Identification and Detection of Volatile Aldehydes as Lung Cancer Biomarkers by Vapor Generation Combined with Paper-Based Thin-Film Microextraction

化学 拉曼光谱 色谱法 肉眼 表面增强拉曼光谱 荧光 分子 胶体金 纳米颗粒 纳米技术 检出限 拉曼散射 有机化学 材料科学 光学 物理 量子力学
作者
Zhaoping Xia,Dan Li,Wei Deng
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:93 (11): 4924-4931 被引量:80
标识
DOI:10.1021/acs.analchem.0c05348
摘要

Accurate, sensitive, and selective on-spot screening of volatile aldehydes as lung cancer biomarkers is of vital significance for preclinical diagnosis and treatment guidance of cancers. However, the common methods of sensing biomarkers are limited by the fact that they are time-consuming, require professional personnel, and have complex matrixes. Here, we developed a smart vapor generation paper-based thin-film microextraction system capable of both sensitive on-field fluorescence detection and accurate surface-enhanced Raman spectroscopy (SERS) quantification of volatile benzaldehyde (BA) by utilizing stimuli-responsive core–shell gold nanorod (GNR) quantum dot (QD)-embedded metal–organic framework (MOF) structures. The amino-modified GNRs and carboxyl-capped QDs can directly assemble with each other by electrostatic interaction, which leads to an almost complete emission quenching of QDs. The addition of BA molecules destroys the GNRs-QD assemblies due to the Schiff base reactions between the amine group of 4-mercaptonoaniline and the aldehyde moiety of BA, resulting in the increase of the fluorescence and Raman signal of hybrid systems, which enables the visualization of BA with the naked eye. Moreover, the "cavity-diffusion" effect of porous MOF shells validates the selective concentration of gaseous BA molecules on the GNR surface, allowing the discrimination of BA in exhaled breath rapidly and precisely even at the sub-ppb level with excellent specificity against other volatile organic compounds. This study not only offers a versatile sensing platform for accurate discrimination of lung cancer from controls but also opens an avenue for the design of smart sensors for point-of-care applications.
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