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Porous Nanoframe Based Plasmonic Structure With High-Density Hotspots for the Quantitative Detection of Gaseous Benzaldehyde

  • Guoqiang Fang
  • , Xiang Lin*
  • , Jinlei Wu
  • , Wen Xu
  • , Wuliji Hasi*
  • , Bin Dong*
  • *Corresponding author for this work
  • Dalian Minzu University
  • Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Owing to its high sensitivity, surface-enhanced Raman scattering (SERS) has immense potential for the identification of lung cancer from the variation in volatile biomarkers in the exhaled gas. However, two prevailing factors limit the application of SERS: 1) the adsorption of target molecules into SERS hotspots and 2) the detection specificity in multiple interference environments. To improve the density of the SERS hotspots, 3D Au@Ag-Au particles are prepared in a porous nanoframes (PPFs) based plasmonic structure, which facilitated a richer local electromagnetic field distribution among the Au nanocubic (NC) cores, Au-Ag porous nanoframes, and Au nanoparticles, thereby promoting the adsorption probability of gaseous aldehydes into the hotspots. L-cysteines (l-Cys)-modified 3D Au@Ag-Au PPFs are proposed as a benzaldehyde (BA) gas detection carrier to accurately detect biomarkers in complex exhaled gases and eliminate interference from other components. Unlike the conventional use of 4-aminothiophenol as a linker molecule, the novel L-Cys-modified SERS substrate is sensitive toward the aldehyde molecules and immune to other volatile organic compounds (ethanol, cyclohexane, toluene, etc.). Furthermore, a medical mask consisting of this SERS substrate is designed to realize intelligent detection of gaseous BA concentrations assisted by a machine learning algorithm.

Original languageEnglish
Article number2408670
JournalSmall
Volume21
Issue number7
DOIs
StatePublished - 19 Feb 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • L-cysteine
  • benzaldehyde
  • machine learning
  • plasmonic structure
  • surface-enhanced raman scattering

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