The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Human–Machine Interfacing Enabled by Triboelectric Nanogenerators and Tribotronics
Abstract
With the advances of artificial intelligence (AI), autonomous robotics, virtual reality (VR) and Internet of things (IoT), an intelligent and ubiquitous human–machine interfacing (HMI) ecosystem has become a desire and attracted lots of interests. Triboelectric nanogenerators (TENG), as a natural and effective mechanical‐to‐electrical signal conversion technology, have been successfully utilized to realize many types of HMI, including smart keyboards, body motion sensors, the electronic skin, voice sensors, the triboelectrification‐induced electroluminescence, and the artificial muscle. Meanwhile, tribotronics, as the coupling field of TENG and semiconductors, has demonstrated the feasibility for tactile switch or sensing with the possibilities of high integration and large scale. The fundamental theory of TENG and tribotronics is revisited herein. In addition, the definition of HMI and, for the first time, the research progress of the TENG and tribotronics enabled HMI are systematically and thoroughly reviewed. Finally, the perspectives in this emerging field are also discussed.
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Hammock, M. L., Chortos, A., Tee, B. C. ‐., Tok, J. B. ‐., & Bao, Z. (2013) · Advanced Materials · doi:10.1002/adma.201302240 · Cited by 2,396
Schwartz, G., Tee, B. C.-K., Mei, J., Appleton, A. L., Kim, D. H., Wang, H., & Bao, Z. (2013) · Nature Communications · doi:10.1038/ncomms2832 · Cited by 2,042
User-interactive electronic skin for instantaneous pressure visualization
Wang, C., Hwang, D., Yu, Z., Takei, K., Park, J., Chen, T., Ma, B., & Javey, A. (2013) · Nature Materials · doi:10.1038/nmat3711 · Cited by 1,182
Yang, Y., Zhang, H., Lin, Z.-H., Zhou, Y. S., Jing, Q., Su, Y., Yang, J., Chen, J., Hu, C., & Wang, Z. L. (2013) · ACS Nano · doi:10.1021/nn403838y · Cited by 798
Self-citationPark, S., Kim, H., Vosgueritchian, M., Cheon, S., Kim, H., Koo, J. H., Kim, T. R., Lee, S., Schwartz, G., Chang, H., et al. (2014) · Advanced Materials · doi:10.1002/adma.201402574 · Cited by 565
Xie, Y., Wang, S., Niu, S., Lin, L., Jing, Q., Yang, J., Wu, Z., & Wang, Z. L. (2014) · Advanced Materials · doi:10.1002/adma.201402428 · Cited by 554
Self-citationFlexible suspended gate organic thin-film transistors for ultra-sensitive pressure detection
Zang, Y., Zhang, F., Huang, D., Gao, X., Di, C.-A., & Zhu, D. (2015) · Nature Communications · doi:10.1038/ncomms7269 · Cited by 548
An, B. W., Heo, S., Ji, S., Bien, F., & Park, J.-U. (2018) · Nature Communications · doi:10.1038/s41467-018-04906-1 · Cited by 426
Liu, Y., Norton, J. J. S., Qazi, R., Zou, Z., Ammann, K. R., Liu, H., Yan, L., Tran, P. L., Jang, K.-I., Lee, J. W., et al. (2016) · Science Advances · doi:10.1126/sciadv.1601185 · Cited by 397
An empirical study of machine learning techniques for affect recognition in human–robot interaction
Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313
Cited by
Wen, F., Zhang, Z., He, T., & Lee, C. (2021) · Nature Communications · doi:10.1038/s41467-021-25637-w · Cited by 494
Wen, F., Sun, Z., He, T., Shi, Q., Zhu, M., Zhang, Z., Li, L., Zhang, T., & Lee, C. (2020) · Advanced Science · doi:10.1002/advs.202000261 · Cited by 478
Shi, Q., Zhang, Z., Chen, T., & Lee, C. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.05.033 · Cited by 202
Shin, J. H., Kwon, J., Kim, J. U., Ryu, H., Ok, J., Joon Kwon, S., Park, H., & Kim, T.-I. (2022) · npj Flexible Electronics · doi:10.1038/s41528-022-00164-w · Cited by 123
Prasad, G., Graham, S. A., Yu, J. S., Kim, H., & Lee, D.-W. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108178 · Cited by 114
Metadata
- Authors
- Ding, W., Wang, A. C., Wu, C., Guo, H., & Wang, Z. L.
- Venue
- Advanced Materials Technologies
- Publisher
- Wiley
- Published
- 2018-11-28
- Volume
- 4
- Issue
- 1
- ISSN
- 2365-709X (ISSN-L)
- OpenAlex
- W2902546013
- Cited by
- 243
- References
- 12
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- closed
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