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Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy

Author

Listed:
  • Jianxiong Zhu

    (Southeast University)

  • Shanling Ji

    (Southeast University)

  • Zhihao Ren

    (National University of Singapore
    National University of Singapore
    NUS Suzhou Research Institute (NUSRI))

  • Wenyu Wu

    (Southeast University)

  • Zhihao Zhang

    (Southeast University)

  • Zhonghua Ni

    (Southeast University)

  • Lei Liu

    (Southeast University)

  • Zhisheng Zhang

    (Southeast University)

  • Aiguo Song

    (Southeast University)

  • Chengkuo Lee

    (National University of Singapore
    National University of Singapore
    NUS Suzhou Research Institute (NUSRI))

Abstract

Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in “cold” plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare.

Suggested Citation

  • Jianxiong Zhu & Shanling Ji & Zhihao Ren & Wenyu Wu & Zhihao Zhang & Zhonghua Ni & Lei Liu & Zhisheng Zhang & Aiguo Song & Chengkuo Lee, 2023. "Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38200-6
    DOI: 10.1038/s41467-023-38200-6
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    References listed on IDEAS

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