Abstract
Based on the triboelectric effect, self-powered triboelectric tactile sensors can convert mechanical inputs into electrical signals, attracting significant interest in self-sustaining, energy-efficient applications. This chapter discusses the triboelectric tactile matrix, single-electrode touchpad, artificial intelligence (AI) augmented tactile systems, and neuromorphic tactile systems. This chapter covers applications such as human-machine interface (HMI), intelligent robots, and artificial intelligence of things (AIoT). A triboelectric tactile matrix with high resolution, high sensitivity, and full dynamic range has been demonstrated as a fundamental component of E-skin. The construction of a triboelectric nanogenerator (TENG) with a single electrode enables functional applications while addressing the signal readout challenges of numerous sensor channels. Multiple smart applications have exhibited AI-enhanced triboelectric tactile sensing for HMI, intelligent robots, and AIoT, among others. By combining triboelectric sensors with neuromorphic circuits, triboelectric neuromorphic tactile sensing is promising for low-energy and high-speed computing. This may pave the way for AI-enhanced self-powered sensing to be utilized in the real world.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bai N et al (2020) Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity. Nat Commun 11:209. https://doi.org/10.1038/s41467-019-14054-9
Cao YZ et al (2020) High-resolution monolithic integrated tribotronic InGaZnO thin-film transistor array for tactile detection. Adv Funct Mater 30:2002613. https://doi.org/10.1002/adfm.202002613
Chun S et al (2019) Self-powered pressure- and vibration-sensitive tactile sensors for learning technique-based neural finger skin. Nano Lett 19:3305–3312. https://doi.org/10.1021/acs.nanolett.9b00922
Chun S et al (2021) An artificial neural tactile sensing system. Nature Electron 4:429–438. https://doi.org/10.1038/s41928-021-00585-x
Dong BW et al (2021) Technology evolution from self-powered sensors to AIoT enabled smart homes. Nano Energy 79:105414. https://doi.org/10.1016/j.nanoen.2020.105414
Dong BW et al (2022) Biometrics-protected optical communication enabled by deep learning-enhanced triboelectric/photonic synergistic interface. Advances 8:eabl9874. https://doi.org/10.1126/sciadv.abl9874
Han JK, Yun SY, Lee SW, Yu JM, Choi YK (2022) A review of artificial spiking neuron devices for neural processing and sensing. Adv Funct Mater 32:2204102. https://doi.org/10.1002/adfm.202204102
Huang YC et al (2020) Sensitive pressure sensors based on conductive microstructured air-gap gates and two-dimensional semiconductor transistors. Nature Electron 3:59–69. https://doi.org/10.1038/s41928-019-0356-5
Jin T et al (2020) Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications. Nat Commun 11:5381. https://doi.org/10.1038/s41467-020-19059-3
Liu YQ et al (2020) Self-powered high-sensitivity sensory memory actuated by triboelectric sensory receptor for real-time neuromorphic computing. Nano Energy 75:104930. https://doi.org/10.1016/j.nanoen.2020.104930
Luo YY et al (2021a) Learning human-environment interactions using conformal tactile textiles. Nature Electron 4:193–201. https://doi.org/10.1038/s41928-021-00558-0
Luo J, Gao W, Wang ZL (2021b) The triboelectric nanogenerator as an innovative technology toward intelligent sports. Adv Mater 33:2004178. https://doi.org/10.1002/adma.202004178
Pan M et al (2020) Triboelectric and piezoelectric nanogenerators for future soft robots and machines. iScience 23:101682. https://doi.org/10.1016/j.isci.2020.101682
Parida K et al (2017) Highly transparent, stretchable, and self-healing ionic-skin triboelectric nanogenerators for energy harvesting and touch applications. Adv Mater 29:1702181. https://doi.org/10.1002/adma.201702181
Pu X et al (2017) Ultrastretchable, transparent triboelectric nanogenerator as electronic skin for biomechanical energy harvesting and tactile sensing. Sci Adv 3:e1700015. https://doi.org/10.1126/sciadv.1700015
Ren ZW et al (2018) Fully elastic and metal-free tactile sensors for detecting both normal and tangential forces based on triboelectric nanogenerators. Adv Funct Mater 28:1802989. https://doi.org/10.1002/adfm.201802989
Shi QF, Lee CK (2019) Self-powered bio-inspired spider-net-coding interface using single-electrode triboelectric nanogenerator. Adv Sci 6:1900617. https://doi.org/10.1002/advs.201900617
Shi Q et al (2019) Triboelectric single-electrode-output control interface using patterned grid electrode. Nano Energy 60:545–556. https://doi.org/10.1016/j.nanoen.2019.03.090
Shi QF et al (2020) Deep learning enabled smart mats as a scalable floor monitoring system. Nat Commun 11:4609. https://doi.org/10.1038/s41467-020-18471-z
Shi Q, Yang Y, Sun Z, Lee CJAMA (2022) Progress of advanced devices and Internet of things systems as enabling technologies for smart homes and health care. ACS Mater 2:394–435. https://doi.org/10.1021/acsmaterialsau.2c00001
Sun FQ, Lu QF, Feng SM, Zhang T (2021a) Flexible artificial sensory systems based on neuromorphic devices. ACS Nano 15:3875–3899. https://doi.org/10.1021/acsnano.0c10049
Sun ZD et al (2021b) Artificial intelligence of things (AIoT) enabled virtual shop applications using self-powered sensor enhanced soft robotic manipulator. Adv Sci 8:2100230. https://doi.org/10.1002/advs.202100230
Sundaram S et al (2019) Learning the signatures of the human grasp using a scalable tactile glove. Nature 569:698–702. https://doi.org/10.1038/s41586-019-1234-z
Tan P et al (2022) Self-powered gesture recognition wristband enabled by machine learning for full keyboard and multicommand input. Adv Mater 34:2200793. https://doi.org/10.1002/adma.202200793
Wang ZL, Wang AC (2019) On the origin of contact-electrification. Mater Today 30:34–51. https://doi.org/10.1016/j.mattod.2019.05.016
Wang XD et al (2016) Self-powered high-resolution and pressure-sensitive triboelectric sensor matrix for real-time tactile mapping. Adv Mater 28:2896–2903. https://doi.org/10.1002/adma.201503407
Wang XD et al (2017) Full dynamic-range pressure sensor matrix based on optical and electrical dual-mode sensing. Adv Mater 29:1605817. https://doi.org/10.1002/adma.201605817
Wang X et al (2018) A highly stretchable transparent self-powered triboelectric tactile sensor with metallized nanofibers for wearable electronics. Adv Mater 30:1706738. https://doi.org/10.1002/adma.201706738
Wang C, Dong L, Peng D, Pan C (2019) Tactile sensors for advanced intelligent systems. Adv Intelligent Syst 1:1900090. https://doi.org/10.1002/aisy.201900090
Wu W, Wang ZL (2021) Convergence of more Moore, more than Moore, and beyond Moore Ch. 6. Jenny Stanford Publishing, pp 227–247
Wu W, Wen X, Wang ZLJS (2013) Taxel-addressable matrix of vertical-nanowire piezotronic transistors for active and adaptive tactile imaging. Science 340:952–957. https://doi.org/10.1126/science.1234855
Wu C et al (2018) Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array. Mater Today 21:216–222. https://doi.org/10.1016/j.mattod.2018.01.006
Wu CX et al (2020) Self-powered tactile sensor with learning and memory. ACS Nano 14:1390–1398. https://doi.org/10.1021/acsnano.9b07165
Xiao X, Fang YS, Xiao X, Xu J, Chen J (2021) Machine-learning-aided self-powered assistive physical therapy devices. ACS Nano 15:18633–18646. https://doi.org/10.1021/acsnano.1c10676
Yan ZG et al (2021) Flexible high-resolution triboelectric sensor array based on patterned laser-induced graphene for self-powered real-time tactile sensing. Adv Funct Mater 31:2100709. https://doi.org/10.1002/adfm.202100709
Yang ZW et al (2016) Tribotronic transistor array as an active tactile sensing system. ACS Nano 10:10912–10920. https://doi.org/10.1021/acsnano.6b05507
Yu JR et al (2021) Contact-electrification-activated artificial afferents at femtojoule energy. Nat Commun 12:1581. https://doi.org/10.1038/s41467-021-21890-1
Zhang ZX et al (2020) Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications. Npj Flex Electron 4:29. https://doi.org/10.1038/s41528-020-00092-7
Zhang ZX et al (2021) 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. https://doi.org/10.1016/j.nanoen.2021.106517
Zhang ZX et al (2022a) Artificial intelligence-enabled sensing technologies in the 5G/Internet of things era: from virtual reality/augmented reality to the digital twin. Adv Intelligent Syst 4:2100228. https://doi.org/10.1002/aisy.202100228
Zhang Q et al (2022b) Wearable triboelectric sensors enabled gait analysis and waist motion capture for IoT-based smart healthcare applications. Adv Sci 9:2103694. https://doi.org/10.1002/advs.202103694
Zhao GQ et al (2019) Keystroke dynamics identification based on triboelectric nanogenerator for intelligent keyboard using deep learning method. Adv Mater Technol 4:1800167. https://doi.org/10.1002/admt.201800167
Zhao GQ et al (2021) Multi-layer extreme learning machine-based keystroke dynamics identification for intelligent keyboard. IEEE Sensors J 21:2324–2333. https://doi.org/10.1109/JSEN.2020.3019777
Zhu ML, He TYY, Lee CK (2020) Technologies toward next generation human machine interfaces: from machine learning enhanced tactile sensing to neuromorphic sensory systems. Appl Phys Rev 7:031305. https://doi.org/10.1063/5.0016485
Zou H et al (2019) Quantifying the triboelectric series. Nat Commun 10:1427. https://doi.org/10.1038/s41467-019-09461-x
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this entry
Cite this entry
Xu, S., Wu, W. (2023). Triboelectric Nanogenerator for Tactile Sensing and AI. In: Wang, Z.L., Yang, Y., Zhai, J., Wang, J. (eds) Handbook of Triboelectric Nanogenerators. Springer, Cham. https://doi.org/10.1007/978-3-031-28111-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-28111-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28110-5
Online ISBN: 978-3-031-28111-2
eBook Packages: Chemistry and Materials ScienceReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics