Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

Yun Ji Hwang, Heejin Yu, Gilho Lee, Iman Shackery, Jin Seong, Youngmo Jung, Seung Hyun Sung, Jongeun Choi, Seong Chan Jun

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for “breath chemovapor fingerprinting” for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications. [Figure not available: see fulltext.]

Original languageEnglish
Article number28
JournalMicrosystems and Nanoengineering
Volume9
Issue number1
DOIs
Publication statusPublished - 2023 Dec

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Materials Science (miscellaneous)
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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