Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc.