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Updated: May 26, 2025

&#181;Tongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo
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Deep-Learning-Assisted Self-Powered Microfluidic Bionic Electronic Tongues.

Zihan Jin1,2, Yuhang Xue2, Bowei Zhang2

  • 1Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

ACS Applied Materials & Interfaces
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

A novel self-powered microfluidic bionic electronic tongue (SMET) uses liquid-solid contact electrification and deep learning for reliable taste detection. This advanced electronic tongue achieves high accuracy in identifying tastes and concentrations, overcoming previous stability issues.

Keywords:
bionic electronic tonguecontact electrificationdeep learningmicrofluidicsself-powered

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Area of Science:

  • Materials Science
  • Sensor Technology
  • Biotechnology

Background:

  • Artificial bionic electronic tongues mimic natural taste perception for taste detection and classification.
  • Liquid-solid contact electrification (LSCE) is effective for self-powered electronic tongues, but droplet-based systems suffer from interference, impacting stability and repeatability.

Purpose of the Study:

  • To develop a monolithically integrated self-powered microfluidic bionic electronic tongue (SMET).
  • To enhance sample identification and concentration detection reliability using LSCE and deep learning.
  • To improve sensitivity and reduce sample volume requirements.

Main Methods:

  • Developed a SMET with a multiplexed microchannel structure.
  • Utilized miniaturized exciters to generate multiple excitation waveforms for improved algorithmic accuracy.
  • Integrated LSCE effect with deep learning algorithms for data analysis.

Main Results:

  • Achieved over 93% classification accuracy for five taste elements and five NaCl concentrations with a single waveform.
  • Reached 100% accuracy by fusing multiple waveform signals.
  • Demonstrated distinct signal variations for over ten different taste samples, indicating high sensitivity.

Conclusions:

  • The SMET offers highly reliable and intelligent sample identification and concentration detection.
  • Its multiplexed microchannel design enhances sensitivity and reduces sample volume.
  • The SMET is a promising tool for rapid and accurate liquid analysis due to its high sensitivity and reliability.