Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 401331, China.
Chongqing Industry Polytechnic College, Chongqing 401120, China.
ACS Sens. 2024 Feb 23;9(2):979-987. doi: 10.1021/acssensors.3c02519. Epub 2024 Feb 1.
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.
通过在金属表面上用表面等离子体激元诱导的强受限电磁场捕获靶分子，表面增强拉曼光谱（SERS）成为一种具有高灵敏度的光谱分析技术。然而，由于难以捕获，对低密度和高速气态分子进行准确的 SERS 识别仍然是一个挑战。在这项工作中，制备了一种由 Ag 纳米线@ZIF-8（AgNWs@ZIF-8）组成的柔性纸质等离子体金属-有机骨架（MOF）薄膜，用于气态分子的 SERS 检测。得益于纳米线网络和 ZIF-8 壳产生的微孔，实现了对气态分子的有效捕获，并在这种基于纸张的柔性等离子体 MOF 纳米线薄膜中获得了其 SERS 光谱。在最佳结构参数下，气态 4-巯基苯胺、4-巯基苯酚和二硫代对苯二酚的光谱表明，该薄膜具有良好的 SERS 性能，在可重复检测过程中，可在 30 天内保持明显的拉曼信号。为了实现对气态分子的 SERS 识别，在这种柔性多孔薄膜中获得的混合气态分析物的 SERS 光谱的基础上进行了深度学习。结果表明，人工神经网络算法可以以 93.7%的高精度识别模拟呼出气体中的气态醛类（结直肠癌的气态生物标志物）。将柔性纸质薄膜传感器与深度学习相结合，为非侵入性结直肠癌筛查提供了一种很有前途的新方法。我们的工作探索了 SERS 在气态分析物检测中的应用，在临床医学、食品安全、环境监测等领域具有广泛的应用潜力。
