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

Jianxiong Zhu (), Shanling Ji, Zhihao Ren, Wenyu Wu, Zhihao Zhang, Zhonghua Ni, Lei Liu, Zhisheng Zhang, Aiguo Song () and Chengkuo Lee ()
Additional contact information
Jianxiong Zhu: Southeast University
Shanling Ji: Southeast University
Zhihao Ren: National University of Singapore
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

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-38200-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38200-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-38200-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().