Peptide-Encapsulated Single-Wall Carbon Nanotube-Based Near-Infrared Optical Nose for Bacteria Detection and Classification

Vlad Shumeiko, Yuval Zaken, Guy Hidas, Yossi Paltiel, Gili Bisker, Oded Shoseyov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Sense of smell has been used as a diagnostic tool for almost entire human history. While successful examples of the use of the human nose for diagnostics are rare in modern history, there are ample reports of use of animals to diagnose various medical conditions. Bacterial infections often result in strong odors. In recent years, electronic noses (e-nose) and optical noses (o-nose) are of high interest in diagnostics and classification of bacterial infections. Artificial olfactory sensors can perform noninvasively, immediately at the point of care, do not require extensive sample handling, and promise to be highly cost-effective. This manuscript demonstrates the development of a near-infrared optical sniffer comprised of peptide-encapsulated (6,5) single-wall carbon nanotubes (SWCNTs) for bacteria detection and classification. Sixteen different peptides that include tyrosine in different proportions and positions were synthesized. The ability of these peptides to disperse SWCNTs in water was tested, and the intensity of the resultant optical signal was evaluated. Overall, longer peptides provided better dispersion as compared to shorter peptides. Addition of the fluorenylmethyloxycarbonyl chloride (Fmoc) group to positively charged peptides tested in the current study significantly improved SWCNT dispersion and signal intensity. The sensors successfully distinguished between the odor of sterile growth medium, Escherichia coli, and Klebsiella pneumoniae. Moreover, we demonstrated the possibility of using the developed sensors for antibiotics susceptibility testing. The sensors provided results in real-time, enabled multiple-usage, and operated at room temperature.

Original languageAmerican English
Pages (from-to)6277-6287
Number of pages11
JournalIEEE Sensors Journal
Volume22
Issue number7
DOIs
StatePublished - 1 Apr 2022

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.

Keywords

  • AST
  • Biosensors
  • SWCNTs
  • bacteria classification
  • optical nose

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