et al., Hybrid piezo/triboelectric-driven self-charging electrochromic supercapacitor power package. Adv. Energy Mater. 8(23), 1800069 (2022). https://doi.org/10.1002/aenm.201800069
  196. C. Li, B. Liu, N. Jiang, Y. Ding, Elucidating the charge-transfer and Li-ion-migration mechanisms in commercial lithium-ion batteries with advanced electron microscopy. Nano Res. Energy 1(3), 9120031 (2022). https://doi.org/10.26599/NRE.2022.9120031
  197. L. Chu, S. Zhai, W. Ahmad, J. Zhang, Y. Zang et al., High-performance large-area perovskite photovoltaic modules, Nano Res. Energy 1, e9120024 (2022). https://doi.org/10.26599/NRE.2022.9120024
  198. D. Li, Y. Gong, Y. Chen, J. Lin, Q. Khan et al., Recent progress of two-dimensional thermoelectric materials. Nano-Micro Lett. 12, 36 (2020). https://doi.org/10.1007/s40820-020-0374-x
  199. J.D. Musah, A.M. Ilyas, S. Venkatesh, S. Mensah, S. Kwofie et al., Isovalent substitution in metal chalcogenide materials for improving thermoelectric power generation—a critical review. Nano Res. Energy 1(3), 9120034 (2022). https://doi.org/10.26599/NRE.2022.9120034
  200. T. Yu, X. Wang, A. Shami, UAV-enabled spatial data sampling in large-scale IoT systems using denoising autoencoder neural network. IEEE Int. Things J. 6, 1856–1865 (2019). https://doi.org/10.1109/jiot.2018.2876695
  201. X. Zhang, Q. Gao, Q. Gao, X. Yu, T. Cheng et al., Triboelectric rotary motion sensor for industrial-grade speed and angle monitoring. Sensors 21(5), 1713 (2021). https://doi.org/10.3390/s21051713
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