A Flexible Single-Sensor MEMS E-Nose with Dual-Temperature Modulation for VOCs Classification and Breath-Based Silent Communication.

Journal: Small (Weinheim an der Bergstrasse, Germany)
Published Date: Jul 9, 2025

Abstract

Electronic noses (e-noses) have become indispensable analytical platforms for gas detection. However, conventional e-nose systems face significant limitations in portable and wearable implementations due to their bulk and high-power consumption. Herein, a single-sensor-based multifunctional e-nose system is reported by integrating a micro-electromechanical system (MEMS) gas sensor with a flexible printed circuit board (FPCB). Specifically, the ZnO-ZnSnO raspberry-like microspheres (ZZSRM) are utilized as the gas-sensitive materials, and the gas selectivity of the sensor is enhanced through a dual-temperature modulation strategy. Additionally, a gas classification model based on the MiniRocket algorithm has been developed, enabling efficient feature extraction and low-complexity classification of response signals, thereby satisfying the real-time processing demands of embedded devices. Moreover, a silent communication method is proposed, which maps breathing frequency to Morse code for information transmission in specific scenarios. Experimental results demonstrate that the wearable system achieves high-precision classification and concentration prediction for eight volatile organic compounds (VOCs), while simultaneously enabling robust recognition of exhaled signals and instantaneous conversion of Morse code into legible alphabetic characters. By combining the gas sensor with artificial intelligence (AI) technology, this work establishes a multifunctional flexible e-nose that merges portable gas detection and silent communication, offering a novel technological framework for environmental monitoring and human-machine interaction.

Authors

  • Mianyi Xiang
    State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
  • Yamin Liu
    School of Computer Science & Technology, Anhui University of Technology, Ma'anshan 243032, PR China.
  • Ziyang Yang
    Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
  • Jinlei Jiang
    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. dxcui@sjtu.edu.cn.
  • Weicheng Wang
    State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
  • Yao Hu
    Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China.
  • Daxiang Cui
    Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai 200240, China. Electronic address: dxcui@sjtu.edu.cn.
  • Qichao Li
    Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, Zhejiang, P. R. China.

Keywords

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