Porous Nanoframe Based Plasmonic Structure With High-Density Hotspots for the Quantitative Detection of Gaseous Benzaldehyde
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...
Gespeichert in:
Veröffentlicht in: | Small (Weinheim an der Bergstrasse, Germany) Germany), 2025-01, p.e2408670 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
ISSN: | 1613-6810 1613-6829 1613-6829 |
DOI: | 10.1002/smll.202408670 |