Issue

Vol. 14 (2022)

An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

https://doi.org/10.1007/s40820-022-00875-9
Chao Wei
Department of Electronic Science, Xiamen University, Xiamen 361005, People’s Republic of China
Wansheng Lin
Department of Electronic Science, Xiamen University, Xiamen 361005, People’s Republic of China
Shaofeng Liang
Department of Electronic Science, Xiamen University, Xiamen 361005, People’s Republic of China
Mengjiao Chen
Department of Electronic Science, Xiamen University, Xiamen 361005, People’s Republic of China
Yuanjin Zheng
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Xinqin Liao
Department of Electronic Science, Xiamen University, Xiamen 361005, People’s Republic of China
Zhong Chen
Department of Electronic Science, Xiamen University, Xiamen 361005, People’s Republic of China

Corresponding Author: Zhong Chen

chenz@xmu.edu.cn

Nano-Micro Letters, Vol. 14 (2022), Article Number: 131

Abstract

Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.


Highlights:


1 Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations.
2 Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions.
3 Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world.

Keywords

Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction
Wei, Chao, Wansheng Lin, Shaofeng Liang, Mengjiao Chen, Yuanjin Zheng, Xinqin Liao, and Zhong Chen. 2022. “An All-In-One Multifunctional Touch Sensor With Carbon-Based Gradient Resistance Elements”. Nano-Micro Letters 14 (June):131. https://doi.org/10.1007/s40820-022-00875-9.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Z. Liu, T. Zhu, J. Wang, Z. Zheng, Y. Li et al., Functionalized fiber-based strain sensors: pathway to next-generation wearable electronics. Nano-Micro Lett. 14, 61 (2022). https://doi.org/10.1007/s40820-022-00806-8
  2. Y. Liang, Z. Wu, Y. Wei, Q. Ding, M. Zilberman et al., Self-healing, self-adhesive and stable organohydrogel-based stretchable oxygen sensor with high performance at room temperature. Nano-Micro Lett. 14, 52 (2022). https://doi.org/10.1007/s40820-021-00787-0
  3. X. Yu, Z. Xie, Y. Yu, J. Lee, A. Vazquez-Guardado et al., Skin-integrated wireless haptic interfaces for virtual and augmented reality. Nature 575(7783), 473–479 (2019). https://doi.org/10.1038/s41586-019-1687-0
  4. X. Lin, F. Li, Y. Bing, T. Fei, S. Liu et al., Biocompatible multifunctional e-skins with excellent self-healing ability enabled by clean and scalable fabrication. Nano-Micro Lett. 13, 200 (2021). https://doi.org/10.1007/s40820-021-00701-8
  5. Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh et al., A bioinspired flexible organic artificial afferent nerve. Science 360(6392), 998–1003 (2018). https://doi.org/10.1126/science.aao0098
  6. T.R. Ray, J. Choi, A.J. Bandodkar, S. Krishnan, P. Gutruf et al., Bio-integrated wearable systems: a comprehensive review. Chem. Rev. 119(8), 5461–5533 (2019). https://doi.org/10.1021/acs.chemrev.8b00573
  7. A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter et al., Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056
  8. L. Guo, T. Wang, Z. Wu, J. Wang, M. Wang et al., Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks. Adv. Mater. 32(45), 2004805 (2020). https://doi.org/10.1002/adma.202004805
  9. F. Wen, Z. Zhang, T. He, C. Lee, AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove. Nat. Commun. 12, 5378 (2021). https://doi.org/10.1038/s41467-021-25637-w
  10. T. Li, Y. Su, F. Chen, X. Liao, Q. Wu et al., A skin-like and highly stretchable optical fiber sensor with the hybrid coding of wavelength-light intensity. Adv. Intell. Syst. 4(4), 2100193 (2021). https://doi.org/10.1002/aisy.202100193
  11. P.H.C. Chen, K. Gadepalli, R. MacDonald, Y. Liu, S. Kadowaki et al., An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat. Med. 25(9), 1453–1457 (2019). https://doi.org/10.1038/s41591-019-0539-7
  12. M. Zhu, Z. Sun, Z. Zhang, Q. Shi, T. He et al., Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications. Sci. Adv. 6(19), eaaz8693 (2020). https://doi.org/10.1126/sciadv.aaz8693
  13. M. Xu, F. Li, Z. Zhang, T. Shen, J. Qi, Piezoresistive sensors based on rGO 3D microarchitecture: coupled properties tuning in local/integral deformation. Adv. Electron. Mater. 5(1), 1800461 (2019). https://doi.org/10.1002/aelm.201800461
  14. X. Liao, W. Wang, M. Lin, M. Li, H. Wu et al., Hierarchically distributed microstructure design of haptic sensors for personalized fingertip mechanosensational manipulation. Mater. Horiz. 5(5), 920–931 (2018). https://doi.org/10.1039/c8mh00680f
  15. S. Niu, N. Matsuhisa, L. Beker, J. Li, S. Wang et al., A wireless body area sensor network based on stretchable passive tags. Nat. Electron. 2(8), 361–368 (2019). https://doi.org/10.1038/s41928-019-0286-2
  16. Q. Liu, Y. Liu, J. Shi, Z. Liu, Q. Wang et al., High-porosity foam-based iontronic pressure sensor with superhigh sensitivity of 9280 kpa−1. Nano-Micro Lett. 14, 21 (2022). https://doi.org/10.1007/s40820-021-00770-9
  17. L. Zhong, X. Lai, D. Xu, X. Liao, C. Yang et al., Capacitive touch panel with low sensitivity to water drop employing mutual-coupling electrical field shaping technique. IEEE Trans Circuits. Syst. I Regul. Pap. 66(4), 1393–1404 (2018). https://doi.org/10.1109/tcsi.2018.2879410
  18. N. Bai, L. Wang, Q. Wang, J. Deng, Y. Wang et al., Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity. Nat. Commun. 11, 209 (2020). https://doi.org/10.1038/s41467-019-14054-9
  19. X. Zhao, Z. Zhang, Q. Liao, X. Xun, F. Gao et al., Self-powered user-interactive electronic skin for programmable touch operation platform. Sci. Adv. 6(28), eaba4294 (2020). https://doi.org/10.1126/sciadv.aba4294
  20. X. Xun, Z. Zhang, X. Zhao, B. Zhao, F. Gao et al., Highly robust and self-powered electronic skin based on tough conductive self-healing elastomer. ACS Nano 14(7), 9066–9072 (2020). https://doi.org/10.1021/acsnano.0c04158
  21. X. Liao, X. Yan, P. Lin, S. Lu, Y. Tian et al., Enhanced performance of ZnO piezotronic pressure sensor through electron-tunneling modulation of MgO nanolayer. ACS Appl. Mater. Interfaces 7(3), 1602–1607 (2015). https://doi.org/10.1021/am5070443
  22. W.Q. Liao, D. Zhao, Y.Y. Tang, Y. Zhang, P.F. Li et al., A molecular perovskite solid solution with piezoelectricity stronger than lead zirconate titanate. Science 363(6432), 1206–1210 (2019). https://doi.org/10.1126/science.aav3057
  23. X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2022). https://doi.org/10.1007/s40820-021-00740-1
  24. S.I. Rich, R.J. Wood, C. Majidi, Untethered soft robotics. Nat. Electron. 1(2), 102–112 (2018). https://doi.org/10.1038/s41928-018-0024-1
  25. A. Chortos, J. Liu, Z. Bao, Pursuing prosthetic electronic skin. Nat. Mater. 15(9), 937–950 (2016). https://doi.org/10.1038/nmat4671
  26. 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(14), 2000261 (2020). https://doi.org/10.1002/advs.202000261
  27. G. Schwartz, B.C.K. Tee, J. Mei, A.L. Appleton, D.H. Kim et al., Flexible polymer transistors with high pressure sensitivity for application in electronic skin and health monitoring. Nat. Commun. 4, 1859 (2013). https://doi.org/10.1038/ncomms2832
  28. M. Ma, Z. Zhang, Q. Liao, F. Yi, L. Han et al., Self-powered artificial electronic skin for high-resolution pressure sensing. Nano Energy 32, 389–396 (2017). https://doi.org/10.1016/j.nanoen.2017.01.004
  29. M. Wang, W. Wang, W.R. Leow, C. Wan, G. Chen et al., Enhancing the matrix addressing of flexible sensory arrays by a highly nonlinear threshold switch. Adv. Mater. 30(33), 1802516 (2018). https://doi.org/10.1002/adma.201802516
  30. X. Liao, W. Song, X. Zhang, H. Zhan, Y. Liu et al., Hetero-contact microstructure to program discerning tactile interactions for virtual reality. Nano Energy 60, 127–136 (2019). https://doi.org/10.1016/j.nanoen.2019.03.048
  31. J. Pang, A. Bachmatiuk, F. Yang, H. Liu, W. Zhou et al., Applications of carbon nanotubes in the internet of things era. Nano-Micro Lett. 13, 191 (2021). https://doi.org/10.1007/s40820-021-00721-4
  32. A.B. Patil, Z. Meng, R. Wu, L. Ma, Z. Xu et al., Tailoring the meso-structure of gold nanops in keratin-based activated carbon toward high-performance flexible sensor. Nano-Micro Lett. 12, 117 (2020). https://doi.org/10.1007/s40820-020-00459-5
  33. Q. Li, X. Zhao, Z. Zhang, X. Xun, B. Zhao et al., Architecture design and interface engineering of self-assembly VS4/rGO heterostructures for ultrathin absorbent. Nano-Micro Lett. 14, 67 (2022). https://doi.org/10.1007/s40820-022-00809-5
  34. A. Handler, D.D. Ginty, The mechanosensory neurons of touch and their mechanisms of activation. Nat. Rev. Neurosci. 22, 521–537 (2021). https://doi.org/10.1038/s41583-021-00489-x
  35. A. Zimmerman, L. Bai, D.D. Ginty, The gentle touch receptors of mammalian skin. Science 346(6212), 950–954 (2014). https://doi.org/10.1126/science.1254229
  36. S. Felton, M. Tolley, E. Demaine, D. Rus, R. Wood, A method for building self-folding machines. Science 345(6197), 644–646 (2014). https://doi.org/10.1126/science.1252610
  37. D. Rus, M.T. Tolley, Design, fabrication and control of origami robots. Nat. Rev. Mater. 3(6), 101–112 (2018). https://doi.org/10.1038/nature14543
  38. S. Cinti, D. Moscone, F. Arduini, Preparation of paper-based devices for reagentless electrochemical (bio) sensor strips. Nat. Protoc. 14(8), 2437–2451 (2019). https://doi.org/10.1038/s41596-019-0186-y
  39. X. Liao, Q. Liao, X. Yan, Q. Liang, H. Si et al., Flexible and highly sensitive strain sensors fabricated by pencil drawn for wearable monitor. Adv. Funct. Mater. 25(16), 2395–2401 (2015). https://doi.org/10.1002/adfm.201500094
  40. S.S. Radhakrishnan, A. Sebastian, A. Oberoi, S. Das, A biomimetic neural encoder for spiking neural network. Nat. Commun. 12, 2143 (2021). https://doi.org/10.1038/s41467-021-22332-8
  41. C. Liu, Q. Ma, Z.J. Luo, Q.R. Hong, Q. Xiao et al., A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 5(2), 113–122 (2022). https://doi.org/10.1038/s41928-022-00719-9
  42. F. Stelzer, A. Röhm, R. Vicente, I. Fischer, S. Yanchuk, Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops. Nat. Commun. 12, 5164 (2021). https://doi.org/10.1038/s41467-021-25427-4
  43. U. Teğin, M. Yıldırım, İ Oğuz, C. Moser, D. Psaltis, Scalable optical learning operator. Nat. Comput. Sci. 1(8), 542–549 (2021). https://doi.org/10.1038/s43588-021-00112-0
  44. S. Oh, Y. Shi, J.D. Valle, P. Salev, Y. Lu et al., Energy-efficient mott activation neuron for full-hardware implementation of neural networks. Nat. Nanotechnol. 16(6), 680–687 (2021). https://doi.org/10.1038/s41565-021-00874-8
  45. H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao et al., Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16(1), 103–110 (2019). https://doi.org/10.1038/s41592-018-0239-0
Read More
Author biographies are not available.
Statistic
Read Counter : 3594 Download : 2582

Most read articles by the same author(s)