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Dual-sensory fusion self-powered triboelectric taste-sensing system towards effective and low-cost liquid identification

Nature Food volume 4, pages 721–732 (2023)Cite this article

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Abstract

Infusing human taste perception into smart sensing devices to mimic the processing ability of gustatory organs to perceive liquid substances remains challenging. Here we developed a self-powered droplet-tasting sensor system based on the dynamic morphological changes of droplets and liquid–solid contact electrification. The sensor system has achieved accuracies of liquid recognition higher than 90% in five different applications by combining triboelectric fingerprint signals and deep learning. Furthermore, an image sensor is integrated to extract the visual features of liquids, and the recognition capability of the liquid-sensing system is improved to up to 96.0%. The design of this dual-sensory fusion self-powered liquid-sensing system, along with the droplet-tasting sensor that can autonomously generate triboelectric signals, provides a promising technological approach for the development of effective and low-cost liquid sensing for liquid food safety identification and management.

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Fig. 1: Droplet-based triboelectric taste-sensing system mimicking the human taste receptor.
Fig. 2: The working principle of a droplet-tasting sensor with one spatially arranged electrode, the sliding process of a water droplet and the electrical output of a single-electrode droplet-tasting sensor.
Fig. 3: Mechanism and sensing characteristics of a TDTS for sensing a droplet at two electrodes.
Fig. 4: Feature extraction of different liquids from droplet-induced triboelectric signals.
Fig. 5: Deep-learning-based data processing for liquid-type identification.
Fig. 6: Synergistic effect of triboelectric signals and image features for a higher accuracy of liquid identification.

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Data availability

All relevant data are included in the article, Supplementary Information and the source data files provided with this paper. All the other raw data are available from the corresponding authors on request.

Code availability

The code is available from the corresponding authors upon reasonable request.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (grant number 61503051; Z.W.).

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Authors and Affiliations

  1. Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, P. R. China

    Xuelian Wei, Baocheng Wang, Xiaole Cao, Hanlin Zhou, Zhiyi Wu & Zhong Lin Wang

  2. School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, P. R. China

    Xuelian Wei, Baocheng Wang, Xiaole Cao & Zhiyi Wu

  3. Georgia Institute of Technology, Atlanta, GA, USA

    Zhong Lin Wang

  4. Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea

    Zhong Lin Wang

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  1. Xuelian Wei
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Contributions

Z.W. and Z.L.W. planned the study and supervised the whole project. X.W., Z.W. and Z.L.W. conceived the idea, analysed the data and wrote the paper. B.W., X.C. and H.Z. helped with the experiments. All the authors discussed the results and commented on the paper.

Corresponding authors

Correspondence to Zhiyi Wu or Zhong Lin Wang.

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The authors declare no competing interests.

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Nature Food thanks Zong-Hong Lin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–28, Table 1 and Note 1.

Reporting Summary

Supplementary Video 1

The dynamic of one water droplet captured by a high-speed camera (a front view).

Supplementary Video 2

A water droplet sliding down an inclined surface.

Supplementary Video 3

Simulation of droplet sliding along an inclined plane by volume fraction of fluid.

Supplementary Video 4

Simulation of droplet sliding along an inclined plane by velocity field.

Supplementary Video 5

The signal response triggered by a coffee droplet sliding.

Supplementary Video 6

The signal response triggered by a water droplet sliding.

Supplementary Video 7

The taste-sensing system effectively identifies different liquids in a real environment.

Supplementary Data

Source data for supplementary figures.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

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Wei, X., Wang, B., Cao, X. et al. Dual-sensory fusion self-powered triboelectric taste-sensing system towards effective and low-cost liquid identification. Nat Food 4, 721–732 (2023). https://doi.org/10.1038/s43016-023-00817-7

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